DISS. ETH NO. 22975
Sensorized Cardiac Radiofrequency
Ablation System for Lesion Depth
Assessment
A thesis submitted to attain the degree of
DOCTOR OF SCIENCES of ETH ZURICH
(Dr. sc. ETH Zurich)
presented by
Péter Sándor Baki
M.Sc. in Electrical Engineering
Budapest University of Technology and Economics
born on 04.05.1984
citizen of
Hungary
accepted on the recommendation of
Prof. Dr. Gábor Székely, examiner
Dr. Gábor Kósa, co-examiner
Prof. Dr. Orçun Göksel, co-examiner
Prof. Dr. Alon Wolf, co-examiner
2015
TO MY UNCLE.
Abstract
In the past few decades radiofrequency catheter ablation has become the primary treatment of choice for a number of cardiac arrhythmia types, in case
the patient does not respond to medication. This endocardial approach offers minimally invasive percutaneous entry into the heart, which is associated
with reduced length of hospitalization, low expenses and reasonably low morbidity and mortality. Since lesion recovery has been a major cause of failure
and recurrence of arrhythmia, creating sufficiently deep lesions and achieving
transmurality are key issues with regard to procedural efficacy. It is technically
challenging to determine precisely and repeatedly the lesion dimensions in a
tissue being ablated, especially in real-time intra-operative scenarios.
Current measures of radiofrequency ablation lesion depth include impedance
measurement and electrode-tissue interface temperature monitoring, which are
influenced by a number of factors difficult to control, such as cavitary blood
flow, coagulum on the tip and electrode orientation. Technological advances
in catheter design and sensor technologies have led to extensive research on
techniques that are appropriate for accurate and reliable real-time lesion size
assessment intraoperatively. Some novel catheter designs aim to estimate the
depth of tissue coagulation using contact force and ultrasound based techniques
including conventional sonography and advanced image processing methodologies as well.
This thesis aims to investigate the feasibility of in vitro lesion size assessment with a custom-developed radiofrequency catheter tip featuring integrated
multi-axial force sensing and M-mode ultrasound imaging capabilities. To this
end, two miniature multi-axial force sensors have been developed. The EulerBernoulli sensor is easy to manufacture and it has smaller diameter, whereas
the Butterfly concept offers superior isotropy. The latter one has been integrated into a catheter tip together with a commercially available ultrasound
transducer. In order to make synchronization possible between radiofrequency
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A BSTRACT
power delivery, force sensing and ultrasound imaging, we developed a complex RF ablation and data acquisition system. Since traditional sonography has
not proven to be sufficient to identify tissue coagulation, we have proposed a
method named Thermal Expansion Imaging (TEI) to determine the instantaneous strain between consecutive ultrasound scans.
The developed system was used to perform RF ablation experiments in vitro
with porcine heart samples while simultaneously acquiring electrical parameters, ultrasound and contact force data. The results have shown that the features
of the developed system can offer significant improvements. Orthogonality
could be determined between the catheter tip and the tissue surface based on
force feedback, whereas lesion depths calculated by TEI correlated well with
the visually identified ones.
Zusammenfassung
In den letzten Jahrzehnten hat sich die Radiofrequenz-Katheterablation als Behandlung der Wahl für Herzrhythmusstörungen etabliert, sofern sie sich medikamentös nicht therapieren lassen. Im Gegensatz zu klassischen, chirurgischen
Behandlungsmethoden, erfordert diese perkutane Methode nur einen minimalen Einschnitt für einen venösen Zugang anstatt eines großen operativen Eingriffs. Minimalinvasive Methoden sind nicht nur für den Patienten mit weniger
Schmerzen und kürzerem Krankenhausaufenthalt verbunden, sondern verursachen auch weniger Kosten und weisen in Studien niedrigere Morbiditäts- und
Mortalitätsrisiken auf. Bei der Radiofrequenz-Katheterablation wird mittels einer entlang der Blutgefäße ins Herz eingeführten Sonde eine thermische Läsion
am Herzmuskel des rechten Vorhofs erzeugt. Die Herausforderung liegt darin,
eine gleichmäßige, transmurale Narbe zu erzeugen, da der erzielte Effekt des
Unterbruchs der Leitfähigkeit des Gewebes sonst nicht oder aufgrund von Heilungsprozessen nur vorübergehend eintritt. Nach dem gegenwärtigen Stand der
Technik stehen keine chirurgischen Instrumente zur Verfügung, die eine Kontrolle der Läsionstiefe in Echtzeit zufriedenstellend ermöglichen.
Heutige Maßnahmen zur Bestimmung der Läsionstiefe umfassen Impedanzmessung und die Elektrode-Gewebe-Temperaturüberwachung, welche durch
schwierig kontrollierbare Faktoren wie kavitäre Blutung, Koagulat auf der Spitze des Katheters oder die Elektrodenausrichtung beeinflusst werden. Neue Katheterkonstruktionen vermessen die Tiefe der Gewebekoagulation mit Hilfe
von Sensoren zur Bestimmung der Kontaktkraft sowie Ultraschallgestützten
Techniken und fortschrittlichen Bildverarbeitungsmethoden.
Diese Arbeit zielt darauf ab, die Machbarkeit der in vitro Beurteilung der
Läsionsgröße mit einer speziell entwickelten Hochfrequenz-Katheterspitze mit
integrierter multi-axialer Kraftmessung und M-Mode Ultraschall-Imaging Funktionen zu untersuchen. Zu diesem Zweck sind zwei wenige Millimeter große
mehrachsige Kraftsensoren entwickelt worden. Der Euler-Bernoulli Sensor ist
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Z USAMMENFASSUNG
leicht herzustellen und hat einen kleineren Durchmesser, wohingegen das Butterfly Konzept eine hervorragende Isotropie bietet. Letztere ist in eine Katheterspitze mit einem im Handel erhältlichen Ultraschallwandler integriert worden.
Um die Synchronisation zwischen Leistungsentfaltung, Kraftmessung und Ultraschall-Bildgebung sicherzustellen, haben wir ein komplexes RF-Ablation und
Datenerfassungssystem entwickelt. Herkömmliche sonographische Bildgebung
hat sich als unzureichend für die Beurteilung von Gewebekoagulation herausgestellt. Im Rahmen dieser Arbeit wurde daher eine Methode mit dem Namen Thermal Expansion Imaging (TEI) entwickelt, welche die thermischen
Veränderungen des Gewebes über Bildfolgen hinweg bestimmt.
Das entwickelte System wurde verwendet, um die RF-Ablation Experimente
mit Schweineherzproben bei gleichzeitiger Erfassung der elektrischen Parameter, der Ultraschall- und Kontaktkraftdaten in vitro durchzuführen. Die Ergebnisse haben gezeigt, dass die Funktionen des entwickelten Systems signifikante Verbesserungen bieten können. Orthogonalität zwischen der Katheterspitze und der Gewebeoberfläche basierend auf Kraftmessungen konnte bestimmt
werden, und die von TEI berechneten Läsionstiefen korrelierten gut mit den
visuell identifizierten Läsionstiefen.
Acknowledgements
The research presented in this thesis has been supported by the Co-Me NCCR
network of the Swiss National Science Foundation.
First and foremost, I would like to thank my advisor Prof. Dr. Gábor Székely
for providing me an opportunity to work in his research group. I am very grateful for his bright comments and insightful suggestions throughout my research.
I would like to express my gratitude to my supervisors Dr. Gábor Kósa and
Prof. Dr. Orçun Göksel who guided my research and supported me in writing
this thesis and my other publications. I sincerely want to thank Dr. Sergio J.
Sanabria for his excellent work and contribution to my thesis.
My thanks go also to my co-referee Prof. Dr. Alon Wolf for reviewing this
dissertation.
I warmly acknowledge the cooperation with the D-ITET and D-PHYS mechanical workshops. The mechanical developments presented in this thesis would
not have been possible without their excellent work. In particular, I want to
thank Martin Vogt for his great help and valuable input.
Many thanks to the secretaries Christina Krüger, Barbara Widmer, Vreni Vogt
and Fiona Matthews for taking care of administrative matters.
I am also very thankful to all BIWI members for the inspiring working environment. Special thanks go to my friends Michael Gessat, Ranveer Joyseeree,
Matthias Dantone, Veronika Grandl, Thomas Wolf and Michael Emmersberger
who enriched my life and kept my spirits up.
I would like thank my sisters and parents for their support and unconditional
love. Last but not least, I would like to express my gratitude to my late uncle,
László Baki, who always believed deeply that I can achieve my goals.
Contents
List of Figures
xiii
List of Tables
xxi
1 Introduction
1.1 Overview . . .
1.2 Motivation . . .
1.3 Contributions .
1.4 Thesis Outline
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2 Medical Background
2.1 Cardiac Arrhythmia . . . . . . . . . . . . . . . . . . . . . .
2.1.1 Basic Anatomy of the Human Heart . . . . . . . .
2.1.2 Mechanism . . . . . . . . . . . . . . . . . . . . . .
2.1.2.1 Abnormal Automaticity . . . . . . . . . .
2.1.2.2 Triggered Activity . . . . . . . . . . . . .
2.1.2.3 Re-entry . . . . . . . . . . . . . . . . . .
2.1.3 Atrial Fibrillation . . . . . . . . . . . . . . . . . . .
2.2 Treatment Options . . . . . . . . . . . . . . . . . . . . . .
2.2.1 Antiarrhythmic Drug Therapy . . . . . . . . . . . .
2.2.2 Interventional Methods . . . . . . . . . . . . . . .
2.2.2.1 Lesion Sets . . . . . . . . . . . . . . . . .
2.3 Transcatheter Ablation . . . . . . . . . . . . . . . . . . . .
2.3.1 Direct Current Catheter Ablation . . . . . . . . . .
2.3.2 Radiofrequency Catheter Ablation . . . . . . . . .
2.3.2.1 Mechanism of Lesion Formation . . . . .
2.3.2.2 Thermodynamics of Radiofrequency Ablation . . . . . . . . . . . . . . . . . . . .
2.3.2.3 Complications . . . . . . . . . . . . . . .
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C ONTENTS
2.3.2.4 Temperature Measurement . .
2.3.2.5 Control Methods . . . . . . . .
2.3.2.6 Importance of Tissue Contact
2.3.3 Techniques for Increasing Lesion Size .
2.3.3.1 Elongated Electrode . . . . . .
2.3.3.2 Electrode Plating . . . . . . . .
2.3.3.3 Irrigated Ablation . . . . . . . .
2.3.3.4 Balloon-based Method . . . .
2.3.3.5 Multielectrode Ablation . . . .
2.3.4 Alternative Methods . . . . . . . . . . .
2.3.4.1 Cryoablation . . . . . . . . . .
2.3.4.2 Laser Photoablation . . . . . .
2.3.4.3 Microwave Ablation . . . . . .
2.3.4.4 Ultrasound Ablation . . . . . .
2.3.5 Tool Navigation . . . . . . . . . . . . . .
3 Force Sensors
3.1 Introduction . . . . . . . . . . . . . .
3.2 Motivation . . . . . . . . . . . . . . .
3.3 Sensor Concept . . . . . . . . . . .
3.3.0.1 Mechanical Design
3.3.0.2 Electrical Design . .
3.4 Euler-Bernoulli Sensor . . . . . . . .
3.4.1 Sensing Principle . . . . . . .
3.4.2 Sensor Design . . . . . . . .
3.4.3 Calibration . . . . . . . . . .
3.4.3.1 Calibration Setup .
3.4.3.2 Calibration Results
3.5 Butterfly Sensor . . . . . . . . . . .
3.5.1 Sensing Principle . . . . . . .
3.5.2 Sensor Design . . . . . . . .
3.5.3 Calibration . . . . . . . . . .
3.5.3.1 Calibration Setup .
3.5.3.2 Calibration Results
3.5.4 Final Version . . . . . . . . .
3.6 Conclusion . . . . . . . . . . . . . .
4 System Development
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xi
C ONTENTS
4.1 Introduction . . . . . . . . . . . . . . . .
4.2 Ultrasound Fundamentals . . . . . . . .
4.3 System Components . . . . . . . . . . .
4.3.1 Ablation Head . . . . . . . . . .
4.3.1.1 Ablation Electrode . . .
4.3.1.2 Ultrasound Probe . . .
4.3.1.3 Ceramic Element . . .
4.3.1.4 Sensor Head Assembly
4.3.2 Ultrasound Devices . . . . . . .
4.3.3 Radiofrequency Ablation . . . . .
4.3.3.1 Electrical Design . . . .
4.3.3.2 Calibration . . . . . . .
4.3.3.3 Temperature Sensing .
4.3.3.4 Force Sensing . . . . .
4.3.4 Platform . . . . . . . . . . . . . .
4.4 Full System . . . . . . . . . . . . . . . .
4.5 Conclusion . . . . . . . . . . . . . . . .
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5 Lesion Size Assessment
5.1 Related Research . . . . . . . . . . . . . . . . . . . . . .
5.1.1 Contact Force Based Methods . . . . . . . . . . .
5.1.2 Temperature Based Methods . . . . . . . . . . . .
5.1.3 Impedance Change Based Methods . . . . . . . .
5.1.4 Ultrasound Based Methods . . . . . . . . . . . . .
5.1.4.1 Conventional Sonography . . . . . . . . .
5.1.4.2 Ultrasonic Motion Tracking . . . . . . . .
5.1.4.3 Backscatter . . . . . . . . . . . . . . . . .
5.1.4.4 Spectral Properties . . . . . . . . . . . .
5.1.4.5 Patterns from Time Series . . . . . . . .
5.1.4.6 Time of Flight . . . . . . . . . . . . . . .
5.1.4.7 Thermal Strain Imaging . . . . . . . . . .
5.1.4.8 Echo Decorrelation . . . . . . . . . . . .
5.1.4.9 Elastography . . . . . . . . . . . . . . . .
5.2 Experiments . . . . . . . . . . . . . . . . . . . . . . . . .
5.2.1 Contact Conditions on Cardiac Tissue Surface . .
5.2.2 Speed of Sound Variations During Homogeneous
Tissue Heating . . . . . . . . . . . . . . . . . . . .
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xii
C ONTENTS
5.2.3 Ultrasound Data Acquisition During Radiofrequency
Ablation . . . . . . . . . . . . . . . . . . . . . . . . 119
5.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
5.3.1 M-mode Imaging . . . . . . . . . . . . . . . . . . . 120
5.3.2 Thermal Expansion Imaging . . . . . . . . . . . . 122
5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
5.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 132
6 Conclusion and Future Work
6.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . .
6.3 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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List of Figures
2.1 Cross-sectional (a) and posterior (b) view of the human
heart. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Commonly used lesion sets for cardiac ablation: Cox III
maze (a), mini maze (b) and radial procedure (c). . . . . .
2.3 Illustration of a catheter inserted into the heart. . . . . . .
2.4 Illustration of an RF ablation setup. A RF catheter is inserted into the heart under imaging guidance. RF energy provided by a generator is applied to the catheter
electrode. A reference patch electrode placed on the patient’s back serves as return path for the RF current. . . .
2.5 Illustration of unipolar (a) and bipolar (b) power delivery
during RF ablation. . . . . . . . . . . . . . . . . . . . . . .
2.6 Heat distribution during RFA procedure. Due to convective heat exchange the hottest point is located in the myocardium. . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.7 Novel methods ensuring tight catheter electrode – tissue
contact: magnetic coupling (a) and vacuum suction (b). .
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3.1 Commonly used multi-axial MEMS force sensor structures with horizontal cantilevers (a) and membrane (b). . . 34
3.2 Physical model of the measurements: the applied force
is defined by its shear angle φ, angle of incidence θ and
force magnitude. . . . . . . . . . . . . . . . . . . . . . . . 43
3.3 Full- 3.3(a) and half-Wheatstone 3.3(b) bridges with compressed (C) and tensed (T ) strain gauges and the fixed
value completion resistors (RC ). Temperature compensation is carried out by the shunt resistor (RT ), which
typically has a high value. . . . . . . . . . . . . . . . . . . 44
xiv
L IST OF F IGURES
3.4 Temperature response of the outputs of a half-bridge while
heating the sensor body from 25 ◦ C to 45 ◦ C. . . . . . . . 45
3.5 Block diagram of the system. The half bridges are extended on separate PCBs, the bridge outputs are connected to precision instrumentation amplifiers. The conditioned signals are converted and processed by the microcontroller. The processed data are sent to the PC via
an RS-232 serial port. . . . . . . . . . . . . . . . . . . . . 46
3.6 Mechanical drawing of the force sensor (a). Image of
the monolithic Ti sensor beam (b) and the gauged force
sensor (c). . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.7 Strain gauges on the Euler-Bernoulli beam. The normal force component is measured by a full-Wheatstone
bridge (N ), whereas the shear half bridges (Sx and Sy )
are completed externally. . . . . . . . . . . . . . . . . . . 49
3.8 Calibrating mechanism presenting 4 degrees of freedom:
two rotational and two translational. The red arrow corresponds to the angle of incidence (θ), the yellow to the
shear angle (φ) and the blue to the sliding movement.
There is a fourth translational DoF (green) which ensures
that the interaction point is on the Nano17’s symmetry axis. 50
3.9 Typical force trajectories of the amplified bridge signals.
Constant force direction results in linear curves. . . . . . . 52
3.10 Bridge sensitivities as a function of the angle of incidence.
The measurements presented are: X shear force (blue
circle); Y shear force (red square) and Z axial force (black
triangle). . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.11 Recorded points in the shear plane. The measurements
were made in 8 shear directions with the angle of incidence ranging from 0◦ to 30◦ with 5◦ steps (black circles).
One can see that the closer the points are to the normal
direction the more significant the shear angle error is. . . 53
3.12 Illustration of the cross dependency between the output
signals of the bridges. The sensitivity of the normal bridge
varies in different shear directions. . . . . . . . . . . . . . 53
L IST OF F IGURES
xv
3.13 Recorded points and the estimated surface as a function
of the calculated shear angle φ and angle of incidence θ.
The force scaling factor represents the ratio between the
calculated magnitude R and the actual force in a given
direction. . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.14 Illustration of the recorded points in the actual force space
relative to the calibrating load (2.62N ). The red crosses
are the target values and the black circles are the measured ones, respectively. . . . . . . . . . . . . . . . . . . . 56
3.15 Recorded points in 4 shear directions relative to the calibrating load. As each measurement was carried out
by applying constant load, the measured points are expected to be on the arc. The radial lines represent the
angle of incidence in 5 steps. . . . . . . . . . . . . . . . . 56
3.16 Force sensor designs with circular (a) and ’C’ shaped (b)
basic sensing elements. The plain part of the latter structure makes it possible to assemble strain gauges on the
sensor. All dimensions are given in mm. . . . . . . . . . . 57
3.17 Illustration of the force components applied on the Butterfly sensor. . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.18 Illustration of the FEA results in case of normal (a) and
shear (b) force component applied on the Butterfy sensor.
58
3.19 Illustration of the manufacturing of the sensor body: forming a cylindrical beam (a), structuring the cross section
(b), shaping the wings in the shear directions (c) and (d),
removing excess material from the middle (e) and eventually shaping the tip (f). . . . . . . . . . . . . . . . . . . . 61
3.20 Strain gauges on the sensor. The vertically and horizontally placed gauges form a half Wheatstone bridge. The
symmetric setup ensures that the bridge has zero output
in case of symmetric strain profile. . . . . . . . . . . . . . 62
3.21 FEA results for the basic sensing element. Compressive
(a) and tensile (b) load at the gap causes uniform tensile
and compressive stress, respectively. Shear stress (c)
results in symmetric strain profile that is not to be measured by the half Wheatstone bridge. . . . . . . . . . . . . 63
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L IST OF F IGURES
3.22 Calibrating mechanism presenting 3 degrees of freedom:
two rotational and one translational. θ corresponds to the
angle of incidence, φ to the shear angle and R to the
sliding movement. . . . . . . . . . . . . . . . . . . . . . . 65
3.23 Typical bridge output versus reference force trajectory.
The correlation coefficient of the curve is 0.9997, the integral nonlinearity is 2.42% full-scale. . . . . . . . . . . . . . 67
3.24 Bridge outputs as functions of the reference force in the
θ = 45◦ , φ = 20◦ direction. The black dots represent
the average bridge outputs of 10 measurements ranging from 0 to 2.5N in 0.1N steps, whereas the grey lines
stand for the sensitivities extracted by linear regression. . 68
3.25 Output sensitivity [mV /N ] versus load orientation for the
four bridges. Due to the sensor’s symmetry, apart from
the rotation the estimated bridge sensitivities are similar. . 69
3.26 Output sensitivity [mV /N ] versus load orientation function of the Bridge 1 (marked black). The grey circles
in the XY plane represent the 30◦ , 60◦ and 90◦ angle
of incidence. For a better visualization the sensitivities
gained from the calibration data are interconnected along
the surface of the third order polynomial estimation. . . . 70
3.27 Experimental setup with the calibrated force sensor mounted
on the Nano17. Recordings have been made while a
plane metal part was pressed against the sensor in different directions. . . . . . . . . . . . . . . . . . . . . . . . 70
3.28 3D Force recording in time domain. The red curve represents the sensor data, whereas the blue one is the reference force. . . . . . . . . . . . . . . . . . . . . . . . . . . 71
3.29 Technical drawing (a) and photo (b) of the latest generation Butterfy sensor. . . . . . . . . . . . . . . . . . . . . . 72
4.1 Returning echoes from tissue regions with different acoustic properties. The distance can be calculated from the
elapsed time and the speed of sound. . . . . . . . . . . . 78
4.2 Components of the catheter head: stainless steel RF ablation electrode (a), US transducer (b), ceramic structural
element (c) and the Butterfly force sensor (d). . . . . . . . 79
4.3 Schematic of the single-crystal US transducer. . . . . . . 80
L IST OF F IGURES
4.4 Technical drawing (a) and CAD model (b) of the assembled catheter head. The dimensions are given in mm. . .
4.5 Exploded and assembled CAD model of the catheter head
and the mechanical support element. . . . . . . . . . . .
4.6 Scheme of the ultrasound setup. The US probe is excited
by the US Box, then the echo is amplified by the Olympus
receiver and finally the data sampling takes place in the
US Box device. . . . . . . . . . . . . . . . . . . . . . . . .
4.7 Schematic of the ablation system. . . . . . . . . . . . . .
4.8 Transformer-coupled voltage switching Class-D amplifier
with a tuned fifth order lowpass LC filter and the corresponding waveforms. . . . . . . . . . . . . . . . . . . . . .
4.9 Electrical system for voltage and current measurement. .
4.10 Schematic of the voltage, current and phase measurement.
4.11 Test load for the calibration. The chain of resistors are
parallel connected with a capacitor. . . . . . . . . . . . . .
4.12 Theoretical values of the impedance magnitude (a) and
phase (b) for different parallel RC test loads. . . . . . . . .
4.13 Measured current (a), voltage (b), phase (c) and the fraction uRF /i (d) for different parallel RC test loads. . . . . .
4.14 Calculated electrical power for different parallel RC test
loads. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.15 Picture of the mechanical setup showing the frame, the
translational actuator and the catheter head. . . . . . . .
4.16 CAD model of the translational actuator. . . . . . . . . . .
4.17 The complete system used throughout this thesis. . . . .
4.18 Scheme of the sampling and communication timing during ablation. . . . . . . . . . . . . . . . . . . . . . . . . . .
4.19 Illustration of the power control loop implemented by a
PID controller. . . . . . . . . . . . . . . . . . . . . . . . . .
xvii
82
83
84
85
87
89
90
90
91
92
93
94
95
97
98
98
5.1 Typical electric power (a), impedance magnitude (b) and
impedance phase (c) curves during RFA in power control
mode. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
5.2 Scheme of the experimental setups ((a) and (b)), estimated angle of incidence ((c) and (d)) and force data displayed in the XY plane ((e) and (f)) for linear translation
and orthogonal palpation, respectively. . . . . . . . . . . . 115
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L IST OF F IGURES
5.3 Illustration of the measurement setup for determining the
temperature dependence of sound speed in cardiac tissue samples. . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.4 Illustration of the echo shift due to the changing speed
of sound. By heating the sample, the increasing sound
speed results in shorter time of flight. . . . . . . . . . . . . 117
5.5 Illustration of the echo shift due to the changing speed of
sound while heating cardiac tissue with a constant distance of 16.45mm between the transducer and the heat
sink. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
5.6 Speed of sound as a function of temperature in porcine
cardiac tissue. . . . . . . . . . . . . . . . . . . . . . . . . 118
5.7 Photo of the experimental setup for Thermal Expansion
Imaging. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
5.8 Photo illustrating the cross section of the ablated myocardium. The lesion boundary is represented by the
dotted line. . . . . . . . . . . . . . . . . . . . . . . . . . . 120
5.9 Processing steps for ultrasound visualisation: enveloping
(a), time gain compensation (b) and logarithmic compression (c) of the echo intensity scan. . . . . . . . . . . . . . 121
5.10 Illustration of US time series (a), M-scan (c), instantaneous displacement (b) and strain (d) profiles during RF
ablation. The black line in (d) identifies the TEB. . . . . . 123
5.11 Instantaneous strain curve corresponding to t = 50s in
Fig. 5.10(d). The first negative zero-crossing of the thermal expansion boundary (TEB) is hypothesized to be the
ablation boundary. . . . . . . . . . . . . . . . . . . . . . . 125
5.12 Tissue optical images acquired after 13 different ablation
experiments (ablation time ∼ 60s, no contact force CF
control). The visually identified lesion depths are marked
with white arrows. . . . . . . . . . . . . . . . . . . . . . . 127
5.13 M-scan images calculated for 13 different ablation experiments (ablation time ∼ 60s, no contact force CF control).
The dotted lines indicate the start and end of ablation,
whereas the white circle represents the observed depth
at the time of cessation. . . . . . . . . . . . . . . . . . . . 128
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xix
5.14 TEI images for the experiments of Fig. 5.13. The identified thermal expansion boundary is plotted on the images, together with the visually identified and US estimated ablation depths. . . . . . . . . . . . . . . . . . . . . 129
5.15 Linear regression between measured variables. US estimated DepthU S versus visually identified Depthvisual ablation depth (with undefined contact force CF data of Fig. 5.12
and with CF = 0.04N and CF = 0.4N ) (a), lesion depth
for given ablation duration with CF = 0.4N (b), electrical
impedance magnitude decrement -∆Z/Z (c) and phase
decrement -∆φ/φ (d). . . . . . . . . . . . . . . . . . . . . 130
5.16 Illustration of the instantaneous strain changes during RF
ablation. After the start of ablation a high strain domain
(A) appears close to the catheter-tissue interface. Over
55◦ C the same domain shows negative strain (B), probably because of SOS variations. Proceeding further with
the ablation, structural changes take place due to tissue
coagulation, resulting in a positive strain region (C). For
(T > 20s) a positive strain band (D) can be observed, indicating thermal expansion of the coagulating tissue. This
thermally expanding band compresses the adjacent region, resulting in a negative strain domain (E) deeper in
the tissue. . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
5.17 Effective SOS for the RFA experiment of Fig. 5.7, based
on echo reflection at the patch electrode plate. . . . . . . 134
List of Tables
3.1 Comparison of multi-component miniaturized force sensors 36
3.2 Sensor specification. . . . . . . . . . . . . . . . . . . . . . 41
1
Introduction
1.1
Overview
Cardiac arrhythmia is any of the conditions in which the electrical activity of
the heart is irregular or on average faster (tachycardia) or slower (bradycardia)
than the normal heart rate. Under normal circumstances, electrical impulses
are initiated by the sinoatrial (SA) node, propagate through the heart and eventually make the myocardium of the ventricles contract. However, the flow of
erratic signals may result in abnormal behavior of the heart. Fibrillation occurs
when an entire chamber of the heart is affected by multiple re-entry circuits.
Ventricular fibrillation may lead to blood circulation stop and is life threatening. In contrast, atrial fibrillation (AF) is not necessarily life-threatening,
nonetheless the resulting abnormal activity of the atria might lead to an erratic
blood flow and potentially thrombus formation and cardiac stroke. AF is the
most common atrial arrhythmia and among the fastest growing diseases, affecting 15% of patients older than 70 years, with a five times higher chance of
stroke than normal sinus rhythm and twofold higher risk of mortality (Kannel
et al. [1982]). Besides, AF often diminishes the patient’s sense of well-being
and functional capacity.
Antiarrhythmic drug therapy (ADT) is the first choice for treating AF. It relies on maintaining the ventricular rate low to avoid adverse arrhythmic events,
however, it has demonstrated limited efficacy for symptomatic patients and
shows potentially toxic effects (Zimetbaum [2012]). In cases where ADT cannot be applied, transcatheter ablation is the most common minimally invasive
interventional treatment, during which a catheter is inserted into a vein and
its tip is wire steered and positioned within the heart chamber under imaging
guidance. The aim of transcatheter ablation is to prevent the flow of erratic
2
1. I NTRODUCTION
electric signals by irreversibly injuring the arrhythmic myocardium without
significantly modifying its mechanical properties. Besides being minimally invasive, catheter ablation can be performed in conscious patients, which allows
doctors to react upon potential complications by termination of the ablation
procedure.
Among medically approved transcatheter ablation methods, radiofrequency ablation (RFA) is by far the most commonly used one (McRury & Haines [1996]).
Unipolar RFA is based on the circulation of sine wave radiofrequency (RF)
current between the catheter tip and a large patch electrode applied to the patient’s skin resulting in a very focal heating around the tip and subsequent tissue necrosis due to protein coagulation. To perform RFA efficiently, sufficient
power is required to obtain tissue temperatures above 50◦ C in the targeted tissue. On the other hand, in order to avoid complications associated with excessive tissue temperatures such as tissue charring, water steaming, increased risk
of thromboembolism and cardiac perforations, the hottest site must be maintained below 100◦ C. This requires balancing the competing demands of efficacy against those of safety.
1.2
Motivation
The success of catheter ablation is related to the identification of the arrhythmia’s site of origin and the formation of ablated lesions. Diagnosis of arrhythmic sites can be obtained by cardiac mapping of electrical activity, whereas
non-invasive imaging allows for catheter guidance. Another important – however less explored – issue is the determination of the dimensions of the lesion
induced by the ablation procedure. Since lesion recovery has been a major
cause of failure and recurrence of arrhythmia, creating sufficiently deep lesions
and achieving transmurality are key issues with regard to procedural efficacy.
There is a need for real-time lesion size assessment intraoperatively to avoid
repeated procedures and improve safety.
It is technically challenging to determine precisely and repeatably the lesion
dimensions in a tissue being ablated, especially in real-time intra-operative
scenarios. Lesion formation during RFA is influenced by a number of factors
difficult to control, such as cavitary blood flow, coagulum on the tip and electrode orientation. Current measures of RFA lesion depth include impedance
measurement and electrode-tissue interface temperature monitoring, which are
1.3. C ONTRIBUTIONS
3
influenced by the aforementioned factors, therefore they are not capable of providing precise feedback.
Monitoring contact forces, however, can be an alternative since contact quality
and duration of treatment have proven to correlate well with lesion dimensions
(Okamura et al. [2004]). Some novel catheter designs with integrated ultrasound (US) sensor aim to estimate the depth of tissue coagulation (Wright et al.
[2010]). Sonography alone has not proven to be sufficient for real-time visualization of necrotic tissue as the echogenicity does not significantly change
during thermal therapy. On the other hand, signal processing of the US echo
has been widely investigated and shows potential for lesion depth assessment.
The main objective of this thesis is to develop an RF catheter tip with a built-in
multi-axial force sensor and an integrated single element US transducer, as well
as a complex RF ablation and data acquisition system to study the feasibility
of real-time lesion size assessment in vitro. In this context we demonstrate the
development and evaluation of two miniature multi-axial force sensors, out of
which one has been integrated into an RF catheter tip together with a commercially available US transducer. In order to make synchronization possible between RF power delivery, force sensing and US imaging, we present a system
featuring RFA, data acquisition and mechanical support for the experiments.
In the scope of this thesis, we want to investigate if the developed features of
the system can jointly offer significant improvements.
1.3
Contributions
This thesis contributes to the enhancement of RF ablation lesion depth assessment in the following ways:
• The first contribution is the development and evaluation of two robust,
miniature tri-axial force sensors that meet the essential requirements of
minimally invasive surgical instruments and can be used in a catheter.
Despite their small dimensions, the sensors’ measurement performance
is comparable to large size, commercially available multi-axial force
sensing devices.
• The second contribution consists of a complex RFA and data acquisition system including an RF catheter tip with integrated multi-axial force
4
1. I NTRODUCTION
sensing and US imaging capabilities, which can be used for ultrasound
guided lesion size assessment.
• The third contribution is a diagnostic US imaging technique allowing for
lesion depth assessment by evaluating the sequence of US recordings.
The method is based on the computation of instantaneous strain between
consecutive US scans and it can be automated.
1.4
Thesis Outline
The thesis is organized as follows:
Chapter 2 provides the reader with necessary insight into the medical background for the understanding of this thesis. First, the essential aspects of cardiac arrhythmia with special emphasis on atrial fibrillation and the underlying anatomy are presented. Then the treatment options are detailed. Finally,
the various transcatheter ablation procedures are described, covering widely
adopted as well as novel methods.
Chapter 3 first summarizes the common force/torque sensor trends and architectures for minimally invasive surgical applications. Then the mechanical and
electrical aspects of the sensor design are addressed. This is followed by the
description of the specific sensing principle, mechanical design, gauging concept, calibration strategies, tools and comparative experiments for the developed sensors.
In Chapter 4 first the requirements for ultrasound guided lesion size assessment
during radiofrequency ablation with multi-axial force feedback are addressed.
After a brief introduction to ultrasound fundamentals, the components of the
system are described in more detail with the specific problems resulting from
the applied technologies. Finally, the complete system and the synchronization
are explained.
Chapter 5 first gives a brief review of lesion size assessment methods, with
special emphasis on those that are based on technologies relevant to the system described in Chapter 4: monitoring multi-axial contact force, electrical
impedance measurement and US imaging. Then the experiments with porcine
cardiac tissue are described, followed by the description of the applied methods. Finally, the results are presented and discussed.
1.4. T HESIS O UTLINE
5
Chapter 6 completes the thesis with a summary of the achievements and the
possibilities of lesion size assessment with the proposed system and method.
This is followed by some conclusions and possible directions for future work.
2
Medical Background
This chapter offers insight into the medical background for the understanding
of this thesis. First, the essential aspects of cardiac arrhythmia with special emphasis on atrial fibrillation and the underlying anatomy are presented. Then in
Section 2.2 the treatment options are detailed. Finally, in Section 2.3 the various transcatheter ablation procedures are depicted, covering widely adopted as
well as novel methods.
2.1
Cardiac Arrhythmia
Cardiac arrhythmia is any of the conditions in which the electrical activity of
the heart is irregular or is faster or slower than the normal heart rate. The typical resting heart rate of adults ranges from 60 to 80 beats per minute, whereas
in children it is significantly faster. Even though most arrhythmias are not lifethreatening, they often diminish the patients sense of well-being and functional
capacity. A heart beat that is too slow is labelled bradycardia. Slower rhythm
than 60 beats per minute might also be present in athletes and be considered
as normal. A heart beat that is too fast is called tachycardia. Nevertheless,
tachycardia is not necessarily an arrhythmia. Higher heart rate is a natural response to physical or emotional stress. Normally, it is mediated by the nervous
system and called sinus tachycardia. Non-sinus tachycardia usually originates
from additional abnormal impulses to the normal cardiac cycle, that may cause
palpitation.
8
2. M EDICAL BACKGROUND
(a)
(b)
Figure 2.1: Cross-sectional (a) and posterior (b) view of the human heart.
2.1. C ARDIAC A RRHYTHMIA
2.1.1
9
Basic Anatomy of the Human Heart
The important anatomical features of the human heart are shown in Fig. 2.1(a).
The right side of the heart is responsible for the blood supply of the lungs,
where the blood picks up oxygen. The oxygen rich blood enters the left side
of the heart, which is responsible for circulating the blood through the rest of
the body. Fig. 2.1(b) shows the posterior view of the heart. In this view the
pulmonary veins can be seen, which play an important role in cardiac arrhythmia treatment. The sinoatrial (SA) node is the natural pacemaker of the heart
located in the right atrium. It initiates electrical impulses, that flow through
the rest of the heart’s electrical system. The next stage of the impulse on its
way through the heart is the atrioventricular (AV) node, electrically connecting
the atrial and ventricular chambers. The AV node slows down the impulses
before transmitting them through the bundle of His to the point of the apex of
the fascicular branches, which lead to the Purkinje fibers. They provide electrical conduction to the ventricles, causing the cardiac muscle of the ventricles
to contract at the appropriate times. Supraventricular tachycardias originate
from anatomic locations within the heart above the bundle of His (Josephson
& Kastor [1977]).
2.1.2
Mechanism
Cardiac arrhythmias result from abnormalities in the cardiac electrical system.
Abnormal impulses in the hearth can occur due to one of three mechanisms: abnormal automaticity, triggered activity or re-entry (McRury & Haines [1996]).
2.1.2.1
Abnormal Automaticity
Every muscle cell in the heart have the ability to initiate an impulse, however,
only some of them are capable of routinely triggering heart beats. Abnormal
automaticity is caused by cell groups that are only partially repolarized in diastole. These cell groups are prone to spontaneous, rapid firing. Parts of the
heart, that initiate an electrical impulse without being triggered by the SA node
are referred to as ectopic focus. Impulses initiated by an ectopic focus in the
atria reach various parts of the heart muscle with irregular timing and can lead
to poorly coordinated contraction. As a consequence, decrease in the heart’s
10
2. M EDICAL BACKGROUND
pumping efficiency may occur. Arrhythmias that are associated with abnormal
automaticity include inappropriate sinus tachycardia, automatic atrial tachycardia and junctional ectopic tachycardia.
2.1.2.2
Triggered Activity
Triggered activity has been hypothesized to be another arrhythmic mechanism.
These rapid sustained rhythms have been attributed to the occurrence of delayed depolarizations mediated by calcium and potassium currents. Supraventricular arrhythmias which may be caused by triggered activity include multifocal atrial tachycardia and some cases of automatic atrial tachycardia.
2.1.2.3
Re-entry
Re-entrant arrhythmias occur when an electrical impulse recurrently travels in
a circle within the heart, instead of propagating from one end of the heart to
the other and then stopping. Cardiac cells transmit action potential impulses
of excitation only once within a short time. Under normal circumstances, in
a cycle each cell responds only once. However, if the impulse cannot spread
through the heart quickly enough, part of it will arrive late and may be treated
as a new impulse. Depending on the timing, this phenomenon may result in
abnormal heart rhythm (De Luna [2012]).
A special form of re-entrant arrhythmia in which an entire chamber of the heart
is affected by multiple re-entrant circuits is termed fibrillation. Ventricular
fibrillation (VF) is life-threatening. If a heart goes into VF, effective circulation
of the blood stops. VF can lead to death within minutes, unless defibrillation is
provided immediately. By applying electric shock to the heart the cardiac cells
will be reset, so the normal heart beat can re-establish itself. Atrial fibrillation
(AF) is an arrhythmia which affects the upper chambers of the heart. Opposite
to VF, AF is not necessarily a medical emergency, however, it may increase the
risk of heart failure or stroke.
2.1.3
Atrial Fibrillation
Atrial fibrillation (AF) is the most common sustained atrial arrhythmia and is
present in 0.4% of the population (Kannel et al. [1982]). AF becomes more
2.1. C ARDIAC A RRHYTHMIA
11
common with age (Cappato et al. [2005]), it is particularly prevalent in the
elderly (Wilber et al. [2011]), affecting 4% of patients older than 60 years and
15% of patients older than 70 years (Ostrander et al. [1965]). AF is among the
fastest growing diseases, it is associated with a five times higher chance of a
stroke than normal sinus rhythm and twofold higher risk of cardiac and overall
mortality (Kannel et al. [1982]; Wolf et al. [1991]).
During AF, the AV node is being bombarded by more electrical impulses than
normal. Some of these signals catch the AV node when it is refractory, therefore they do not get passed through. However, other impulses get through the
AV node and cause faster ventricular rhythm than usual. The signals often
reach the AV node at the time when it is beginning to be able to transfer another impulse, resulting in an irregular electrical activity. As the AV node is
passing signals in an unpredictable way, the normal activity of the atria is lost.
The atria quiver instead of beating, which reduces the pumping performance of
the heart. Consequently, the blood flow is erratic, which might lead to thrombi
formation, potentially causing stroke. As a physiological reaction to the abnormal behavior, the atria and ventricles may enlarge in order to try to improve the
contractility, leading to increased chance of heart failure.
Studies on the underlying reasons for AF have reported contradictory findings. Haissaguerre et al. [1998] postulated that AF originates focally in the
pulmonary veins. Others, however, hold the view that AF is induced by reentrant circuits, that are created by pro-arrhytmogenic atrial substrate (Pappone et al. [2000]). In practice, it seems that there is residual atrial tissue in the
pulmonary veins that may induce focal electrical activity, producing re-entrant
circuits. This tissue is a residual from embryonic development. If it breaks
apart, a pro-arrhythmogenic region may be created that promotes arrhythmias.
The Ganglionic Plexi (GP) are also hypothesized to be involved in the initiation and maintenance of AF. The GP are fatty deposits on the surface of the
heart that are held together by interconnecting nerves. Randall et al. [1998]
showed that the posterior atrial GP neurons inhibit the propagation of excitory
impulses in the heart. Gray et al. [2004] have demonstrated that GP seem to
form feedback control loops, which contribute to re-entry.
If multiple episodes of AF are experienced, it is referred to as recurrent AF
that can be further classified as ”paroxysmal AF” (terminates within a week),
”persistent AF” (sustained longer than a week) or ”permanent AF” (sustained
longer than one year) (Hedna et al. [2012]). Paroxysmal AF is often an early
12
2. M EDICAL BACKGROUND
stage in the progression of the disease. As the atria is made more pro-arrhythmic
over time and electrical modifications take place, the originally paroxysmal AF
may become persistent or permanent. An international survey of > 10000 patients with AF seen at > 800 sites in 26 countries (Chiang et al. [2012]) has
revealed that 26.5% of the patients had paroxysmal, 23.8% had persistent and
49.6% had permanent AF.
Focal AF originates predominantly from the pulmonary veins. It is typically
paroxysmal AF – the arrhythmia that occurs and disappears spontaneously.
Wavelet AF is constituted of several reentrant circuits firing simultaneously,
often associated with later stages of the arrhythmia.
2.2
Treatment Options
The paroxysmal, persistent and permanent AF differ significantly in terms of
clinical characteristics. In addition, the underlying mechanism also affects the
management strategy and long-term outcomes. Due to the uncertainties in the
relative risks and benefits of different approaches the proper treatment method
often remains subject to debate.
2.2.1
Antiarrhythmic Drug Therapy
Antiarrhythmic drug therapy (ADT) has been available for nearly 100 years
(Zimetbaum [2012]). Drugs are still considered to be the first therapy of choice
for treating AF according to the 2014 AHA/ACC/HRS guidelines for the management of patients with AF (January et al. [2014]). The main objective of
ADT is to reduce the occurrence of symptomatic arrhythmia as well the mortality and hospitalization associated with it. Basically, there are two strategies
for the treatment of AF: maintaining sinus rhythm and heart rate control. The
latter one allows AF to persist. Trials have shown that a rhythm control strategy for management of AF may carry no benefit over rate control (van Gelder
et al. [2002]; Wyse et al. [2002]). However, the rate control approach offers a
lower risk of adverse drug effects. It is unlikely that any interventional procedure will be offered to patients with well managed AF, whose ventricular rate is
within the normal range, and there is a low chance of serious adverse events. In
these cases the patient will be placed on drugs in order to keep the ventricular
2.2. T REATMENT O PTIONS
13
rate low and to avoid any symptoms associated with AF. On the other hand, in
symptomatic patients ADT has demonstrated limited efficacy and the potential
for significant toxic effects (Roy et al. [2000]; Singh et al. [2005]), as well as
new heart rhythm problems (VerNooy & Mounsey [2004]; Gaita et al. [2002]).
2.2.2
Interventional Methods
Interventional approaches offer a chance to eliminate the symptoms or reduce
them. Even though complications from approved devices are more serious
and occur more often than with drugs, the low complication rate may prove
worthwhile to take the chance for a cure or a reduction in AF burden. The
rational behind these methods is that if the arrhythmic myocardium is injured
irreversibly, it prevents the flow of erratic electric signals so arrhythmia would
no longer occur. On the other hand, by creating scar tissue the mechanical
properties of the heart tissue are not significantly modified. Major requirements
of treating AF by cardiac ablation include:
• termination of AF and restoring normal atrial function,
• reducing risk of stroke,
• avoiding non-targeted tissue damage,
• performing the procedure on a beating heart,
• limited exposure to fluoroscopy and
• confirmation of success.
Interventional management of AF includes surgical (epicardial) as well as percutaneous (endocardial) approaches. The epicardial methods produce lesions
from the outside in, whereas the endocardial ones from the inside out. The endocardial approach offers minimally invasive percutaneous entry into the heart,
associated with reduced length of hospitalization, low expenses and reasonably low morbidity and mortality. However, clinical outcomes have proven
to be better in the epicardial approach. Minimally invasive port-access procedures are now performed epicardially. These methods provide much better
visualization, thus lesion transmurality can be determined by sight for many
of the lesions. The morbidity and mortality rates seem to be comparable to
14
2. M EDICAL BACKGROUND
the endocardial approach. Nevertheless, port-access techniques require general anesthesia and somewhat longer hospital stays.
2.2.2.1
Lesion Sets
The regions that are to be ablated are defined by lesion sets. The main purpose of lesion sets is to break the re-entrant circuits and to create an electrical
pathway from the SA node to the AV node in order to restore normal atrial function. Various lesion sets have been proposed over the decades, the most widely
adopted is the Cox III maze procedure (Cox et al. [2000]), see Fig. 2.2(a).
Originally it was an open chest cut and sew operation that was too long, too
difficult and had too many clinical complications. Pacemaker implants were
required in 5% to 10% of the patients undergoing the intervention, mortality
rates were ranging from 2% to 3%. Nevertheless, AF was successfully cured
in 95% of patients with paroxysmal AF (Schaff et al. [2000]).
(a)
(b)
(c)
Figure 2.2: Commonly used lesion sets for cardiac ablation: Cox III maze (a),
mini maze (b) and radial procedure (c).
Other techniques aim to take advantage of the Cox III maze lesion set and try
to further reduce the complications that occur and to achieve improved atrial
function. Instead of the ’cut and sew’ approach, these methods use catheter
ablation to create lesions. In order to reduce the amount of tissue damage,
the mini maze lesion set (Szalay et al. [1999]) uses a reduced number of lines
compared to the Cox III Maze pattern, as shown in Fig 2.2(b). One of the major
advantages of this procedure is that it can be performed by many catheters.
Nitta et al. [1999] introduced another method what they called radial approach,
which creates lesions parallel to the coronary arteries from the SA node toward
2.3. T RANSCATHETER A BLATION
15
the aortic valve annulus, as demonstrated in Fig. 2.2(c). It is assumed, that the
radial approach preserves more physiologic atrial transport function. Although
the various lesion sets might have certain advantages compared to the others, it
has not been proven conclusively that any of them is better than another.
2.3
Transcatheter Ablation
The advent of technological advances in catheter design has led to the widespread
use of transcatheter ablation techniques. Transcatheter ablation has become the
treatment of choice for a number of cardiac arrhythmias, in case the patient
does not respond to medication.
During cardiac ablation a catheter is inserted into a vein (typically in the groin
or neck) and steered into the heart, where the abnormally conducting region is
destroyed, as depicted in Fig. 2.3. A handle featuring a wire steering mechanism is responsible for the ablation tip positioning within the heart chamber.
The mechanical actuation device in the handle controls how much torque is
provided by curving the distal section of the catheter.
Figure 2.3: Illustration of a catheter inserted into the heart.
16
2. M EDICAL BACKGROUND
Atrial arrhythmias that may be successfully treated by transcatheter ablation
include focal arrhythmias such as automatic atrial tachycardia, atrial flutter
and atrial fibrillation (McRury & Haines [1996]). Nonetheless, focal origins in
difficult sites may have a lower ablation success rate, as well as higher recurrence rate. Multifocal atrial tachycardias cannot be treated by catheter ablation
techniques.
2.3.1
Direct Current Catheter Ablation
The first catheter ablation was carried out coincidentally by Vedel et al. [1979]
while performing a defibrillation procedure, during which a defibrillator electrode was in electrical contact with a catheter electrode, creating a complete AV
block. High energy direct current catheter ablation was introduced into clinical
use in the early 80’s by Gallagher et al. [1982] and Scheinman et al. [1982]
to treat supraventricular tachycardias. A DC shock was delivered in a unipolar way from the tip electrode to a large patch electrode in contact with the
skin of the patient. The applied waveform was typically an exponential decay
(McRury & Haines [1996]). The mechanism of tissue necrosis was believed
to be the combination of heating, direct electrical damage and barotrauma.
Besides being highly efficient, DC ablation is accompanied by severe complications. During the delivery of high energy DC shock, the temperature may
rise over 5000◦ C, creating rapidly expanding gas bubbles in the vicinity of the
catheter tip. High current arcing through the gas bubbles causes damage to the
electrode tip (Boyd & Holt [1985]; Haines & DiMarco [1992]). In addition,
DC shock therapy may induce ventricular arrhythmia (Moak et al. [1987]).
Owing to the short duration of conventional DC shock ablation, controlling the
myocardial damage is difficult. For the aforementioned reasons, DC catheter
ablation for cardiac arrhythmias has been replaced by other techniques.
2.3.2
Radiofrequency Catheter Ablation
Radiofrequency ablation (RFA) is a commonly used treatment modality for
various diseases including tumor (van Sonnenberg et al. [2008]), uterine bleeding (Cooper & Gimpelson [2004]), and cardiac arrhythmia (McRury & Haines
[1996]). RFA has several advantages over DC ablation. There is minimal
discomfort during RF power delivery, so it is possible to perform the procedure in conscious patients. Unlike DC ablation, RFA is not associated with
2.3. T RANSCATHETER A BLATION
17
skeletal- and cardiac-muscle stimulation or barotrauma. RFA makes it possible to achieve very focal injury. An RFA power delivery period takes typically
30 to 60s, thus potential complications can be avoided by terminating the application. The RF generators approved for cardiac catheter ablation use a floating
power source to produce unmodulated sine wave current in the RF range. The
power is delivered in a unipolar way between the tip electrode of an ablation
catheter and a dispersive ground patch applied to the patient’s skin. Application of RF current between the catheter tip and a patch electrode results in local
heating around the tip electrode, and subsequent tissue necrosis due to protein
coagulation. Fig. 2.4 shows a general setup, where an RF electrode is advanced
into a heart chamber.
Figure 2.4: Illustration of an RF ablation setup. A RF catheter is inserted
into the heart under imaging guidance. RF energy provided by a generator is
applied to the catheter electrode. A reference patch electrode placed on the
patient’s back serves as return path for the RF current.
The ablation electrode, located at the catheter distal tip, is connected to one
terminal of the RF generator. In order to provide good electrical conductivity between the tip and the ablated tissue, the RF electrode is typically made
of stainless steel, platinum, or Ni-Ti alloys (Wittkampf & Nakagawa [2006]).
Common dimensions of catheter distal tip are 6 − 8F diameter (1F = 0.3mm)
and 4 − 10mm length (Langberg et al. [1993a]).
In unipolar mode the other terminal of the RF generator is connected to a patch
electrode applied to the patient’s skin typically at the dorsal side of the body,
18
2. M EDICAL BACKGROUND
see Fig. 2.5(a). In order to ensure that only a small fraction of the delivered
RF power is lost at the skin interface and no epidermal burns occur (Steinke
et al. [2003]), the patch electrode typically has a large area of 150cm2 (Panescu
[1997]). This way the current densities over its surface are significantly lower
than those of the ablation electrode-tissue interface. The skin in contact with
the patch electrode also heats up, but owing to the large patch surface area temperature rises are low. To prevent skin discomfort or burn, the patch electrode
is coated by electrically conducting gel.
(a)
(b)
Figure 2.5: Illustration of unipolar (a) and bipolar (b) power delivery during
RF ablation.
Alternatively, bipolar power delivery between two catheter electrodes can also
be employed (Bashir et al. [1993]). This method is associated with two areas of
high current density, thus the region of greatest resistive heating can be bigger
compared to the unipolar technique, as shown in Fig. 2.5(b). However, the
major drawback of bipolar method is that the efficacy is highly dependent on
the orientation of the catheter tip, even a slight deviation from the ideal case
may result in not sufficient power delivery.
Most of the RFA applications operate in the 300 − 500kHz range, where the
impedance is significantly lower compared to DC or lower frequency harmonic
excitation (Geddes & Backer [1989]; Bragos et al. [1996]). The frequency
dependence of the impedance originates from the capacitive behavior caused
by the ion accumulation around the cell membranes (Pethig & Kell [1987]).
The aforementioned range is high enough to avoid myocardial depolarization,
which might induce cardiac arrhythmias (Haines [1993]). On the other hand,
this frequency range is low enough to ensure that the mechanism of tissue
heating is purely resistive (McRury & Haines [1996]). Another reason is that
2.3. T RANSCATHETER A BLATION
19
diagnostic devices are operated in the same range (Haemmerich [2010]). Consequently, both the diagnostic electrophysiologic test and the catheter ablation
can be carried out in a single session (Calkins et al. [1991]).
2.3.2.1
Mechanism of Lesion Formation
The primary mechanism of irreversible tissue injury during RFA is presumed to
be thermally mediated (Simmers et al. [1996]; McRury & Haines [1996]). Hyperthermia causes significant changes in myocardial cellular electrophysiological properties, such as membrane depolarization, loss of excitability and abnormal automaticity (Nath et al. [1993]). Irreversible conduction block occurs
at locations where the tissue temperature exceeds 50◦ C (Nath et al. [1993]).
The length of exposure to heating also has an influence on whether cell death
occurs. At 50◦ C it takes a few minutes to kill cells, whereas at temperatures
above 60◦ C only seconds (Dewhirst et al. [2003]).
Typical ablation catheters create lesions approximately 5 to 6mm in diameter
and 2 to 3mm deep (Haines et al. [1990]). Lesion dimensions are often defined
by the tissue region within the 50◦ C isotherm (Berjano [2006]). This well demarcated zone of coagulation necrosis is surrounded by a zone of hemorrhage
and inflammatory cells (Huang et al. [1988]). Decrease in microvascular perfusion and injury to the myocytes can be observed beyond the visible pathological lesion border as well (He et al. [1994a]; Stevenson et al. [1986]). If the site
of origin of an arrhythmia has not been successfully ablated, inflammation can
resolve, resulting in the recurrence of arrhythmia (Langberg et al. [1992b]).
Conversely, the border zone of a lesion may become permanently nonfunctional and the inflammation within this zone can cause progressive necrosis
(Langberg et al. [1993b]).
Besides hyperthermia, high-intensity electric fields can also induce myocyte
damage (Panescu [1997]). Nath et al. [1994] observed conduction blocks in
cardiac tissue within 1s of RF power delivery, before significant heating could
have occurred.
2.3.2.2
Thermodynamics of Radiofrequency Ablation
During unipolar RFA, alternating current flows between the catheter electrode
in the heart and the indifferent electrode applied to the skin. Inside the abla-
20
2. M EDICAL BACKGROUND
tion electronics, the cables, the dispersive and the catheter electrode the charge
carriers are represented by free electrons. In the tissue, however, ions carry the
electric current. The applied RF current causes ion movement, and ohmic heat
is produced by friction of the ions (Haemmerich [2010]). Various parameters
of the tissue – blood thermodynamic system and the RF ablation setup affect
the overall tissue temperature distribution and indirectly the lesion dimensions.
These factors include:
• electrical power density distribution,
• thermal conduction,
• convective heat loss at the tissue-electrode-blood interface,
• duration of RF application,
• electrode orientation with respect to tissue.
The temperature distibution during RF ablation is given by the heat transfer
equation (Kreith [2000]):
ρc
∂T
= ∇ · (κ∇T ) + Qp + Qm − Qh − Qel
∂t
(2.1)
where ρ is the volumetric mass density, c is the heat capacity, T represents
the spatially and temporally varying tissue temperature and κ is the thermal
conductivity. The left-hand side term represents change in tissue temperature
due to heating or cooling, and the first term on the right-hand side describes
thermal conduction. Qp , Qm , Qh and Qel account for the dissipated power,
the power generated by metabolic processes, the power loss associated with
convection due to the blood vessels in the ablated tissue and the heat exchanged
between the tissue and the ablation electrode, respectively.
The direct resistive heating is characterized by the dissipated power (Panescu
[1997]):
Qp = J · E,
(2.2)
where J is the current density an E is the electric field intensity. The region of
highest resistance in the circuit is represented by the catheter tip-tissue interface. The current density is highest immediately contiguous to the catheter
2.3. T RANSCATHETER A BLATION
21
electrode, and decays rapidly with distance. For small diameter, spherical
electrodes, the current flows outwards radially and current density therefore
decreases with the square of distance from the center of the electrode. As a
consequence, power dissipation and direct resistive heat production per unit
volume decreases with the fourth power of radial distance (Lustgarten & Spector [2008]). Therefore, the tissue zone heated based upon the Joule effect is
restricted to a narrow rim in the vicinity of the electrode. In practice, approximately 90% of the delivered power is absorbed within the first 1 − 1.5mm
from the electrode surface (Wittkampf & Nakagawa [2006]). Beyond these
first few millimeters the current path is determined by the electrical properties
of the surrounding tissue and the position of the indifferent electrode (Labonté
[1994]).
At distances further from the electrode, thermal conduction is the main contributing factor to tissue heating (Schramm et al. [2006]). Unlike resistive
heating, that takes place immediately with RF current delivery, heat conduction is slow (Wittkampf & Nakagawa [2006]). Thus, it takes 30 − 60s to reach
thermal equilibrium, at which there is a steady flow of heat from the electrode
to its environment (Wittkampf et al. [1996]). At this state tissue temperatures
reach their maximum value, and termination of RF power delivery causes a
direct drop in tissue temperatures. However, early interruption of the application when thermal equilibrium has not yet been reached, does not immediately
terminate conductive heating.
Metabolic heat production, Qm , is believed to have negligible contribution to
permanent lesion formation.
RF power delivery is limited also by convective heat exchange between the
tissue and the circulating blood pool
Qh = hbl (T − Tbl ),
(2.3)
hbl = ρbl · cbl · wbl ,
(2.4)
where Tbl is the blood temperature, ρbl represents the blood mass density, cbl
is the blood heat capacity and wbl is the blood perfusion. As a consequence
of the convective cooling generated by the blood flow, even though the current
density maximum is located at the tissue-electrode interface, the highest tissue
22
2. M EDICAL BACKGROUND
Figure 2.6: Heat distribution during RFA procedure. Due to convective heat
exchange the hottest point is located in the myocardium.
temperature is located beneath the electrode surface (Panescu [1997]). This
phenomenon is responsible for the ’teardrop’ shape of a typical lesion with its
widest point approximately 1 − 2mm deep in the myocardium, as shown in
Fig. 2.6.
The contribution of microvascular perfusion to convective cooling during RF
heating appears to be insignificant (McRury & Haines [1996]).
The heat exchange between the tissue and the ablation electrode (Qel ) is usually considered negligible. However, it plays an important role in the design of
actively cooled ablation catheter systems.
2.3.2.3
Complications
Typical complications associated with RFA range from reversible problems to
potentially life-threatening complications. When the electrode-tissue interface
temperature reaches 80◦ C, coagulated plasma and carbonized tissue may form
on the electrode, that prevents any further RF power delivery (Matsudaira et al.
[2003]). Moreover, tissue charring and the presence of blood clots increase the
risk of thromboembolic complications. Tissue temperatures above 100◦ C turn
water into steam. Being trapped in the myocardium, the steam may undergo
explosive expansion often referred to as steam pop. In extreme cases, muscle
2.3. T RANSCATHETER A BLATION
23
dysfunctions or cardiac perforations may occur (McRury & Haines [1996]).
Additionally, prolonged procedures owing to complications of site identification or catheter positioning may lead to significant levels of radiation.
Patients with smaller hearts are exposed to significantly higher risk of complications (Wood & Morady [1999]). Therefore, RFA is performed in children
only in serious cases that cannot be treated by drugs. Besides, some forms of
arrhythmia may resolve spontaneously with age.
2.3.2.4
Temperature Measurement
To perform ablation efficiently, sufficient power is required to obtain tissue
temperatures above 50◦ C in the targeted tissue volume. On the other hand,
the hottest site must be maintained below 100◦ C. This requires balancing the
competing demands of efficacy against those of safety. Even though the range
between 50◦ C and 100◦ C may appear to provide a wide margin of safety, the
difficulties associated with tissue temperature measurement make it difficult to
guarantee that the entire ablated tissue volume falls within this range. Currently there is no technology available that allows for direct tissue temperature
measurement (Lustgarten & Spector [2008]).
Temperature monitoring of RF ablation is a widely used method to monitor lesion formation and to avoid excessive tissue heating. Most of the commercially
available RF generators include some form of temperature monitoring. It has
been shown in vitro that electrode-tissue interface temperature is proportional
to lesion size (Haines & Watson [1989]), even so poor correlation was found
in vivo (Hindricks et al. [1989]). The main reasons for this are the variance in
electrode-tissue contact pressure and area and the fluctuating convective heat
loss.
During RFA alternating current heats cardiac tissue, which in turn transfers
heat to the catheter electrode (Eick [2003]). The greatest amount of the heating
and lesion formation occurs near the electrode tip where the contact surface
area is small and the current density is the highest (Cosman et al. [1988]). As a
consequence, the catheter tip temperature is lower than – or ideally equal to –
the actual tissue surface temperature (Wittkampf & Nakagawa [2006]). Moreover, due to convective heat exchange by blood, the highest tissue temperature
may be measured 1−2mm deep in the myocardium (Kongsgaard et al. [1997])
24
2. M EDICAL BACKGROUND
and the electrode-tissue interface temperature can underestimate these temperatures by 10 − 30◦ C (Kottkamp et al. [1997]). The underestimation error is
affected significantly by the temperature sensor location. Depending on the
geometry, the highest amount of heating occurs at the greatest current density.
For example, owing to the edge effect the junction of the electrode and catheter
insulation can reach higher temperatures than the electrode tip. Besides, convective cooling reduces the surface temperature of the catheter tip too, potentially resulting in a temperature gradient inside the electrode. The efficacy of
temperature measurement depends upon the sensor’s position in the catheter
tip. Since electrode temperature is usually measured at one specific location
inside the catheter tip, it is not always a good indicator of tissue temperature.
In order to avoid complications associated with overheating it is advisable to
set the target temperature to approximately 70◦ C (McRury & Haines [1996]).
Calkins et al. [1994] proposed a design using a thermocouple embedded in a
solid distal electrode. Since the sensor measures the mean temperature of the
entire solid electrode tip exposed to the bloodstream, the electrode-tissue interface temperature may be underestimated. Another approach uses electrically
isolated thermistors centered at the tip of a hollow distal electrode (Blouin et al.
[1991]). This method can successfully estimate the electrode – tissue interface
temperature with most catheter positions in vivo, unless the catheter is positioned parallel to the tissue surface.
2.3.2.5
Control Methods
Control procedures are widely used in order to perform RFA efficiently and
safely. Open loop temperature monitoring methods aim to provide information about the efficiency of tissue heating. Closed loop temperature monitoring
methods feature a control algorithm in order to prevent the measured temperature from exceeding a target value (Calkins et al. [1994]). The generator power
is regulated by the electrode-tissue interface temperature. Temperature control
minimizes the risk of complications at the cost of limited power delivery and
lesion depth (Haemmerich [2010]).
In power control mode the RF energy delivery is adjusted to keep applied power
constant. Nonetheless, even in power control mode the process is terminated
in case the measured temperature exceeds a preset temperature threshold. Although in vitro studies have demonstrated that lesion dimensions are proportional to generator power (Haines & Watson [1989]), in vivo sites of high blood
2.3. T RANSCATHETER A BLATION
25
flow require significantly higher power than those in lower flow (Wittkampf
et al. [1989]).
Monitoring impedance between the RF electrode and the ground pad can be
used to detect rapid impedance rises, which may help indicate potentially lifethreatening phenomena, such as coagulum formation and vaporization (McRury
& Haines [1996]). However, coagulum formation has been reported even in the
absence of remarkable interface impedance rise (Demolin et al. [2002]; Matsudaira et al. [2003]). In most RFA devices if impedance exceeds a certain
threshold, RF power delivery is terminated to prevent complications.
2.3.2.6
Importance of Tissue Contact
Energy transfer to the myocardium during RFA is dramatically affected by
the contact conditions (Burkhardt & Natale [2009]), thus maintaining tight
catheter-tissue contact is a key criterion for safe and efficient lesion formation. Part of the endocardial electrode contacts cardiac tissue and the rest is
in contact with blood. As the specific impedance of blood is lower than that
of tissue (Wittkampf & Nakagawa [2006]), a significant amount of the power
is transmitted through the portion of the electrode in contact with blood. In
order to minimize this loss, the electrode surface area in contact with the endocardium should be maximized throughout the full cardiac cycle (Langberg
et al. [1992a]).
Differences in contact conditions in vivo cause significant variations in power
requirements (Kalman et al. [1997]). Too little force is associated with decreased power delivery efficiency and small lesion volume. Improvement in
tissue contact results in better efficacy, hence the same tissue temperatures and
lesion volume may be achieved at lower power settings (Wittkampf & Nakagawa [2006]). In case of higher contact force a greater part of the electrode is
in contact with the heated tissue. Furthermore, the blood flow cools a smaller
part of the electrode, allowing for diminished inconsistency between tissue and
electrode temperature. Excess contact force may lead to pressure and overheating related complications, such as charring and steam pops, as it has been
shown by Yokoyama et al. [2008] and Thiagalingam et al. [2010].
In the absence of visualization of the catheter electrode-tissue interface, contact
conditions can be estimated from catheter tip temperatures, from impedance
changes or more recently from contact force.
26
2. M EDICAL BACKGROUND
(a)
(b)
Figure 2.7: Novel methods ensuring tight catheter electrode – tissue contact:
magnetic coupling (a) and vacuum suction (b).
Various methods have been introduced to improve the catheter-tissue contact.
Maestroheart SA have proposed an apparatus utilizing magnetic coupling for
ablating cardiac tissues along continuous lines in the left atrium (Verin & Flaction [2011]). The method features a guiding part intended to be introduced in
the hollow structure around the left atrium and an ablating member comprising an ablation electrode mounted at the tip of a catheter. Both components
are magnetized and can enter into magnetic coupling if they are brought in
close contact, as demonstrated in Fig. 2.7(a). Once the magnetic coupling is
achieved, the catheter tip can be guided by moving the guiding member.
A unipolar RFA device with suction support has been introduced by Kadoya
et al. [2010]. The suction support adapter ensures tight contact between the
ablation probe and the atrial wall to be ablated, as shown in Fig. 2.7(a). Due
to the enhanced contact conditions significantly deeper coagulation range has
been achieved in porcine hearts in vivo compared to conventional methods.
2.3.3
Techniques for Increasing Lesion Size
2.3.3.1
Elongated Electrode
Larger active surface and accordingly augmented heat dispersion can be achieved
by the use of longer electrode tips. Elongated geometries result in increased
heat conduction into the blood stream by convective cooling. Consequently,
more electrical power can be delivered without the risk of tissue overheating.
2.3. T RANSCATHETER A BLATION
27
High current density is distributed deeper into the tissue and increased lesion
size can be obtained (Haines et al. [1990]). On the other hand, this way a significant proportion of the delivered power is lost to the blood stream where it
does not contribute to lesion formation. Therefore, a larger electrode reduces
the overall efficiency of power delivery (Langberg et al. [1990]). Accordingly,
the same total power with an elongated electrode tip results in smaller lesions.
2.3.3.2
Electrode Plating
Another promising passive method for enhanced heat dispersion is the use of
metal plating with higher thermal conductivity (Simmons et al. [1996]; Lewalter et al. [2005]). Owing to the special coating, heat generated in the tissue is
transferred more efficiently to the blood pool. Increased electrode cooling allows for delivery of higher power, which may create larger lesions at the same
electrode target temperature.
2.3.3.3
Irrigated Ablation
Other techniques utilize active cooling to increase lesion dimensions. Irrigation is a commonly used method that allows for greater power delivery either
by circulating irrigant fluid inside the catheter, or flushing iced saline through
small perforations in the electrode. The objective of irrigation is to achieve
deeper lesions at the lowest effective power setting and shortest duration. By
actively cooling the electrode the position of highest temperature is located
deeper in the tissue, which may result in a larger lesion (Haemmerich et al.
[2003]). This is often interpreted erroneously as if irrigation pushes the lesion deeper into the tissue. Active cooling does not make ablation either safer
or more effective. During active cooling catheter temperature does not reflect
tissue temperature, therefore irrigated ablation is always performed in power
control mode (Lustgarten & Spector [2008]). The use of irrigated RF is considered when ablating thick tissue, in the arterial circulation or areas of limited
blood flow.
2.3.3.4
Balloon-based Method
Increasing the contact area between the RFA catheter and the ablated tissue is
another approach to achieve greater lesions. Balloon-based ablation is a tech-
28
2. M EDICAL BACKGROUND
nique suitable for pulmonary vein isolation (Burkhardt & Natale [2009]). This
method employs an inflatable balloon plated with conductive material that fits
in the veins and can create large area lesions (Groeneveld et al. [1993]). This
way the isolation of the veins can be carried out with a reduced number of
lesions. Besides RFA, the balloon-based method is suitable for other ablation modalities, including laser, ultrasound and cryoablation. Nevertheless,
balloon-based ablation has limited success because pulmonary vein isolation
alone is not sufficient for all forms of atrial fibrillation.
2.3.3.5
Multielectrode Ablation
Multielectrode RFA increases the electrode size by simultaneous RF power delivery to multiple electrodes (Chang et al. [1993]). There are various circular
and mesh array shaped designs based on unipolar or bipolar RF power delivery. Similarly to balloon-based systems, multielectrode RFA cannot be used
at locations that are not accessible to large catheters. Both balloon based and
multielectrode RFA methods require several applications for each vein.
2.3.4
Alternative Methods
Some other methods utilize alternative energy sources to create lesions. Each
of these ablation technologies has different potential benefits.
2.3.4.1
Cryoablation
In catheter-based cryoablation procedures the catheter tip is cooled to sub-zero
temperatures, removing heat from tissue by freezing it to temperatures as low
as −90◦ C at the catheter-tissue interface (Piccini & Daubert [2011]). The main
mechanism of tissue injury is thermal, the formation of ice crystals in the cells
causes irreversible membrane damage. Besides, as a consequence of blood coagulation the interrupted blood flow results in ischemia and cell death. Finally,
extreme low temperatures may induce programmed cell death. With cryoablation critical sites can be mapped by reversible freezing without permanent
damage. If the result is satisfying, permanent ablation can be achieved by
further freezing the tissue to lower temperatures. In general, cryoablation is
associated with diminished risk of ablation-induced perforation (Evonich III
2.3. T RANSCATHETER A BLATION
29
et al. [2007]). However, this technique has higher recurrence rate and takes
longer on average compared to RFA.
2.3.4.2
Laser Photoablation
Laser ablation delivers argon, excimer or neodymium-yttrium-aluminium-garnet
(Nd-YAG) laser energy through a felxible fiberoptic to create lesions (Haines
[1992]). The mechanism of tissue injury is thermal. The tissue in direct contact with the laser is vaporized, whereas the deeper myocardium is heated by
passive heat exchange. As light does not penetrate tissue or blood deeply, maintaining good catheter-tissue contact during laser ablation procedures is important. One of the main advantages of laser therapy is that it can be combined
with endoscopic visualization. Despite its proven ability to treat ventricular
arrhythmias (Svenson et al. [1987]), laser ablation has remained less popular
because of the laser equipment’s high cost and the potential complications associated with tissue evaporation and perforation.
2.3.4.3
Microwave Ablation
Microwave (MW) is another promising ablation modality. The mechanism
of tissue injury is thermally mediated, however, contrary to RF ablation, the
heating is dielectric. The electromagnetic energy is converted to heat by the
oscillation of water molecules. Comparisons of in vivo lesions from RF and
MW (915 and 2450MHz) catheter sources demonstrated that lesion formation
takes longer with MW but the volume of direct heating is greater (Whayne
et al. [1994]). Even though MW energy appears to be suitable for transcatheter
ablation, current technologies cannot provide sufficient power delivery and adequate lesion formation.
2.3.4.4
Ultrasound Ablation
Ultrasound ablation modalities also have a potential for creating lesions (He
et al. [1994b]). During ultrasound ablation mechanical pressure waves are
propagated through the medium and converted into heat, hence the mechanism
of injury is thermal. However, unlike in RF ablation, the lesion formation is
not dependent on surface heating. Since the depth of penetration is determined
30
2. M EDICAL BACKGROUND
by the crystal vibration frequency, the lesion dimensions may potentially be
set by tuning the actuation frequency of the ultrasound transducer. Moreover,
the imaging capabilities of the US transducer could also be used. Owing to the
high tissue to blood absorption ratio, the catheter-tissue contact is less important compared to other techniques. The major challenge of US ablation remains
to be the difficulty to produce stable and durable crystal actuators that are small
enough to fit in a catheter tip.
2.3.5
Tool Navigation
In general, device-based treatment methods take advantage of anatomical features. The success of catheter ablation is related to the accurate identification
of the arrhythmia’s site of origin and how well the operator can stabilize the
catheter tip at this point. Fluoroscopy alone has limited ability to determine the
exact location and orientation of the ablation catheter in the heart (Burkhardt
& Natale [2009]). In general, ablative procedures are aided by X-ray computed tomography (CT), magnetic resonance imaging (MRI) or intracardiac
echocardiography (ICE) to guide the catheter to a target location (Haemmerich
[2010]).
Cardiac mapping is a widely used guidance method for catheter ablation. In
this technique electrical measurements are made by additional special recording catheters and incidentally the ablation catheter itself. Monitoring the electrical activity provides diagnosis of the arrhythmia and enables accurate localization of the target site. These electrical activity measurements at various
locations produce activation maps that shed light on the spread of impulses
through the heart.
Other methods aim to achieve improved guidance by using external magnetic
field. AEON scientific AG has presented Aeon Phocus, a robotic remote cardiac catheter steering system providing excellent reachability within all four
heart chambers. The field generator consists of two movable parts that contain
a set of electromagnets. The field generator coils are arranged in a way that
magnetic field can be set to any direction without needing to move the electromagnets. By manipulating the external magnetic field, the system is capable
of precisely navigating the catheter and pushing it against cardiac tissue during
ablation. Additionally, the Aeon Phocus can be operated remotely from a separate control room, enabling the electrophysiologist to avoid X-ray radiation
exposure associated with fluoroscopy.
2.3. T RANSCATHETER A BLATION
31
Stereotaxis Inc. has introduced remote magnetic navigation system (MNS) to
improve precision and safety during ablation (Hall et al. [2001]). This method
utilizes external magnetic field to guide the catheter tip in the heart. Unlike
conventional ablation procedures, this technique uses very soft catheters. After
establishing contact between the magnetically responsive catheter tip and the
cardiac tissue, the contact force is controlled by the external electromagnetic
device. This way the catheter can move together with the endocardium, obtaining more consistent contact in spite of the cardiac motion. This type of contact
would not be possible to achieve with a manual catheter without applying excessive force. Ablation with MNS proved to be safer and more effective than
conventional ablation (Bauernfeind et al. [2011]).
3
Force Sensors
This chapter first details the common force/torque sensor trends and architectures for minimally invasive surgical applications. Following our motivation
described in Section 3.2, the sensor concept is presented, such as the mechanical and electrical aspects of development. Then, the specific sensing principle,
mechanical design, gauging concept, calibration strategies, tools and results
are explained for the Euler-Bernoulli and the Butterfly sensors. Finally, the
latest versions of the sensors are depicted at the end of this chapter.
3.1
Introduction
Multi-axial force/torque sensors are widely used for generating feedback for
various medical fields. Monitoring the contact force is beneficial in minimally
invasive surgery (MIS), which is gaining popularity because it reduces hospitalization time and post operative trauma. The cumbersome laparoscopic
instruments, however, limit the surgeon’s ability to control the applied force.
The absence of force feedback results in less precise tool navigation and higher
risk of collateral damage during the intervention. The solution is embedding a
multi-axial force sensor in the MIS tools and providing the surgeon force feedback. Valdastri et al. [2006] demonstrated a bending manipulator mounted on
a triaxial force sensor, what they intended to use for fetal surgery procedures.
Minimally invasive robotic surgery (MIRS) further increases MIS advantages
by offering increased precision and reliability by extending the force sensitivity
beyond the surgeon’s threshold of perception.
Several sensors were realized using different sensing principles and fabrication
technologies. Valdastri et al. [2005] gave a thorough summary of multi-axial
34
3. F ORCE S ENSORS
miniaturized force sensors up to the date of the publication. In Table 3.1 we
extended their review with more recent publications. Force sensors can be
classified by several criteria, such as sensing principle, fabrication technology,
sensing range or accuracy.
(a)
(b)
Figure 3.1: Commonly used multi-axial MEMS force sensor structures with
horizontal cantilevers (a) and membrane (b).
Many of the sensors utilize piezoresistive principle by doping strain gauges in
single crystal Si. This method is convenient because of the high gauge factor of
silicon (about 200 − 300) and the ability to implement the sensor on a singlecrystal silicon structure of a bridge’s plate. Several integrated piezoresistive
MEMS structures consist of a central mass on a flexible vertical membrane
or cantilevers, as shown in 3.1. This approach allows for small size and high
sensitivity, however the vulnerable sensor body introduces limitations on the
force range, which is typically between 0.001 and 0.2N for these sensors. The
low force range sensors have high accuracy and are used in micro systems and
for measurement of forces created by biological organisms. These sensors are
also used in biomedical devices and for estimation of 3D contact forces. Wang
et al. [2009] introduced a tri-axial tactile force sensor featuring four cantilever
beams supporting a suspended mass with a quartz fiber probe attached on it.
The MEMS structure showed very high sensitivity (3µN ) in low force range,
however can measure only up to 1mN , far too small for the typical force range
of MIS. A three-axis load detector using four piezoresistive sensors on a exible
silicon membrane was designed by Benfield et al. [2011]. They investigated the
possibility of increasing the shear to normal sensitivity ratio without reducing
the sensor’s strength. Hu et al. [2010] developed a 3D tactile sensor for MIS
inspired by the principle of hair cells. The sensor features a piezoresistor –
embedded polyimide diaphragm and a central silicon post attached to it. The
3.1. I NTRODUCTION
35
high aspect ratio provides increased shear sensitivity. 4 sensors form a 2 ×
2 array, each sensor has a size of 390µm × 470µm. As the full scale of
this sensor is in the mN range, it can only be considered for microsurgical
applications. Wen & Fang [2008] suggested a structure with four piezoresistive
cantilevers and a polymer membrane covering them. The main advantage of
this approach is that by adding nano-particles to the polymer, the stiffness of
the membrane can be tuned, thus the sensor sensitivity can be scaled. This
way higher tolerable load can be achieved at the cost of significantly decreased
sensitivity.
Multi axial force sensors for biomedical devices and tactile sensing typically
have the full scale of 0.5 − 5N , which is achievable by introducing a polymer
layer between the otherwise vulnerable sensing element and the contact area.
However, this setup can be problematic because of the reduced accuracy and
the viscoelastic properties of the polymer interface, introducing dependence
of the measured force on the loading velocity and direction. Vasarhelyi et al.
[2006] developed a 2 × 2 sensor array covered by an elastic material which
they used for tactile sensing. The sensor elements consist of a suspended crosslike bridge and four piezoresistors at the suspension points. They found that
the measured profiles matched the simulated strain distribution better than the
stress. Another tactile sensor concept was presented by Ho et al. [2010]. Three
MEMS fabricated sensors form an equilateral triangle for the purpose of manipulating small objects. Valdastri et al. [2006] introduced a three-axial silicon
based force sensor with high linearity and low hysteresis for biomechanical
measurements.
A similar concept was presented by Spinner et al. [2006]. They designed a
sensor featuring piezoresistive mechanical stress transducers integrated in thin
membrane hinges supporting a suspended flexible cross structure. Kristiansen
et al. [2008] presented a setup with four or eight strain gauges attached to the
four arms of a single cross-shaped force-measuring cantilever spring that is
capable of measuring either displacement or force components in three orthogonal directions. A flexible tactile sensor skin with 4 × 4 MEMS sensor cells
was designed by Shan et al. [2005]. The cells are hybrid integrated on a flexible
printed circuit board.
36
3. F ORCE S ENSORS
Device
Description
Sensing
Principle
Fabrication
Technology
# of
axes
Wang et al. [2009]
Si structure of a column on
4 bridges
PiezoResistive
SOI Micro
Machining
3
Benfield et al. [2011]
Column on a rectangular
plate with 4 strain gauges
PiezoResistive
Bulk Micro
Machining
3
Hu et al. [2010]
Si structure of a column on
circular diaphragm, 2X2
array
PiezoResistive
Bulk Micro
Machining
3
Wen & Fang [2008]
4 Si cantilevers embedded
in PDMS
PiezoResistive
Bulk Micro
Machining
3
Ho et al. [2010]
Si structure of a column
on a rectangular plate supported by 4 beams
PiezoResistive
Bulk Micro
Machining
3
Vasarhelyi et al. [2006]
Si structure of 4 bridges in
an elastic substrate
PiezoResistive
Bulk Micro
Machining
3
Valdastri et al. [2005]
Si structure of a column on
4 bridges
PiezoResistive
Bulk Micro
Machining
3
Spinner et al. [2006]
Si structure of a column on
4 bridges
PiezoResistive
Bulk Micro
Machining
3
Kristiansen et al. [2008]
Si structure of a column on
4 bridges
PiezoResistive
Bulk Micro
Machining
3
Shan et al. [2005]
Column on a rectangular
Si plate
PiezoResistive
Bulk Micro
Machining
3
Tholey & Desai [2007]
Strain Gauges installed on
the outer part of a laparoscopic tool
PiezoResistive
Integration
with
adhesives
3
Polygerinos et al. [2010]
Tube like flexible structure
Optical
Machining
3
Puangmali et al. [2011]
Polycarbonate tube structure with a rolling sphere
probe
Optical
Machining
3
Table 3.1: Comparison of multi-component miniaturized force sensors
37
3.1. I NTRODUCTION
Size
[mm]
F/M range
Accuracy
(F→mN; M→mN·m)a (F→N; M→N·m)
Characterization
method
4 × 4× 20.9i
X, Y = 3 · 10−3
X, Y, Z = 10−3
X,Y, Z stage
6.5 × 6.5 × 0.25
X, Y = 0.7,
Z = 2.7k
X, Y, Z = 0.025
Load cell
9 × 9 × 0.5
X, Y = 3 · 10−3
X, Y, Z = 0.05
X, Y, Z stage
4×4×1
X = 29,
Y = 20.7,
Z = 21
Fz = 319,
Mx = 6.53 · 10−3 ,
My = 9.8 · 10−3 j
X, Y, Z = 0.2
Force gauge
palpation
Fz = 0.5(1),
Mx/y = 0.125 · 10−3
X, Y, Z stage
2 × 2 × 0.5
5×5×2
X, Y = 5−100,
Z = 12.5−250f
X, Y = 0.1−2,
Z = 0.25−5f
Specifically
designed setup
2.3 × 2.3 × 1.3
X, Y = 7,
Z = 10
X, Y = 0.5−0.7,
Z=3
X, Y, Z test bench
with Nano17
4.5 × 4.5 × 7g
Z = 0.44g
Z = 1.16 ± 0.12
X-Y table on a
Z stage, using a
vacuum chuck
10 × 10 × 6.25h
X, Y = 0.16,
Z = 0.23
X, Y = 1,
Z = 2.7
10 × 10 × 3
X = 900,
Y = 914,
Z = 152e
X, Y = 500
X, Y, Z = 2
X-Y table on a
Z stage
X, Y, Z = 13
Specifically
designed setup
X, Y, Z = 0.5
Mounting on
Nano17
Loading masses
on the sensor
8 × 20
4 × 10
5 × 20
X, Y = 4,
Z=8
X, Y, Z = 20
X, Y = 1.5,
Z=3
38
a
3. F ORCE S ENSORS
Device
Description
Sensing
Principle
Fabrication
Technology
# of
axes
Peirs et al. [2004]
Ti6Al4V alloy tube
Optical
Machining
3
Tokuno et al. [2008]
Two orthogonal frames
from PEEK450GF
Optical
Machining
2
Tan et al. [2011]
Cubic Delrin structure
made of 3 orthogonal
frames
Optical
EDM and
Machining
3
Ohka et al. [2005]
Si Rubber 10 × 12 array
Optical
EDM mold,
Si
rubber
casting
3
Beyeler et al. [2009]
Si comb drive
Capacitive Bulk Micro
Machining
6
Lee et al. [2008]
4 capacitors embedded in
PDMS
Capacitive Bulk Micro
Machining
3
Seibold et al. [2005]
6 DoF Stewart Platform
Current
generator,
Magnetic
6
Precision
Engineering
X and Y are the in plane shear forces respectively and Z is the normal force direction.
Estimated from the Figures provided in the paper because in the reference the authors did not provide the data.
The overall size includes a 3mm long probe which is not essential for the function of the device.
d
The values in parenthesis are the design values, the study reports only on application of 2.5N experimentally.
e
The accuracy was calculated according to the asymmetry of the cross-talk reported by the authors.
f
The data was retrieved from commercial publication of the authors spinoff company Tactologic.
g
The height is determined by a 7mm long probe pin. The resolution is estimated from the minimal
displacement given in Levey et al. [2007].
h
The height is determined by a 6.25mm long probe.
i
The sensor has a 20.4 mm long tactile element and the sensor itself is 0.5mm high.
j
The accuracy was calculated according to the asymmetry of the cross-talk reported by the authors.
k
The accuracy was calculated according to the experimental sensitivity measurements? uncertainty.
l
The accuracy was determined according to friction force F = ±0.7N that is expressed as crosstalk in the
experiments.
b
c
39
3.1. I NTRODUCTION
Size
(mm)
Accuracy
(F→mN; M→mNm)
F/M range
(F→N; M→Nm)
Characterization
method
5 × 4 × 8.85
X, Y, Z = 40
25 × 11
X, Y = 48
X, Y = 1.7,
Z = 2.5
X, Y = 3
48.3 × 49.5 × 50.8
X, Y, Z = 140l
X, Y, Z = 6
Specifically
designed setup
29E12A-I25
force sensor
with X-Z stage
X-Z stage
6 × 7.2 × 0.4
X, Y = 1.85,
Z = 0.5b
X, Y = 10,
Z = 10b
X-Z stage with
an optical setup
10 × 9 × 0.5c
Fx,y,z = 1.4 · 10−3 ,
Mx,y,z = 3.6 · 10−6
Fx,y,z = 1 · 10−3 ,
Mx,y,z = 2.6 · 10−6
2 × 2 × 1.212
X = 0.25,
Y = 0.29,
Z = 0.3
Fx,y = 50,
Fz = 250,
Mx,y,z =?
X, Y, Z = 0.01
Palpation with
a force gauge
Fx,y,z = 2.5(30),
Mx,y = (300),
Mz = (150)d
Specifically
designed setup
8.4 × 3.2
40
3. F ORCE S ENSORS
Other sensors feature strain gauges assembled on e.g. metal structures. Seibold
et al. [2005] suggested a miniature Stewart platform with 6 DoF installed at the
tip of a minimally invasive surgical tool. The sensor’s full range and accuracy
would make it suitable for MIS applications, however its size of 8.4mm ×
3.2mm does not allow for size critical applications, e.g. intravascular surgery.
Tholey & Desai [2007] installed strain gauges on a laparoscopic grasper in
order to get multi-axial force feedback. The gauges do not increase the tool’s
diameter significantly, however, determining the applied forces at the tip of
the gasper precisely might be problematic because of the distance between the
strain gauges and the tooltip.
There are several studies that utilize optical sensing principle based on light
intensity or interferometry. These sensors are fabricated typically by standard
precision or electrical discharge machining (EDM) and require complex light
emitting and photo sensing electronics. Tokuno et al. [2008] proposed a 2
DoF optical sensor with parallel plate structure composed of nonmetallic parts,
being therefore compatible with magnetic resonance imaging (MRI). It has a
diameter of 25mm, which makes it too big for MIS applications. Polygerinos
et al. [2010] developed a 7F MRI-compatible catheter head with a fiber optic
force sensor embedded in it. They used a deformable monolithic structure
made of silicon rubber to modulate light intensity. Peirs et al. [2004] presented
a tri-axial optical force sensor for MIRS, based on a flexible Titanium structure
and three optical fibers. Similarly to Tholey & Desai [2007], they integrated
their sensor in a laparoscopic gripper, far away from the interaction point.
Force sensors with the highest measurement range for robotics and MIRS
can sense up to 5 − 30N . The commercial 6 DoF force/torque sensor of
ATI Industrial Automation known as Nano17 having a size of 17mm and
height of 14.5mm also belongs to this category. Ohka et al. [2005] suggested a high range micro-optical 3 DoF tactile array featuring 10 × 12 sensing cells. Another high range fiber-optic MRI compatible force sensor was
introduced by Tan et al. [2011]. The proposed design reduces the cross coupling among the force components at the cost of a complex and big structure
(48.3mm × 49.5mm × 50.8mm).
Capacitive force sensors are best suited for biomechanical measurement since
they are associated with low force range and high accuracy. A 6 DoF capacitive
force-torque sensor was introduced by Beyeler et al. [2009]. The resolution of
the MEMS structure is in the range of a µN and nN m, accordingly. Lee et al.
41
3.2. M OTIVATION
[2008] presented a 8 × 8 array of capacitive sensors. Each unit consists of 2 × 2
electrodes embedded in polydimethylsiloxane (PDMS).
3.2
Motivation
Although there are several 3D force sensors developed for MIS, we found that
there is still a need for solutions that meet the size limitations, cover the critical
force range and can be operated in surgical environment. Despite the tendency
of making the MEMS sensors smaller, the packaged sensors do not differ much
in size from other sensors. We concerned ourselves with the development and
evaluation of robust, miniature tri-axial force sensors based on strain gauges
mounted on precision machined metal structures. Our aim was to create sensors providing force data that are comparable to the commercially available
ones in terms of full scale and resolution (see Table 3.2) with a smaller size.
Peirs et al. [2004] performed in vivo experiments and showed that for MIS the
needed force range is from 0 to 2.5N with 10mN resolution. We have developed two sensors, one based on the Euler-Bernoulli beam model that is easy to
manufacture, and another one what we call ’Butterfly’. The latter one features
improved isotropy at the cost of a more complex structure. Both concepts make
it possible to further decrease the size without affecting the performance of the
sensors.
Full Scale
Resolution
Bandwidth
Size
Degrees of Freedom
ATI Nano 17
12N
3.125mN
> 1kHz
14.5 × 17mm
6
Our sensors
2N
5mN
> 1kHz
3 − 4mm
3
Table 3.2: Sensor specification.
Currently the sterilization of the sensors is not addressed. As the strain gauges
might get damaged in case of being exposed to higher temperatures, chemical
sterilization would be advantageous. One possible solution for sterilization is
to isolate the sensing elements by encapsulating them.
42
3.3
3.3.0.1
3. F ORCE S ENSORS
Sensor Concept
Mechanical Design
We use solid metal structures to convert the applied force to mechanical strains
to be detected by piezoresistive sensors. One of the advantages of this concept is the monolithic metal body, which is capable of enduring much higher
forces than those corresponding to the measurable strain range. In this respect,
the force range is restricted by the strain sensing technology. In contrary to
previously developed sensors (Vasarhelyi et al. [2006], Lee et al. [2008]), our
design requires no damping. Such polymer embedding would introduce hysteresis and loss in sensitivity and linearity. The sensors are manufactured using
a combination of conventional mechanical precision and electrical discharge
machining (EDM) technologies.
In order to determine the correspondence between the raw bridge signals and
the force vector, we needed a coherent force model. The 3D force data can
be characterized either by three Cartesian force components (FX , FY and FZ
designated also as shear x, shear y and normal force, respectively) or by the
magnitude and exact orientation of the load force. Our intent is to integrate the
force sensors into medical devices as close to the interaction point as possible.
Hence, the location and size of the sensors enable modeling the contact interaction of the tool with the tissue as a point force acting on the axes of the sensor.
This assumption allows us to neglect the torque applied on the sensor. We have
introduced a model in which the sensor tip is the origin and all the interacting
forces have radial direction. The force is characterized by its magnitude, shear
angle (φ) and angle of incidence (θ) as shown in Fig. 3.2.
The dependency on the ambient temperature has to be taken into account, as
the sensors can be exposed to temperature fluctuation during interventions (e.g.
tissue ablation). In order to minimize the influence of temperature variation we
used Ti6Al4V alloy for the sensor bodies with fairly low thermal expansion
coefficient, 8.6 × 10−6 /◦ C. Moreover, this material is advantageous in magnetic environments, biocompatible and widely used in biomedical devices (for
example the Nano17 titanium sensor made by ATI Inc.). The Ti6Al4V cast
alloy features 758MPa yield strength.
43
3.3. S ENSOR C ONCEPT
Figure 3.2: Physical model of the measurements: the applied force is defined
by its shear angle φ, angle of incidence θ and force magnitude.
3.3.0.2
Electrical Design
Semiconductor strain gauges manufactured by Micron Instruments Ltd. (California, USA) are used to convert the mechanical strain to electrically measurable signal through resistance change due to piezoresistive effect. We chose
this type of gauges because of their high gauge factor, small size and high linearity over a wide strain range. The gauges are covered by a thin polymer layer
for mechanical protection and electrical insulation. The influence of this layer
on the sensor’s performance is negligible. The measured change in resistance
is proportional to average strain over the surface that is covered by the gauge.
Knowing the nominal resistance of the strain gauge R, the strain ∆L/L and
the gauge factor GF , the difference in resistance is:
∆R = R
∆L
GF.
L
(3.1)
The strain gauges form full- and half-Wheatstone bridges with the active sensing resistors located on the surface exposed to strain. In addition to the higher
strain sensitivity than of the single elements, the bridge connection is associated with reduced temperature dependence. The bridges are thermally compensated by connecting typically high value resistors in parallel to either of
the strain gauges as shown in Fig. 3.3. The high value of the shunt resistances
ensures that they do not affect the linearity of the bridge significantly.
44
3. F ORCE S ENSORS
(a)
(b)
Figure 3.3: Full- 3.3(a) and half-Wheatstone 3.3(b) bridges with compressed
(C) and tensed (T ) strain gauges and the fixed value completion resistors (RC ).
Temperature compensation is carried out by the shunt resistor (RT ), which
typically has a high value.
The wiring of sensors can pose a manufacturing obstacle and also a potential failure-point under daily operation. Unless, additional benefit is provided,
fewer wires would thus be preferential. In the sensor setup above, each half
bridge needs three wires, two for the excitation and one for the output, whereas
a full bridge requires four connections. All the bridges are driven by 5V DC
voltage, thus the common bridge excitation reduces the number of wires required.
To characterize the temperature variation of the bridges, recordings were made
while heating a gauged sensor from 25 ◦ C to 45 ◦ C. Fig. 3.4 demonstrates
the bridge output as a function of temperature variation. One can see that the
curves show good linearity, thus further compensation can be carried out by
measuring the temperature of the sensor body.
The strain range has been chosen to be low so the bridge outputs show good
linearity. Assuming perfectly matched gauges and neglecting the high value
shunt resistance, the relationship between the input and output voltages of the
bridge is:
RC
RC
UOU T = UBR
−
,
(3.2)
RC + R − ν∆R RC + R + ∆R
where UBR is the excitation voltage, RC is the completion resistor and ν is
the Poisson’s ratio, which is the relation between transverse and contraction
3.3. S ENSOR C ONCEPT
45
Figure 3.4: Temperature response of the outputs of a half-bridge while heating
the sensor body from 25 ◦ C to 45 ◦ C.
strain. In our setup, within the ±150 × 10−6 strain range the bridge outputs
show integral nonlinearity error of 0.625% full-scale.
A piezoresistive strain gauge bridge with the parameters above produces an
output voltage typically in the range of 10mV , so further signal conditioning is
needed before data processing. A custom-design circuit developed by us is responsible for signal conditioning, data acquisition and communication with the
host PC (see Fig. 3.5). Every bridge output is connected to a separate channel,
the analog input stage of each channel is an AD8221 precision instrumentation
amplifier, which is characterized by high common-mode rejection ratio and adjustable gain. In order to fit the dynamics of the AD channels, depending on the
location, architecture (full- or half-bridge) and the covered strain range of the
bridges, different gains have been applied to them. No analog filter is used in
the system. The amplified signal is directly sent to the AT90USB1287 (Atmel
Corp., California, USA) microcontroller’s integrated AD channels, where the
data conversion takes place at 10 bits. At the AD inputs the peak-to-peak noise
is 7mV . The resolution of the AD stages is 4.88mV at 5V reference voltage.
The maximal obtainable refresh rate for all the channels is over 1kHz. The
noise is suppressed by digital filtering of the oversampled signal. The acquired
data are sent to the computer via an RS232 serial port. We use a LabVIEW
virtual instrument for receiving, visualizing, processing and storing the data.
46
3. F ORCE S ENSORS
In the current design, the force components are computed by the LabVIEW
module, however, our custom design circuit is also capable of doing that. The
main advantage of performing the data processing on the microcontroller unit
lies in the system’s flexibility. By implementing the processing on the custom
design circuit it is possible to integrate the sensor in a control loop without the
need for a computer.
Figure 3.5: Block diagram of the system. The half bridges are extended on
separate PCBs, the bridge outputs are connected to precision instrumentation
amplifiers. The conditioned signals are converted and processed by the microcontroller. The processed data are sent to the PC via an RS-232 serial port.
3.4
3.4.1
Euler-Bernoulli Sensor
Sensing Principle
The Euler-Bernoulli sensor design is based on a hollow beam that converts
the applied force to mechanical strain. The sensing element can be modeled
as an Euler-Bernoulli beam. The interacting forces create bending in the shear
directions and axial displacement (compression) in the normal direction. Using
the assumptions of the Euler-Bernoulli beam theory (Gere & Goodno [2009]),
47
3.4. E ULER -B ERNOULLI S ENSOR
we find the theoretical sensitivity of the sensor. The sensitivity due to bending
is
GF 48(Lsgi − 2L + Lsgf )H
Sx(t) =
Fx ,
(3.3)
(16BH 3 − 3πDi4 )Y
and
Sy(t) =
GF 48(Lsgi − 2L + Lsgf )B
Fy .
(16HB 3 − 3πDi4 )Y
(a)
(b)
(3.4)
(c)
Figure 3.6: Mechanical drawing of the force sensor (a). Image of the monolithic Ti sensor beam (b) and the gauged force sensor (c).
The sensitivity due to the application of an axial force is:
N (t) =
GF
Fz ,
(BH − πDi2 /4)Y
(3.5)
where Lsgi is the lowest point of the strain gauge as can be seen in Fig. 3.6(a),
L is the length of the beam, Lsgf is the highest point of the strain gauge, B is
the width of the beam in x direction, H is the width of the beam in y direction,
Di is the diameter of the inner hole, GF is the gauge factor and Y is the Young
modulus of elasticity.
48
3. F ORCE S ENSORS
(t)
(t)
The ratio between Sx (or Sy assuming a square beam cross section) and
N (t) indicates the anisotropic nature of the sensitivity because of the beam like
structure:
(t)
r=
12H(4H 2 − πDi2 /4)(Lsgi − 2L + Lsgf )
Sx
=
.
16H 4 − 3πDi4
N (t)
(3.6)
For example, for the current design (L = 4.4mm; Lsgi = 1.7mm; Lsgf =
1.9mm; H = B = 1mm; Di = 0.8mm; Y = 113.8GP a) the sensitivity in
the X and Z directions is:
i h
(t)
(3.7)
Sx , N (t) = 180, 17.67 ⇒ r = 10.2.
Omitting the inner through hole, Di = 0 will result in:
i h
(t)
Sx , N (t) = 137, 8.8 ⇒ r = 15.6.
(3.8)
Consequently, in addition to system integration advantages (channel leading
to parts in front of the force sensor) the through hole increases the absolute
sensitivity and its isotropy in the sensor.
3.4.2
Sensor Design
In order to make the calibration easier, a monolithic structure has been designed
that consists of a base, the sensing beam itself and a threaded tip which serves
for fixing purposes (see Fig. 3.6(b)). The beam has the size of 1mm × 1mm ×
4.4mm and there is a concentric 0.8mm hole along its symmetry axis. FEM
analysis showed that the sensor body can endure 10N load. The force range is
scalable by modifying the geometrical parameters. We used SS-027-013-500P
strain gauges, which have a resistance of 540 ± 50Ω and a gauge factor of
155 ± 10. The thermal coefficient of the gauge factor is −0.3241/◦ C, whereas
of the resistance is 0.4321/◦ C at room temperature.
The shear forces (Fx and Fy ) are measured by two half-Wheatstone bridges
(Sx and Sy respectively) which are completed externally by additional resistors
to form full bridges. Since the bending moment increases linearly from the tip
to the base, the strain gauges are exposed to uneven strain distribution. In
practice this means that the strain readings are averages over the surface that
3.4. E ULER -B ERNOULLI S ENSOR
49
is covered by the gauge. The strain gauges are placed close to the base, where
the bending strain is maximal, as depicted in Fig. 3.7. Both pairs of gauges are
assembled on the sensing part in a way that the corresponding gauges are put
on opposite sides of the beam. Therefore, the two shear base directions can
be determined. Each half bridge contains two vertically fixed gauges which
ensures that axial displacement has no effect on the shear force measurement.
Figure 3.7: Strain gauges on the Euler-Bernoulli beam. The normal force
component is measured by a full-Wheatstone bridge (N ), whereas the shear
half bridges (Sx and Sy ) are completed externally.
The normal force Fz is converted into axial displacement and measured by a
full bridge, N , which is positioned at the half height of the beam. The bridge
consists of 4 strain gauges, two of them are in horizontal whereas the other
two are in vertical orientation. We are interested in measuring the compressive
strain, but experimental results revealed that this structure shows sensitivity in
case of bending as well. We corrected this crosstalk through signal processing
as detailed later.
3.4.3
Calibration
In order to gain actual 3D force data, the sensor had to be calibrated. An external force was applied on our sensor in various directions and we compared
the readout of the 3 bridges to the force measured by the ATI Nano17. Three
independent degrees of freedom have been selected in order to implement the
aforementioned calibration model: the shear angle φ, the angle of incidence θ
and the translation in radial direction. The latter one is a 1 DoF movement and
50
3. F ORCE S ENSORS
the applied force is measured by a standard force sensor that is in interaction
with our sensor. This way, in a given solid angle domain, arbitrary shear angle
and angle of incidence combinations [θ, φ] can be set up where the interacting
force’s magnitude is also known. After making recordings from defined directions, one can find the relationship between the sensor’s recorded data and the
given angular setup.
3.4.3.1
Calibration Setup
Figure 3.8: Calibrating mechanism presenting 4 degrees of freedom: two
rotational and two translational. The red arrow corresponds to the angle of
incidence (θ), the yellow to the shear angle (φ) and the blue to the sliding
movement. There is a fourth translational DoF (green) which ensures that the
interaction point is on the Nano17’s symmetry axis.
We have built a calibrating setup so that the necessary measurements can be
done in a repeatable and precise manner, see Fig. 3.8. The design consists of
aluminium parts in order to provide a rigid structure which serves as a frame
for our experiments. One rotational degree of freedom is implemented by a
disc on which the sensor is assembled. By rotating this element the shear angle
3.4. E ULER -B ERNOULLI S ENSOR
51
can be set to the desired value. The angle of incidence is adjustable by tilting
the base of the disc. The third, translational degree of freedom is implemented
by a slide. There is an ATI Nano17 6 DoF force sensor assembled on the
moving part so the interacting force is always directed toward it. As the slide
with the Nano17 has only one DoF and the interaction point is intersected by
the Nano17’s length axis no shear forces or torques occur. For this reason, the
normal force component of the ATI sensor is identical to the absolute force
that is applied on our sensor’s tip. In order to achieve isotropic load we fixed a
hemisphere shaped metal cap on the thread. In our experience the cap does not
introduce additional stress on the column. Even in the absence of the cap, the
torque is not significant owing to the short sensing beam.
3.4.3.2
Calibration Results
The following analysis investigates the possibilities of assessing the applied
3D force based on the sensor’s data. The primary goal of this step is to reveal
the cross-dependencies in our sensor’s raw data and to find a model which
makes it possible to calculate the actual forces with satisfactory accuracy. All
the experiments were performed using the aforementioned calibration device.
Fig. 3.9 shows the bridge outputs in a fixed orientation as functions of the
applied force. One can see that 1 DoF force interaction results in linear force
trajectory. Therefore, recording the outputs with the same constant load we can
determine the sensitivity in a given direction. The initial experiments showed
that there is no observable drift of the signals. The peak-to-peak noise of 15mV
is canceled by digital filtering of the oversampled signals.
We use the slide’s own weight of 2.62N as a constant load for the calibration
experiments. Continuous recordings were made at different shear directions
while θ ranges from 0◦ to 30◦ in 5◦ steps measured from the sensor’s orthogonal direction, as shown in Fig. 3.10. Taking the average of the values measured
at constant load we got static points in the parameter space of the raw signals.
One can see in Fig. 3.11 that at different shear angle setups the recorded points
at the orthogonal direction deviate from the origin (Sx , Sy = 0).
Since these measurements were all made in the θ = 0◦ direction with a constant load, the erroneously observed shear components around the origin are
probably caused by the calibration mechanics mostly due to the slide’s imperfection. As 3D force recording is extremely sensitive to any kind of deviation
from the ideal conditions, we regard this phenomenon unavoidable.
52
3. F ORCE S ENSORS
Figure 3.9: Typical force trajectories of the amplified bridge signals. Constant
force direction results in linear curves.
Figure 3.10: Bridge sensitivities as a function of the angle of incidence. The
measurements presented are: X shear force (blue circle); Y shear force (red
square) and Z axial force (black triangle).
3.4. E ULER -B ERNOULLI S ENSOR
53
Figure 3.11: Recorded points in the shear plane. The measurements were
made in 8 shear directions with the angle of incidence ranging from 0◦ to 30◦
with 5◦ steps (black circles). One can see that the closer the points are to the
normal direction the more significant the shear angle error is.
Figure 3.12: Illustration of the cross dependency between the output signals
of the bridges. The sensitivity of the normal bridge varies in different shear
directions.
54
3. F ORCE S ENSORS
Fig. 3.12 demonstrates the angular dependency of the measured vector’s magnitude. It can be seen that the signal vector’s magnitude is heavily dependent
on its orientation. One would expect the recorded points to be on a spherical
surface in the variable space [Sx , Sy , N ] as the load is constant and only the
direction varies. Unlike the standard linear least squares (LLSE) method, our
approach takes this effect into consideration. In order to take into account the
high certainty of signals Sx and Sy compared to N (caused by the sensitivity difference (Eq. 3.6)) we converted the Cartesian coordinates into spherical
ones:
Sy
φ = tan−1
,
(3.9)
Sx


N
,
(3.10)
θ = tan−1  q
2
Sx + Sy2
q
R = Sx2 + Sy2 + N 2 .
(3.11)
This step is essential in order to exploit the higher accuracy of Sx and Sy due
the higher sensitivity of the sensor in the bending direction. In the original parameter space the magnitude sensitivity depends on three parameters, whereas
after the transformation it is only dependent on the angular components. As
shown in Fig. 3.13, the recorded static points span a surface that can be approximated by a 4th order, two variable polynomial:
PR (θ, φ) =
4 X
4
X
aij θi φj .
(3.12)
i=1 j=1
Therefore, if the calculated angle of incidence and shear angle are known, the
force magnitude can be determined:
R̃ =
R
FN ano17 ,
PR (θ, φ)
(3.13)
55
3.4. E ULER -B ERNOULLI S ENSOR
Figure 3.13: Recorded points and the estimated surface as a function of the
calculated shear angle φ and angle of incidence θ. The force scaling factor
represents the ratio between the calculated magnitude R and the actual force in
a given direction.
where FN ano17 is the force measured by the Nano17 sensor. The angle of
incidence which is obtained from the measurements, can be calculated in the
same way:
4
4 X
X
θ̃(θ, φ) =
bij θi φj .
(3.14)
i=1 j=1
Knowing the calculated shear angle (Eq. 3.9), the estimated force magnitude
(Eq. 3.13) and the angle of incidence (Eq. 3.14), the 3D force vector can be
deduced using a spherical to Cartesian coordinate transformation:
Fx = R̃ sin θ̃ cos φ,
(3.15)
Fy = R̃ sin θ̃ sin φ,
(3.16)
Fz = R̃ cos θ̃.
(3.17)
The calibrated data are in accordance with the theoretical values as demonstrated in Fig. 3.14. The angle of incidence and the shear angle both can be
determined with ±1◦ accuracy and the force magnitude is within the ±4%
range as shown in Fig. 3.15.
56
3. F ORCE S ENSORS
Figure 3.14: Illustration of the recorded points in the actual force space relative
to the calibrating load (2.62N ). The red crosses are the target values and the
black circles are the measured ones, respectively.
Figure 3.15: Recorded points in 4 shear directions relative to the calibrating
load. As each measurement was carried out by applying constant load, the
measured points are expected to be on the arc. The radial lines represent the
angle of incidence in 5 steps.
57
3.5. B UTTERFLY S ENSOR
3.5
Butterfly Sensor
3.5.1
Sensing Principle
The sensing part of the Butterfly sensor consists of four bending arcs that ideally have a circular profile with discontinuity, as shown in Fig. 3.16(a). Such
a structure is capable of orthogonal decomposition of the force vector. However, the used strain sensing technology (semiconductor strain gauges) requires
plain surfaces because the gauges cannot be mounted on a high curvature surface. For this reason, each arc has a 1mm long straight section that serves as
a base for the semiconductor strain gauges, as demonstrated in Fig. 3.16(b).
By modifying the thickness of the arcs, different force range values can be
implemented.
(a)
(b)
Figure 3.16: Force sensor designs with circular (a) and ’C’ shaped (b) basic
sensing elements. The plain part of the latter structure makes it possible to
assemble strain gauges on the sensor. All dimensions are given in mm.
The force vector applied on the sensor can be decomposed into three components, as depicted in Fig. 3.17. The reaction of the sensor is the superposition
of the bending due the three force components: normal force Fn and two shear
forces Fsx and Fsy . The four arc design ensures that the cross-coupling between the two shear directions is minimal.
58
3. F ORCE S ENSORS
Figure 3.17: Illustration of the force components applied on the Butterfly sensor.
(a)
(b)
Figure 3.18: Illustration of the FEA results in case of normal (a) and shear (b)
force component applied on the Butterfy sensor.
59
3.5. B UTTERFLY S ENSOR
Fig. 3.18(a) shows the Finite Element Analysis (FEA) results of a pure normal
force applied on the sensor. One can observe that the bending of the four arcs
is symmetric, therefore we model the bending moment applied on each arc as
follows:
a
Mn = − Fn ,
(3.18)
4
where a is the distance from the symmetry axis of the sensor to the neutral axis
of the beam (see Fig. 3.17). We used the Euler-Bernoulli beam assumption in
order to calculate the strain caused by on the straight part of the arc. The strain
in arcs 1 − 4 is:
(n)
ε1/2/3/4 =
3aFn
aFn h
=
.
4EI 2
2Ebh2
(3.19)
Here E is the Young modulus of elasticity, I is the cross section inertia, b is
the width of an arc and h is the thickness of an arc.
Due to the symmetry of the sensor, the bending reactions to Fsx and Fsy are
the same. Therefore, we present here only the results for Fsx . Fig. 3.18(b)
demonstrates the FEA simulation of the sensor’s reaction to a pure shear force.
We can observe that the displacements of the four beams are identical. In order
to have the same curvature in the beams 2 and 4, the force applied on them has
(sx)
(sx)
= F4 . The forces applied on the other two arcs are
to be the same, F2
also the same, although not equal to the force applied on the former arcs. We
compare the two curvatures assuming again Euler-Bernoulli beam conditions:
(sx)
(c + x)F1
EI1
(sx)
F1
(sx)
=
(c + x)F2
EI2
(sx) I1
= F2
,
(3.20)
.
(3.21)
) = Fsx .
(3.22)
I2
On the other hand the force equilibrium is
(sx)
2(F1
(sx)
+ F2
From 3.20 and 3.21 we derive the bending force applied on each arc:
(sx)
F2/4 =
I2
h2
Fsx ,
Fsx =
2
2(I1 + I2 )
2(b + h2 )
(3.23)
60
3. F ORCE S ENSORS
(sx)
F1/3 =
I1
b2
Fsx =
Fsx .
2
2(I1 + I2 )
2(b + h2 )
(3.24)
We can see that the load on each arc depends on the ratio between its width b
and height h. For a square cross section the load will be equal to Fsx /4. The
bending strains at the outer face of the arcs 2 and 4 and the right face of arcs 1
and 3 due to the shear force are
(sx)
ε2/4 = ±
(sx)
ε1/3 = ±
3(c + x) F (sx)
,
(b2 + h2 )b E
(3.25)
3(c + x) F (sx)
.
(b2 + h2 )h E
(3.26)
We observe that the face of arc 4 is in tension and the face of arc 2 is com(sx)
pressed, as illustrated in Fig. 3.18(b). The ε1/3 are the maximal strains in the
arcs 1 and 3 although they are less important because we do not place the strain
gauges there. By using superposition and averaging the strains above we can
find the sensor’s theoretical sensitivity:
B = SF
B = B1 B2 B3 B4


0
Ss Sn
−Ss
0
Sn 

S=
 0
−Ss Sn 
Ss
0
Sn
T
F = Fsx Fsy Fn
3Ga2
2Ebh
3G(2(c + d) + lsg )
Ss =
2(b2 + h2 )bE
Sn =
(3.27)
(3.28)
(3.29)
(3.30)
(3.31)
(3.32)
Here G is the gauge factor of the strain gauge, d represents the distance of the
strain gauge from the top of the arc and lsg is the length of the strain gauge.
The gauges are situated at the middle point of the arc.
61
3.5. B UTTERFLY S ENSOR
3.5.2
Sensor Design
The shape of the Butterfy sensor resembles of the wings of a butterfly, therefore we name it accordingly. The sensor is a new concept to the best of our
knowledge. It enables precise 3 DoF force measurement in applications that
have strict size limitations such as at catheter tips to be used in interventional
radiology. The base structure consists of two beams and four bending arcs connecting them symmetrically, as depicted in Fig. 3.19. Although a tri-arc sensor
suffice in acquiring 3D force data, the two arc pair design simplifies signal processing of the sensor. In order to ensure that the applied force has significant
effect only on the bending arcs, the sensor’s base and tip are much stiffer than
the middle section. As a consequence, the sensor’s size is determined by the
sensing part, whereas the base and tip serve a mounting purpose. The outer
diameter of our first prototype is 2.6mm, its minimal length (height of the
arcs) is 2mm and the current embodiment’s total length is 7mm. However, the
structure is scalable in terms of size and force range and with the current manufacturing method we estimate that it can be reduced down to 1.5 × 6mm.
Owing to the polymer coating the circumferential diameter of the prototype is
3mm. Isotropic sensitivity is reached by converting normal forces to bending instead of contraction. Due to the simple design only a couple of steps are
needed to shape the structure, as demonstrated in Fig. 3.19.
(a)
(b)
(c)
(d)
(e)
(f)
Figure 3.19: Illustration of the manufacturing of the sensor body: forming a
cylindrical beam (a), structuring the cross section (b), shaping the wings in the
shear directions (c) and (d), removing excess material from the middle (e) and
eventually shaping the tip (f).
62
3. F ORCE S ENSORS
Figure 3.20: Strain gauges on the sensor. The vertically and horizontally
placed gauges form a half Wheatstone bridge. The symmetric setup ensures
that the bridge has zero output in case of symmetric strain profile.
We chose the SS-018-011-3000PU strain gauge of Micron Instruments Ltd.
The selected gauge has 3000 ± 300Ω nominal resistance and gauge factor of
155 ± 10. The thermal coefficient of the gauge factor is −0.324%/◦ C, of the
resistance is 0.774%/◦ C at room temperature.
A half Wheatstone bridge requires two strain gauges, one with positive and
one with negative change of resistance. A common way of ensuring this is to
put two gauges to the opposite sides of the bending element (Pallas-Areny &
Webster [2001]). One of them is compressed under load whereas the other one
is tensed. In our case assembling the gauges on the inner side of the arcs would
have been cumbersome. Hence, we decided to put both gauges on the outer
side of the arc, one of them vertically, the other one horizontally oriented. The
gauging concept is shown in Fig. 3.20.
63
3.5. B UTTERFLY S ENSOR
(a)
(b)
(c)
Figure 3.21: FEA results for the basic sensing element. Compressive (a) and
tensile (b) load at the gap causes uniform tensile and compressive stress, respectively. Shear stress (c) results in symmetric strain profile that is not to be
measured by the half Wheatstone bridge.
Fig. 3.21 demonstrates the FEA results for the basic sensing element, the ’C’
shaped arc. Taking a closer look at the arc’s strain profile one can see that the
gauges are exposed to uneven strain distribution. In order to avoid crosstalk we
paid special attention to the symmetric placement of the bridges. Therefore,
in case of shear load the bridge output is expected to be close to zero (see
Fig. 3.21(c)).
The breaking load of the structure has been estimated by a commercial FEA
simulator (Autodesk Inventor). According to the simulation results it corresponds to 7.08N and 54.07N maximal load in the shear and normal direction,
respectively. Our design is based on a maximal load of 5N that can be applied
from any direction on the butterfly sensor. The gauges can endure a maximal
strain of ±3000 × 10−6 . Such high strain values occur only beyond the force
range specified above, 21.16N in the shear and 75N in the normal direction.
The four trenches of the cross section (see Fig. 3.17) provide enough space
for commercially available insulated small diameter wires (0.15mm), so the
connection of the gauges could be carried out without contributing to the overall diameter of the sensor or introducing extra rigidity to a flexible sensorized
tool.
64
3.5.3
3. F ORCE S ENSORS
Calibration
The aim of the calibration is to find the linear matrix transform C(3 × 4) between the strain gauge bridge signals B = [B1, B2, B3, B4] and the threecomponent force vector F = [FX , FY , FZ ] applied to the sensor:
 
 
B1
FX
B2 
 FY  = C   .
B3 
FZ
B4
(3.33)
The transform matrix C can be determined by evaluating the Moore-Penrose
least-squares error solution of the overdetermined set of equations:

B1,1
 B2,1

D= .
 ..
B1,2
B2,2
..
.
B1,3
B2,3
..
.

B1,4
B2,4 

..  ,
. 
B25,1
B25,2
B25,3
B25,4

F1,X
 F2,X

E= .
 ..
F1,Y
F2,Y
..
.

F1,Z
F2,Z 

..  ,
. 
F25,X
F25,Y
F25,Z
+
C = D E,
(3.34)
(3.35)
(3.36)
where D+ is the Moore-Penrose pseudo inverse of the bridge signal measurement matrix and E is the matrix of the corresponding force components. Overall, 25 calibration force vectors have been used as reference data for the calculations. The sensitivity of the bridges in a given direction is represented by
the slope of the force-bridge output trajectories. Three independent degrees of
freedom have been selected in order to make measurements in arbitrary directions: the shear angle φ, the angle of incidence θ and the translation in radial
direction. This way, in a given solid angle domain, any shear angle - angle
of incidence combination [θ, φ] can be set up. After making recordings from
defined directions one can find the relationship between the sensor’s recorded
data and the given angular setup.
3.5. B UTTERFLY S ENSOR
3.5.3.1
65
Calibration Setup
Figure 3.22: Calibrating mechanism presenting 3 degrees of freedom: two
rotational and one translational. θ corresponds to the angle of incidence, φ to
the shear angle and R to the sliding movement.
A calibrating setup has been developed so that the necessary measurements can
be taken in a repeatable and precise manner, as shown in Fig. 3.22. The structure is made of aluminium and it enables fixing the load at predefined shear
angle and angle of incidence and applying a load on the sensor along the linear
sliding joint. The mechanism’s design improves the reliability and repeatability of the applied force during the calibration process. The calibration setup is
spherical and it enables the adjustment of the angle around the yaw axis φ and
the angle of incidence θ. The sensor is positioned in a way that the calibration
structure’s yaw and pitch axes intersect each other at the base of the sensor.
The third, translational degree of freedom is implemented by a sliding bar. Our
66
3. F ORCE S ENSORS
aim is to measure the applied force and we use a commercial reference sensor
for the calibration. We chose the ATI Nano17 manufactured by ATI Industrial
Automation, Inc. (6 DoF force sensor, that has a resolution of 6.25mN in
both normal and shear directions), assembled on the tip of the bar. In order
to provide better access to the sensor, an additional poking tip was mounted
on the Nano17. The main axes of the sliding bar, the reference sensor and the
tip are concentric. It is important to emphasize that even though the Nano17
is capable of 6 DoF measurement, we want to determine the force component
only in the normal direction (we neglect friction that creates shear forces at
the contact between the tip and the sensor). Since the calibration mechanics
enable exclusively translational movement, the normal force component of the
reference sensor is identical to the absolute force that is applied on the sensor’s
tip. According to our sensor model hypothesis, this does not take the moments
into account. The calibration setup’s mechanical structure makes sure that no
torques occur thanks to the design.
3.5.3.2
Calibration Results
The calibration process enables the accurate measurement of the force vector.
We related the output voltage response of the strain gauges to the data of the
reference force sensor and the preset direction angles φ and θ. Measurements
have been made in 25 directions in order to obtain reliable data . The angle of
incidence ranged from 0◦ to 90◦ in 30◦ steps, whereas the shear angle varied
from 0◦ to 360◦ in 45◦ steps, covering a whole half-sphere. In each direction
the force was exerted by means of pressing the sliding bar with the Nano17 and
the tip against our sensor.
As the recorded data of the ATI reference sensor and our sensor were synchronized in time, we could evaluate the relationship between the bridge outputs
and the known force. The load on the sensor was applied by loading and releasing the contact force manually. The load force range was selected to fit
the sensitivity of the sensor considering the simulation results. The bridge output versus loading force trajectories were investigated in order to evaluate the
hysteresis and the linearity of the sensor. Fig. 3.23 demonstrates the absence
of hysteresis. The slope of the curves, that has been extracted using linear
regression, represents the sensitivity in a given direction. The coefficient of
determination was found to be close to one for all the cases, demonstrating
high linearity of the trajectories. Fig. 3.24 shows the average bridge outputs
67
3.5. B UTTERFLY S ENSOR
Figure 3.23: Typical bridge output versus reference force trajectory. The correlation coefficient of the curve is 0.9997, the integral nonlinearity is 2.42%
full-scale.
of measurements ranging from 0 to 2.5N in 0.1N steps and the bridge sensitivities extracted by linear regression of one measurement cycle. Even though
the calibration structure ensured that in a given orientation the only degree of
freedom is translation of the sliding bar, the manual guidance induced slight
wobble. However, in spite of the tremor the linear regression provides a reliable estimate of the bridges’ sensitivity.
The responsiveness of each bridge output can be characterized by the shear
angle φ and the angle of incidence θ. To demonstrate the sensitivity’s direction
dependence, a 3D parameter space has been defined in the following way: the
distance of the XY projection from the origin represents the angle of incidence
θ, the angle to the x axis is defined by the shear angle φ and z is the calculated
slope:
x = θ cos(φ),
(3.37)
y = θ sin(φ).
(3.38)
Fig. 3.25 presents the load orientation versus bridge output sensitivity in the introduced parameter space. For a better visualization the third order polynomial
estimation of the surface span by the responsiveness values is shown. Due to
68
3. F ORCE S ENSORS
Figure 3.24: Bridge outputs as functions of the reference force in the θ =
45◦ , φ = 20◦ direction. The black dots represent the average bridge outputs
of 10 measurements ranging from 0 to 2.5N in 0.1N steps, whereas the grey
lines stand for the sensitivities extracted by linear regression.
manufacturing and gauge alignment imperfections the bridges exhibit different
sensitivities. As a result of the symmetrical structure apart from a rotation the
4 bridge outputs are similar. The more detailed direction dependent sensitivity
of a bridge can be observed in Fig. 3.26. The maximal sensitivity of Bridge 1
is 32.38mV /N at θ = 62◦ , φ = 180◦ with reference to the gauge plane orientation. In the θ = 90◦ , φ = 90◦ and 270◦ directions the sensitivity is close
to zero which is in close agreement with our model and the preliminary FEA
results. The sensitivity matrix S(4 × 3), which is the Moore-Penrose pseudo
inverse of the transformation matrix C:
S = C+ ,

0.18
29.63
−29.43 −0.23
S=
 1.22
−22.6
27.74 −0.11
(3.39)

9.77
11.93
 mV .
10.42 N
10.2
(3.40)
One can see that S correlates well with the theoretical sensitivity, see Eq. 3.29.
The maximal sensitivity in the normal direction was found to be 11.93mV /N ,
3.5. B UTTERFLY S ENSOR
69
Figure 3.25: Output sensitivity [mV /N ] versus load orientation for the four
bridges. Due to the sensor’s symmetry, apart from the rotation the estimated
bridge sensitivities are similar.
whereas sensitivities for the shear x and y directions are 29.43mV /N and
29.63mV /N , respectively. Considering the gain of the instrumentation amplifier and the resolution of the A/D stage the shear resolution is 4.87mN and
the normal resolution is 12.07mN in the force range of 2.5N . As a final evaluation step we mounted the calibrated sensor on top of the Nano17 reference
sensor and made measurements in order to compare the signals. Fig. 3.27
shows the experimental setup, the results are presented in Fig. 3.28. The RMS
errors of the x, y and z force components were found to be 23mN , 22.6mN
and 22.7mN , respectively. It is important to emphasize that the misalignment
between the investigated sensor and the Nano17 also contributes to the error.
70
3. F ORCE S ENSORS
Figure 3.26: Output sensitivity [mV /N ] versus load orientation function of
the Bridge 1 (marked black). The grey circles in the XY plane represent the
30◦ , 60◦ and 90◦ angle of incidence. For a better visualization the sensitivities
gained from the calibration data are interconnected along the surface of the
third order polynomial estimation.
Figure 3.27: Experimental setup with the calibrated force sensor mounted on
the Nano17. Recordings have been made while a plane metal part was pressed
against the sensor in different directions.
3.5. B UTTERFLY S ENSOR
71
Figure 3.28: 3D Force recording in time domain. The red curve represents the
sensor data, whereas the blue one is the reference force.
72
3.5.4
3. F ORCE S ENSORS
Final Version
In order that the sensor can be integrated in minimally invasive surgical tools,
the latest version of the Butterfly sensor has a slightly modified shape. Both
the bottom and the top of the sensor has an M 2.5 thread, which facilitates
sensor assembly. There is a 0.6mm through hole along the sensor’s length
axis, which allows for tool wiring without contributing to the sensor’s overall
diameter. As demonstrated in Fig. 3.29, the bending arcs are made thinner
in order to achieve increased sensitivity. Thus, we achieved approximately 3
times higher sensitivity compared to the first Butterfly sensor. The sensitivity
matrix of the improved sensor is

13.08
86.42
−87.03 13.69
S=
 −9.73 −78.51
80.64
−8.52
(a)

27.99
30.43
 mV .
27.96 N
(3.41)
24.34
(b)
Figure 3.29: Technical drawing (a) and photo (b) of the latest generation Butterfy sensor.
3.6. C ONCLUSION
73
Despite the increased sensitivity, the strain gauges operate still in their linear
range. As a consequence of the thinner arcs, the breaking load has been reduced
to 2.58N and 17.19N in the shear and normal direction, respectively. With our
data acquisition circuit the resulting shear resolution is 1.74mN and the normal
resolution is 5.2mN .
3.6
Conclusion
We have developed novel piezoresistive tri-axial force sensors, which can be
manufactured by conventional fabricating technologies. Even though EDM
and the assembly of strain gauges are costly technologies the fabrication expenses can be decreased significantly by automation of the assembly and large
scale production. Our estimate is that the cost of the sensor could be reduced
to a couple of hundred US dollars (2013). Despite their miniature size, the
sensors’ measurement performance is comparable to large size, commercial
6 DoF sensors (e.g. ATI Nano 17). In comparison with other sensors that
employ the same principle, our sensor models are associated with remarkable
mechanical robustness. The Euler-Bernoulli sensor is easy to manufacture and
it has smaller diameter, however the Butterfly concept offers superior isotropy.
Therefore, we decided to use the latter one further on. The introduced calibration methods allowed achieving high angular and magnitudinal accuracy,
which makes it possible to use the sensors in any application, in which both
precision and small sensor size play a significant role. Hence, it is possible
to integrate the sensor in minimally invasive surgical instruments. Since the
main focus of this study was to develop and evaluate miniature tri-axial force
sensors that are capable of making measurements in a surgical environment,
certain aspects of the sensing performance were favored to others. Based on
the assumption that the 3D force data are sufficient to assess the surgical tool’s
orientation and the applied force, the torque data were of little interest to us.
However, at the cost of a larger size and more complex wiring, the design can
be extended to allow for torque measurements too, e.g. by duplicating the Butterfly structure along its axis of symmetry.
4
System Development
This chapter first describes the requirements for ultrasound guided lesion size
assessment during radiofrequency ablation with multi-axial force feedback.
After a brief introduction to ultrasound fundamentals, the components of the
system are described in more detail with the specific problems resulting from
the applied technologies. Finally, the complete system and the synchronization
are depicted at the end of the chapter.
4.1
Introduction
We set out to investigate various aspects of lesion size assessment during RF
ablation. In this context, the system requirements should be mentioned here:
Radiofrequency ablation ability with temperature and impedance measurement as additional feedback.
Multi-axial force feedback for determination of the contact force magnitude
and the angle between the catheter tip and the target tissue.
Diagnostic imaging for real-time evaluation of the lesion depth.
Based upon these requirements, we have designed a system consisting of commercially available as well as custom design parts. During RF power delivery
high voltage occurs that may lead to corrupted measurements of contact parameters or lesion depth. In order to avoid disturbances between the RF ablation
and other components of the system we designed an RF ablation equipment
that allows for synchronization of power delivery and data acquisition. The
76
4. S YSTEM D EVELOPMENT
Butterfly sensor introduced in Chapter 3 has been used for multi-axial force
feedback, whereas for the diagnostic imaging we chose a single element ultrasound transducer.
4.2
Ultrasound Fundamentals
Diagnostic medical ultrasound is an imaging method that uses high-frequency
sound waves to produce images of structures within the body. Ultrasonic waves
are described as mechanical waves propagating longitudinally causing rarefaction and compression of the medium. The term ’ultrasound’ refers to frequencies greater than 20kHz, beyond the threshold of human hearing. For clinical
applications the typical frequency ranges from 2 to 20MHz. While ultrasound
waves propagate through a material, different phenomena occur such as reflection, scattering and attenuation. Specular reflexion takes place when the ultrasound echo is originated from relatively large objects with smooth surface,
for example tissue boundaries. Such echoes are typically intense and angle
dependent. Scattering arises from small, less reflective and irregularly shaped
objects, that are smaller than the wavelength. At each interface between tissue
regions with different acoustic properties the reflection of the sound wave induces echo. Consequently, the penetrating wave loses energy at each interface.
The reflected intensity IR from a surface can be characterized by:
IR = I0 R,
(4.1)
where I0 [W/m2 ] is the acoustic intensity at the interface and
R=
Z1 − Z2
Z1 + Z2
2
(4.2)
is the reflection coefficient. The parameters Z1 , Z2 [kg/(sm2 )] are the acoustic
impedances of the two different mediums. The characteristic acoustic impedance
is an inherent property of a medium:
Z = ρc,
where c [m/s] is the speed of sound and ρ [kg/m3 ] is the density.
(4.3)
77
4.2. U LTRASOUND F UNDAMENTALS
Attenuation refers to the loss of energy of the wave due to damping of the
tissue. This loss is caused by the internal conversion of acoustic energy to heat.
When sound travels through a medium, its intensity diminishes with distance.
It results in an exponential decay of the amplitude of the propagating wave:
A(z) = A0 e−µA z .
(4.4)
A0 is the amplitude transmitted at distance z = 0, A is the reduced amplitude
after the wave has traveled a distance z [cm] and µA [1/cm] is the amplitude
attenuation factor. The attenuation coefficient α [dB/cm] describes the amplitude drop:
α = 20 log10
A0
A(z)
1 ∼
= 8.68µA .
z
(4.5)
It has been observed that within the frequency range used for medical ultrasound imaging, most tissues have attenuation coefficient that is linearly proportional to the frequency:
α = a · f,
(4.6)
where a [dB/(cm · MHz)] is the absorption coefficient and f [MHz] is the
frequency. Finally, the attenuation at a given distance and frequency can be
calculated:
attenuation[dB] = a · z · f.
(4.7)
For cardiac muscle the value of the absorption coefficient is 1.8dB/(cm · MHz).
Since our main objective is to gather information about the heart wall thickness and the progression of tissue necrosis during RF ablation, we decided to
utilize A-mode ultrasound imaging, in which a single transducer acts both as a
transmitter and receiver. The reflected echo is converted to electric signal and
recorded as a function of time, as depicted in Fig. 4.1.
Knowing the speed of sound in a medium, the distance to an obstacle can be
calculated. To do this we must measure the time for a pulse of sound to travel
to the object and back again:
d = c·
1
· te ,
2
(4.8)
78
4. S YSTEM D EVELOPMENT
Figure 4.1: Returning echoes from tissue regions with different acoustic properties. The distance can be calculated from the elapsed time and the speed of
sound.
where d [m] is the distance between the transducer and the obstacle, c [m/s]
is the sound speed and te [s] is the time-of-flight. The speed of sound in a
material is the function of the bulk modulus K [P a] and density ρ [kg/m3 ]:
s
K
c=
.
(4.9)
ρ
4.3
System Components
The system used throughout this thesis consists of the following components:
• Ablation head
• Ultrasound devices
• Radofrequency ablation equipment
79
4.3. S YSTEM C OMPONENTS
Each of them is explained in more detail in the following section. Additionally,
a mechanical platform with linear actuator has been developed, that is needed
only for evaluation purposes.
4.3.1
Ablation Head
In order to implement the RF ablation, ultrasound imaging, and force sensing
functions in a single tool, we designed an integrated catheter head. As demonstarted in Fig. 4.2, the head consists of a stainless steel RF ablation electrode,
a single element US transducer, a ceramic structural element and the butterfly
sensor detailed in the previous chapter.
(a)
(b)
(c)
(d)
Figure 4.2: Components of the catheter head: stainless steel RF ablation electrode (a), US transducer (b), ceramic structural element (c) and the Butterfly
force sensor (d).
4.3.1.1
Ablation Electrode
Previous studies demonstrated the feasibility of using multi-element lead zirconate titanate (PZT) arrays with hollow catheter tips for US imaging during
RF ablation. Seo et al. [2011b] filled the gap between the US transducer and
the contact point by US gel, whereas Stephens et al. [2009] used irrigated water
as US coupling medium. FEA and experimental results of Wright et al. [2011]
80
4. S YSTEM D EVELOPMENT
have shown that hollow profile catheter tips are capable of performing cardiac
ablation. Therefore, we decided to use a similar concept with a tube-shaped
stainless steel cap slightly bent at one of its termination to increase the contact area, as depicted in Fig. 4.2(a). The electrode has an outer diameter of
3.2mm, the bending curvature at the contact surface is 20mm, the diameter
of the opening is 2.2mm.
4.3.1.2
Ultrasound Probe
Figure 4.3: Schematic of the single-crystal US transducer.
In ultrasonic applications the sound pulse is produced by vibrating piezoelectric elements that are capable of detecting the echo, as shown in Fig. 4.3. The
transient voltage applied on the crystal electrodes causes mechanical deformation of the piezo element. Such conversion of electrical energy to mechanical
energy is known as the piezoelectric effect. The resonant frequency of the US
transducer’s crystal determines its behavior: rise in frequency increases the US
image resolution, and a decrease results in deeper penetration with a larger field
of view. As a consequence, specifying the resonant frequency of the transducer
is a trade-off between high resolution and deeper penetration. For cardiac ablation the depth of interest is in the range of 10mm, therefore, we chose to use
a 20MHz probe that is capable of achieving the desired depth. Being reflected
4.3. S YSTEM C OMPONENTS
81
from the object under investigation, the US pulse hits the crystal and has the reverse effect: owing to the mechanical excitation the spatially separated charge
results in electric potential. The signal is then amplified and conditioned by the
receiver circuit, which makes the resulting RF data available for further analysis. To prevent the crystal from ringing and from transmitting pulses in both
directions, there is damping material behind the piezo element. In addition, the
insulation between the crystal and the plastic case further improves the acoustic
decoupling. On the contrary, the isolation on the front electrode is acoustically
invisible so that it does not reduce the signal intensity.
In the context of this thesis, a custom made, single element US transducer
(Vermon, Tours, France) was chosen. In contrast to previous studies, the probe
of our choice does not allow B-mode US imaging, however, the data acquisition and signal processing of the single element transducer does not require
complex, expensive equipments. By defining the transducer’s dimensions to
be 5mm × 2mm it fits the opening of ablation electrode with an 0.1mm gap.
In order to keep the design simple and to minimize the length of the catheter
head, the contact surface of the US probe and the round opening of the electrode are on the same plane. Therefore, no coupling medium is used between
the transducer and the tissue.
4.3.1.3
Ceramic Element
As depicted in Fig. 4.2(c), a structural element has been designed in order that
the ablation electrode and US probe can be mounted on the force sensor. During RF ablation high temperatures (> 70◦ C) occur at the contact area between
the ablation electrode and the tissue. Even though the Wheatstone bridges on
the force sensor are thermally compensated, such temperatures may result in
measurement errors. Therefore, the part is made out of MACOR (Corning Inc.,
NY, USA), a glass ceramic with low thermal conductivity of 1.46W/(mK),
which makes it a good thermal insulator. In addition, owing to its high compressive strength of 350MP a, MACOR can be shaped using standard precision
machining tools, which simplifies the manufacturing. Another important role
of the part is the electrical insulation between the ablation electrode and the
force sensor.
82
4. S YSTEM D EVELOPMENT
(a)
(b)
Figure 4.4: Technical drawing (a) and CAD model (b) of the assembled
catheter head. The dimensions are given in mm.
4.3.1.4
Sensor Head Assembly
In order to reduce the electrical noise the force sensor body is electrically
grounded, whereas the electrode might reach 200V AC at 500kHz. The 0.7mm
wide rim of the ceramic element provides electrical insulation between the
force sensor and the electrode, as can be seen in Fig. 4.4(a). The US transducer
is inserted into the 2mm hole of the ceramic part, the force sensor is fixed
to the element by a 2.5mm long M 2.5 screw thread. The hollow structure of
the ceramic part and the force sensor allows for through-hole wiring of the US
transducer. The electrode is glued to the MACOR element, the wire for the RF
current is soldered to the electrode.
4.3. S YSTEM C OMPONENTS
83
Figure 4.5: Exploded and assembled CAD model of the catheter head and the
mechanical support element.
We designed a stainless steel part that serves as the basis of the integrated
catheter head. The symmetric force sensor is fastened into the M 2.5 internal
thread of the hollow basis, that has an outlet on its side for the US wiring.
Additionally, the shape of the basis makes it possible to assemble it on the
Nano 17 multi-axial force-torque sensor.
4.3.2
Ultrasound Devices
The ultrasound pulse can be generated either by short harmonic or transient
electrical excitation, such as spike or square signal. The spike excitation produces an abrupt voltage transition followed by a recovery to the baseline. This
technique optimizes broadband response and near surface resolution by ultrafast rise time, resulting in a wideband transducer response. Square wave excitation, however, is associated with improved penetration capability and increased
sensitivity while maintaining broadband performance by tuning pulse width of
the square wave to half that of the transducer’s center frequency. This way the
transfer of energy between the transmitter and the transducer is maximal.
As the ultrasonic signal resulting from a transducer is bipolar, it is important to
choose which alternation will be used for the release of the pulse. As shown in
Fig. 4.6, for the excitation we use a USBox (LeCoeur Electronique, Chuelles,
84
4. S YSTEM D EVELOPMENT
Figure 4.6: Scheme of the ultrasound setup. The US probe is excited by the
US Box, then the echo is amplified by the Olympus receiver and finally the
data sampling takes place in the US Box device.
France) ultrasonic testing tool to generate negative square pulses with an amplitude of 50V and 25ns duration to fit the transducer’s resonant frequency
of 20MHz. Then the echo is received by an Olympus 5073PR (Waltham, MA,
USA) analogue pulser/receiver, which has superior low-noise receiver response
and high performance signal conditioning. With the low and high pass filters
turned off, the receiver has a bandwidth of 1kHz to 75MHz and 200µV peakto-peak noise. In order to fit the dynamics of the data acquisition’s A/D stage,
the receiver has a gain of 35dB. Finally, the amplified signal is digitized by
the receiver stage of the USBox. The input stage has a broadband RLC filter
with a bandwidth of 500kHz to 25MHz. We found that the internal amplifier
of the USBox shows nonlinearity at higher gain than 15dB, so this value has
been set. The sampling of the non-rectified high-frequency A-mode line takes
place at 80MHz frequency and 12 bit resolution. Each RF scan consists of
2000 samples, in case of water at room temperature the scan depth is
d = c·
1 1
1
1
· · N = 1623m/s · ·
· 2000 = 20.29mm,
2 fs
2 80MHz
(4.10)
where fs is the sampling frequency and N is the number of samples. The
digitized data are sent to the computer for signal processing via USB interface.
4.3. S YSTEM C OMPONENTS
4.3.3
85
Radiofrequency Ablation
The commercially available RF ablation tools are expensive and offer limited
synchronization capabilities. For this reason, we decided to develop an RF ablation system with multi-axial force sensing and temperature measurement. We
have designed and calibrated an ablation module, which can be synchronized
with a host computer. Additionally, it is capable of measuring and recording
the basic electrical parameters, such as the impedance and power. Taking safety
considerations into account, the equipment is enabled by a footswitch pedal, so
the operator can quickly interact in case of danger. Additionally, a buzzer and
the red light of the illuminated push button are indicating the ongoing ablation.
Fig. 4.7 demonstrates the hardware components of the ablation system.
Figure 4.7: Schematic of the ablation system.
4.3.3.1
Electrical Design
To generate high frequency alternating current for the ablation procedure, we
have designed a transformer coupled voltage-switching Class-D power amplifier (Grebennikov et al. [2012]), that employs a pair of active devices operating
in push-pull mode with a tuned output filter. Ideally, it transforms the total
DC power into a fundamental-frequency power delivered to the load without
power losses at the harmonics. As a consequence, the theoretical efficiency of
86
4. S YSTEM D EVELOPMENT
an idealized Class-D power amplifier achieves 100%. Fig. 4.8 shows the simplified circuit schematic of a transformer-coupled voltage-switching Class-D
MOSFET power amplifier including a tuned fifth order lowpass LC filter and
a load resistance RL . The active devices Q1 and Q2 are driven 180◦ out of
phase to switch on and off alternately, creating rectangular voltage drain waveforms. The broadband center-tapped transformer has m turns in each half of
the primary winding and n turns in the secondary winding. Assuming zero saturation resistance of the active devices, while transistor Q1 is turned on during
the first half-cycle, its drain voltage v1 (ωt) is zero. Consequently, DC supply
voltage VCC is applied to one-half of the transformer’s primary winding, causing voltage (−n/m)VCC to appear on its secondary winding. However, when
transistor Q2 is turned on, DC supply voltage VCC is applied to the other half
of the primary winding, being transformed to the voltage (n/m)VCC on the
secondary winding.
As a consequence, when Q1 is on and Q2 is off for 0 ≤ ωt ≤ π,
v1 (ωt) = 0
(4.11)
v2 (ωt) = 2VCC
n
v(ωt) = − VCC .
m
(4.12)
(4.13)
When Q1 is off and Q2 is on for π ≤ ωt ≤ 2π,
v1 (ωt) = 2VCC
(4.14)
v2 (ωt) = 0
n
v(ωt) = VCC .
m
(4.15)
(4.16)
Hence, the resulting secondary voltage v(ωt) is a square wave with levels of
±(n/m)VCC , whereas the drain voltages are square waves with levels of 0
and +2VCC . Taking the saturation resistance rsat of the active devices into account, the fundamental-frequency voltage amplitude VL of the voltage vL (ωt)
on the load RL can be obtained by
87
4.3. S YSTEM C OMPONENTS
Figure 4.8: Transformer-coupled voltage switching Class-D amplifier with a
tuned fifth order lowpass LC filter and the corresponding waveforms.
VL = −
1
π
Z
2π
v(ωt)sin(ωt)d(ωt) =
0
4 n
1
.
VCC
π m
1 + rsat
R
(4.17)
With one-half of the primary winding open, the equivalent fundamental-frequency
resistance seen by each device across the other half of the transformer’s primary winding can be expressed by
R=
m 2
n
RL ,
(4.18)
88
4. S YSTEM D EVELOPMENT
so assuming resistive load and an ideal transformer the output power P at the
fundamental-frequency is obtained by
P =
2
8 n 2 VCC
1
1
1 VL2
8 V2
= 2
= 2 CC
. (4.19)
2
2 RL
π m
RL 1 + rsat
π R 1 + rsat 2
R
R
Hong et al. [2002] made in vitro measurements of electrical impedance during RF cardiac ablation in order to predict the catheter – endocardial contact.
They found that at 500kHz frequency the characteristic impedance is typically
around 100Ω. Based on the assumption that our setup produces similar results,
we used these values in our design. In order to suppress the third- and higherorder odd harmonic components of the RF current output, the inductance and
capacity values of the fifth order Butterworth lowpass LC filter have been determined considering 600kHz cutoff frequency:
L1 = 40µH
L2 = 10µH
(4.20)
C = 1nF.
We use IRFB4127 power MOSFETs driven by an IR2110 high speed FET
driver. The maximal saturation resistance of the MOSFETs is 20mΩ. The
transformer has a turns ratio of 2:6, whereas the supply voltage is VCC = 24V ,
thus the power is
P =
8
π2
2
(24V )2
1
6
= 41.85W.
2
100Ω 1 + 20mΩ 2
11.11Ω
(4.21)
The circuit is operated in power control mode. It means that the ablation circuit
delivers arbitrary constant power within its performance range, independent of
the load’s impedance. Instead of setting the amplitude of the RF current, the
power control is implemented by pulse-width modulation of the ablation enable
signal. Fig. 4.9 shows the schematic of the ablation system. To determine the
impedance, we measure the voltage, current and the phase between them. We
neglect the loss of the coaxial cable between the Class-D amplifier and the
ablation patch electrode. A high-value shunt-resistor voltage-divider is used to
measure the voltage uu over the load:
89
4.3. S YSTEM C OMPONENTS
uu (ωt) = uRF (ωt) ·
R1
,
R1 + R2
(4.22)
whereas the low-value series-resistor rs creates a voltage signal ui proportional
to the current i through the load:
ui (ωt + φ) = Rs · i(ωt + φ).
(4.23)
In order to reduce electromagnetic radiation that might interfere with the US
and force sensor wires, the RF amplifier is connected to the catheter tip electrode via a coaxial cable. The shield of the cable and the patch electrode are at
the same potential ui .
Figure 4.9: Electrical system for voltage and current measurement.
The voltage and the current sensing have the same signal conditioning process,
as demonstrated in Fig. 4.10. In both cases, a buffer amplifier provides electrical impedance transformation. Then, non-inverting amplifiers fit the signal
amplitude to the dynamics of the AT90USB1287 (Atmel, San Jose, CA, USA)
microcontroller’s AD channels. The next stage is a full-wave rectifier, followed
by an active lowpass filter. This way, the resulting DC voltages Uu and Ui are
proportional to the amplitude of the measured AC voltages uu and ui , accordingly. In order to determine the phase shift φ between uu and ui , comparators
are used to create square waves from the amplified AC voltages. The square
waves are connected to an exclusive or (XOR) gate, which produces rectangular waveform with a duty cycle that correlates with the phase shift. Then an RC
lowpass filter converts the signal to DC voltage (Uφ ), which can be connected
to the AD stage of the microcontroller.
90
4. S YSTEM D EVELOPMENT
Figure 4.10: Schematic of the voltage, current and phase measurement.
Figure 4.11: Test load for the calibration. The chain of resistors are parallel
connected with a capacitor.
4.3.3.2
Calibration
To relate the measured DC voltages to the load’s current, voltage and the phase
shift between them, a calibration process has been carried out. As a reference,
we used a series resistor chain consisting of n · 22Ω, with n ranging from 2
to 14. To obtain different phase values, different capacitors were connected
in parallel to the series of resistors, as shown in Fig. 4.11. In four sets of
experiments we used 0.23nF , 0.51nF and 1nF , as well as no capacitive load.
The impedance of the test load can be calculated by
Z=
R
,
1 + jωRC
(4.24)
91
4.3. S YSTEM C OMPONENTS
thus the magnitude of the impedance is
|Z| = p
R
,
(4.25)
φ = − tan−1 (ωRC) .
(4.26)
(ωRC)2 + 1
and the phase is expressed by
Fig. 4.12 shows the theoretical magnitude and phase values of the test load’s
impedance for every possible combination. The covered impedance and phase
range have been chosen based on the previous findings of Bragos et al. [1996].
We gathered experimental data using the corresponding loads. In total, 14 × 4
measurements were made while recording the voltage, current and the phase.
Fig. 4.13 depicts the measured voltage uRF , current i and phase φm of the test
loads, as well as the fraction
(a)
(b)
Figure 4.12: Theoretical values of the impedance magnitude (a) and phase (b)
for different parallel RC test loads.
of uRF and i, which can be expressed by the magnitude and phase angle of the
impedance:
uRF
|Zload |
=
.
i
cos(φm )
(4.27)
92
4. S YSTEM D EVELOPMENT
(a)
(b)
(c)
(d)
Figure 4.13: Measured current (a), voltage (b), phase (c) and the fraction
uRF /i (d) for different parallel RC test loads.
As a consequence, knowing the measured phase and corresponding theoretical impedance magnitude values, the impedance magnitude and phase can be
approximated by 3rd order, two variable polynomials:
|Z̃|(u, i, φm ) =
3 X
3
X
aij
u i
i
i=1 j=1
φ̃(u, i, φm ) =
3 X
3
X
i=1 j=1
bij
u i
i
φjm ,
φjm .
In case of continuous operation, the power is given by
(4.28)
(4.29)
93
4.3. S YSTEM C OMPONENTS
Figure 4.14: Calculated electrical power for different parallel RC test loads.
P̃ =
1
· u · i · cos(φ̃).
2
(4.30)
The calculated power is demonstrated in Fig. 4.14. Unlike Eq. 4.19 suggests,
it is not inversely proportional to the load impedance, not even in case of pure
resistive load. The main reason for this is the frequency dependent transmission
property of the ablation circuit’s output filter. However, in every case above
66Ω load (# of resistors = 3), the circuit is capable of delivering more than
20W while being operated continuously.
4.3.3.3
Temperature Sensing
To measure the temperature we used a K-type thermocouple with an integrated,
cold junction compensated MAX6675 (MAXIM, San Jose, California, USA)
converter, featuring 0.25◦ C resolution and SPI bus communication with the
microcontroller.
4.3.3.4
Force Sensing
The electronics for the force sensing is identical to the one detailed in the previous chapter.
94
4.3.4
4. S YSTEM D EVELOPMENT
Platform
For validation purposes, we have built a platform consisting of a frame, a linear
actuator and a rotating unit that can be set up in arbitrary direction, as shown in
Fig. 4.15. The platform provides mechanical support and precise control over
the position of the catheter tip in our laboratory setup and evaluation experiments. The custom made electro-mechanical linear actuator generates linear
motion from a rotating motor. As depicted in Fig. 4.16, for the actuation we
used a high precision 2.5W DC step motor, a digital encoder of 512 counts
per turn and a planetary gearhead with 19:1 reduction matched to the motor
(Maxon Motor AG, Sachseln, Switzerland). The shaft of the gear is mechanically connected to rotate a lead screw, which has a continuous helical M 4
thread machined on its circumference running along the length. Threaded onto
the lead screw, there is a nut with corresponding threads. The nut is prevented
from rotating by a notch screw. As a consequence, when the lead screw is rotated, the nut will be driven along the threads without rotating. The direction
of motion of the nut depends on the direction of rotation of the lead screw,
ensuring identical behavior in case of extending or retracting. One of the advantages of this structure is that if the rotational force on the screw is removed,
it will remain motionless where it was left, regardless of how much load it is
supporting. Moreover, it provides repeatable move with fine resolution in the
micron range.
Figure 4.15: Picture of the mechanical setup showing the frame, the translational actuator and the catheter head.
95
4.4. F ULL S YSTEM
Knowing the pitch P [mm] of the thread, the encoder’s number of counts per
turn N and the reduction R of the gearhead, the theoretical resolution can be
calculated:
Figure 4.16: CAD model of the translational actuator.
resolution =
0.7mm
P
=
= 72nm.
N ·R
512 · 19/1
(4.31)
The position feedback of the actuator has been implemented by a Maxon EPOS
24/2 modular digital positioning controller, that is communicating with the host
computer via USB port.
4.4
Full System
The scheme of the fully assembled system is depicted in Fig. 4.17. The RF
ablation is performed in power control mode, so during ablation constant electrical power is delivered, independent of the varying load impedance. Instead
of controlling the magnitude of the RF current, the ablation module operates
in pulse width modulation (PWM) control mode. Considering the thermodynamics of the ablated tissue, the switching frequency of 20Hz is sufficient.
Preliminary experiments with chicken breast samples showed that the PWM
96
4. S YSTEM D EVELOPMENT
operated system is capable of creating lesions just as well as ablating with
lower power and 100% duty cycle. The PWM function is implemented by the
aid of the microcontroller, in each cycle 50ms corresponds to 12500 controller
clock cycles, thus the time resolution is 4µs. As the high amplitude RF voltage
would make the ultrasound recording noisy, the US Box and the RF ablation
can not be operated at the same time. The highest single recording sample frequency of the US Box is 100Hz. Taking this into account, the last 10ms of
each cycle is dedicated to the US sampling and communication with the host
PC, during which the ablation is turned off, as shown in Fig. 4.18. Considering
the time constants of the filters applied in the system, the electrical parameters
such as voltage, current and the phase between them are sampled 1ms after
the beginning of each cycle. This way, the duty cycle ranges from 2% to 80%.
The scheme of the closed-loop power control system implemented on the microcontroller is demonstrated in Fig. 4.19. In every cycle, the ablation module
sends the measured current, voltage and phase to the host computer, as well as
the raw force data, the temperature sensor’s reading and a flag indicating if the
ablation is on. The duty-cycle is defined by the ratio of the value of the PWM
register N and the total number of controller clocks in a cycle. Knowing the
electrical parameters, the calculated phase and N , the delivered power can be
calculated:
P =
N 1
· u · i · cos(φ̃).
12500 2
(4.32)
Comparing it to the desired electrical performance value and normalizing the
error, the controller’s output is given by
Zt
u(t) = Kp e(t) + Ki
e(τ )dτ + Kd
de(t)
,
dt
(4.33)
0
whereas the discrete implementation is
u(tk ) = u(tk −1)+Kp e(tk )+Ki
k
X
i=1
e(ti )∆t+Kd
e(tk ) − e(tk−1 )
. (4.34)
∆t
97
4.4. F ULL S YSTEM
Figure 4.17: The complete system used throughout this thesis.
We determined the proportional Kp , integral Ki and derivative Kd terms by
intuitive tuning of the control system:
Kp = 0.35
Ki = 0.001
(4.35)
Kd = 0.002.
The scaling parameter FS1 was chosen to be 30W , which represents the maximum deliverable power. The corresponding PWM register value is 10000, so
is the FS2 parameter.
98
4. S YSTEM D EVELOPMENT
Figure 4.18: Scheme of the sampling and communication timing during ablation.
Figure 4.19: Illustration of the power control loop implemented by a PID controller.
4.5. C ONCLUSION
4.5
99
Conclusion
We have introduced a novel system for ultrasound-guided lesion observation
during RF ablation with multi-axial force feedback. The components as well
as the assembly of the sensorized catheter head have been presented in detail.
Additionally, we have implemented an ultrasound setup and built a mechanical
platform with a precise linear actuator, the latter for the purposes of validation
experiments in this thesis. Finally, we elaborated on the design and calibration of the RF ablation equipment and proposed a concept for synchronization
between the ablation and US imaging. The resulting system is capable of performing the planned lesion size assessments.
5
Lesion Size Assessment
This chapter first gives a review of lesion size assessment methods, with a
focus on those which are based on technologies that are available in the system described in Chapter 4: monitoring multi-axial contact force, electrical
impedance measurement and US imaging. Then the experiments with porcine
cardiac tissue are depicted, followed by the description of the applied methods.
Finally, the results are presented and discussed at the end of this chapter.
5.1
Related Research
Lesion formation during RFA is influenced by various factors such as cavitary
blood flow, coagulum on the tip and electrode orientation. Since these parameters are hard to control, it is challenging to predict the resulting lesion size.
Other selectable factors include contact force between the catheter and tissue,
power settings, interface temperature and duration of treatment. Monitoring
these parameters, as well as tissue impedance, and US imaging techniques offer potential for lesion size assessment.
5.1.1
Contact Force Based Methods
The ablation electrode – tissue contact force is an important determinant of
safety during RFA, and it shows potential for assessing lesion size. Jain &
Wolf [1998] simulated temperature controlled RF ablation with a 3D finite
element model to investigate the effect of electrode penetration depth on lesion
dimensions. They demonstrated that the mean power decreased with increasing
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penetration depth, which showed good correlation with lesion size. The lesion
growth reached a plateau after approximately 90 seconds irrespectively of the
penetration depth. Weiss et al. [2002] performed RFA with constant contact
force ranging from 0.05 to 0.5N and a parallel or orthogonal orientation of
the catheter. They found that only in orthogonal orientation with high catheter
contact force of 0.5N were significantly deeper lesions created compared to
0.05, 0.1 and 0.3N contact force.
It is important to emphasize that contact forces vary significantly in situ. Shah
et al. [2010] evaluated contact force – time integral as a predictor of unipolar irrigated RF lesion depth and volume in a contractile model simulating the
beating heart. An open-tip irrigated catheter equipped with an optical fiberbased force sensor (Endosense SA, Geneva, Switzerland) was attached to a
movable platform allowing closed loop control to obtain contact force variations between the catheter tip and bovine skeletal muscle in vitro. It has been
demonstrated that the time integral of contact force correlates linearly with
lesion dimensions. Okumura et al. [2008] established a relationship between
contact force and lesion formation during irrigated ablation of canine cardiac
tissue in vivo. They found that contact force was a robust predictor of lesion
size and transmurality. It has been reported that an increase in contact force
generates greater contact area, resulting in a favorable impact on lesion formation. Yokoyama et al. [2008] also developed a saline irrigated catheter to
measure contact force during RFA. The catheter tip was held orthogonally to
canine tissue in vivo with different contact forces ranging from 0.02 to 0.4N .
At each power level, higher contact forces resulted in increased tissue temperatures, lesion sizes as well as a higher risk of thrombus formation.
5.1.2
Temperature Based Methods
Over the past decades, electrode tip temperature has been commonly used as an
effective indicator of lesion formation. A lesion size estimator for temperaturecontrolled cardiac RFA has been introduced by Yu-Chi et al. [2004]. Their
prediction of lesion dimensions was in good agreement with FEM simulations
and in vitro experimental results. However, direct measurement of catheter tip
temperature in vivo is subject to inherent limitations. Temperature monitoring
is highly dependent on catheter orientation and convective cooling of the electrode (Blouin et al. [1991]; Chugh et al. [1999]). It has been reported by Jain
& Wolf [1998] that lesion growth reaches a steady state during temperature
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103
controlled RFA. Bunch et al. [2004] showed that even after cessation of power
delivery, tissue temperatures remain elevated in cardiac muscle. Consequently,
direct temperature measurement is not optimal for lesion size assessment.
5.1.3
Impedance Change Based Methods
Monitoring electrical parameters during RFA is another possible approach for
prediction of lesion dimensions. In case of unipolar RF power delivery, the
impedance measured between the RF and patch electrode represents the cumulative impedance of electrical pathway, which is impacted by a variety of factors related to anatomical parameters such as body size of the patient, catheter
tip dimensions, reference patch area and the proximity of the patch to the heart
(Wang et al. [1995]). Since the tissue domain close to the RF electrode has the
highest influence, changes of its electrical conductivity have the most effect on
the cumulative impedance (Wittkampf & Nakagawa [2006]).
Fig. 5.1 depicts typical electric power, impedance magnitude and phase curves
during unipolar RFA in power control mode. Initial heat transfer during ablation procedures results in higher ion mobility and consequently an impedance
drop. A similar effect can be observed in saline as well (Stogryn [1971]). It
has been demonstrated by Hartung et al. [1995] that the magnitude of decrease
in impedance correlates well with the temperature of the electrode-tissue interface. Harvey et al. [1992] reported an average fall in impedance of 10Ω
while continuously monitoring electrical parameters during RFA treatment in
vivo. The early decrease in impedance proved to be predictive of tissue heating, whereas initial values of output voltage, current and impedance were not
capable of estimating the effectiveness of power delivery into the tissue.
Impedance measurement is heavily influenced by contact conditions too. A
3D finite element model presented by Jain & Wolf [1998] revealed that deeper
electrode penetration depth is associated with greater impedance drop as well.
Wang et al. [1995] showed that increasing tip-tissue contact results in increased
impedance. The reason for that is the higher resistivity of blood than that of
the endocardium (Cao et al. [2002]). Thiagalingam et al. [2010] performed
RFA on freshly excised porcine hearts ex vivo and demonstrated that the initial
impedance drop correlates well with catheter contact force.
Despite its promising potential, impedance monitoring has certain limitations.
Okumura et al. [2008] reported that there exist occasional plateaus in lesion
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(a)
(b)
(c)
Figure 5.1: Typical electric power (a), impedance magnitude (b) and
impedance phase (c) curves during RFA in power control mode.
5.1. R ELATED R ESEARCH
105
size and transmurality even in cases of observed impedance drops. For this
reason, monitoring the interface impedance alone is not a reliable predictor
of lesion dimensions. On the other hand, impedance changes are good indicators of potentially life-threatening complications. Haines & Verow [1990]
demonstrated both in vitro and in vivo that coagulum formation on the catheter
electrode and the occurrence of popping produce a sudden rise in impedance.
5.1.4
Ultrasound Based Methods
5.1.4.1
Conventional Sonography
A-mode (amplitude mode) sonography uses a single crystal transducer to scan
a line through the sample with the echoes recorded as a function of depth.
M-mode (motion mode) ultrasound imaging uses pulses emitted in quick succession to generate a sequence of amplitude mode recordings, that can be used
to display the change of specific organ structures over time. In B-mode (brightness mode) ultrasound imaging an array of transducers scan a plane through the
body and visualizes the data as a 2D image. The brightness of the patterns in
the grayscale image corresponds to the echo intensity at a given location.
Standard B-mode ultrasound imaging has been commonly used to visualize the
progress of thermal therapy (ter Haar et al. [1989]; Yang et al. [1993]). However, it is generally inadequate to identify necrotic tissue as the echogenicity
shows no significant change during thermal therapy (ter Haar [1995]; Hynynen
[1997]; Bush et al. [1993]). The coagulative tissue region can only be identified
in the case of high microbubble concentration caused by cavitation or boiling
(Vaezy et al. [1997]; Chavrier et al. [2000]; Kennedy [2005]). However, the
hyperechoic region does not necessarily correspond to the ablated domain seen
on gross-pathology. It has been demonstrated by Bunch et al. [2004] that microbubbles are inconsistent markers of tissue overheating, therefore can not be
used for lesion assessment. Hence, conventional ultrasonography is not suitable for the assessment of necrotic lesions.
Nevertheless, some recent works using high frequency US report progress in
this field. Wright et al. [2011] performed RF ablation in an open-chest sheep
model by self-developed irrigated catheters with integrated high frequency (25
- 33MHz) ultrasound. Upon noise filtering and smoothening of the M-mode
US image acquired during ablation, they successfully identified the depth of
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tissue necrosis and predicted lesion transmurality. Kumon et al. [2012] acquired RF data with a confocally and perpendicularly aligned high-frequency
imaging system (Visualsonics Vevo 770 with 55MHz center frequency) before, during and after HIFU exposure of porcine cardiac tissue samples. They
reported good correlation between the lesion width gained from the M-mode
image and the pathology images.
5.1.4.2
Ultrasonic Motion Tracking
Non-invasive temperature-induced echo-shift imaging is based on the fact that
the speed of sound in non-fatty tissues increases with temperature. Monitoring
local tissue dilatations from echo waveforms aims to extend lesion assessment
capabilities beyond those of the conventional ultrasound imaging and offers
potential for lesion size assessment.
The temperature dependence of ultrasound echo is caused predominantly by
two physical phenomena: thermal expansion of the propagating medium and
variation of sound speed due to changes in temperature (Amini et al. [2005]).
Besides, the degree of thermal damage also has an impact on the echo. In
most tissue media around body temperature, by increasing the temperature the
speed of sound generally increases (Kinsler et al. [1999]). However, as it was
shown by Zheng & Vaezy [2010], the rate of change is heavily dependent on
the proportion of fat in the tissue. The change of sound speed induces a time
shift in ultrasound RF echoes, often referred to as a virtual shift (Mehrabani
et al. [2008]). Tracking this shift offers real-time characterization of sound
speed changes in cardiac tissue as a function of temperature.
Temporal tracking of the zero-crossings in the RF signals has been proposed
by Srinivasan & Ophir [2003]. In this technique, the motion is estimated by
identifying and assigning the zero-crossings between successive RF A-scans.
Since the zero-crossings may fail due to excessive local dislocation at areas of
high strain matching, this method performs well only in the case of smaller
motion.
Cross-correlation (CC) based approaches are among the most commonly used
motion estimation techniques. In these methods signal blocks registered in
each frame are swept over the consecutive recording in the vicinity of the reference block positions. The local instantaneous displacements between two RF
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lines are estimated by finding the maximum of the CC function among corresponding signal blocks (Lubinski et al. [1999]). The major problem associated
with the standard CC technique is that significant deformation of the media
might create dissimilarities between blocks that make the method fail. To circumvent this problem, Varghese & Ophir [1997] suggested an improved technique that takes temporal stretching of the signal into consideration, resulting
in more robust and accurate motion estimation. Nonetheless, estimating both
the temporal stretching and the time-shifts notably increases the computational
effort.
Other approaches aim to determine peak displacements. A one-dimensional
peak tracking method was proposed by Eskandari et al. [2007] to estimate the
motion between successive ultrasound RF signals. The algorithm locates the
peaks using the continuous wavelet transform. In order to avoid false matches,
the peaks in the adjacent RF frames are assigned to each other by a peak matching technique. Besides its higher sensitivity and better signal-to-noise ratio
than the standard CC technique, this method shows high computational efficiency. However, the obtainable accuracy is limited by the sampling rate.
5.1.4.3
Backscatter
Other techniques aim to determine parameters related to local changes in received echo energy. The integrated backscatter (IBS) represents the frequency
average of the backscatter function over the useful bandwidth, it can be characterized by (Waters et al. [2001]):
1
IBS =
fh − fl
Zfh
10 · log10
Ps (f )
df
P0 (f )
(5.1)
fl
where fl and fh are the lower and upper limits of the selected bandwidth,
Ps (f ) is the power spectrum of the backscattered signal and P0 (f ) is the transfer function of the measurement system. Straube & Arthur [1994] showed that
the temperature dependence of the backscattered power was dominated by the
effect of temperature on the backscatter coefficient of the tissue volume of interest. In contrast, effects of temperature on velocity had no significant impact
on the backscattered power. Dramatic increase in both attenuation and IBS
during ablative therapy was reported by Zhong et al. [2007]. They attributed
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this phenomenon to the bubble formation and cavitation due to boiling and
structural change of the tissue. The proposed method compared the RF data
frames with a reference frame taken before treatment to obtain a differential
image. Zhang et al. [2009] investigated the transient characteristics of attenuation coefficient and IBS during HIFU treatment in both phantoms and ex
vivo bovine livers. During therapy, IBS and attenuation coefficient of the ablated region initially increased and simultaneously bubble clouds appeared in
the B-mode image. As the lesion appeared, IBS and attenuation both showed
rapid increase. Then, they increased further with fluctuations owing to protein coagulation and bubble formation. Bush et al. [1993] reported significant
increase in the speed of sound and attenuation coefficient in HIFU lesioned
porcine liver tissue samples, whereas the backscatter coefficient did not show
significant change, only in case of acoustic scattering caused by undissolved
gas.
5.1.4.4
Spectral Properties
Some methods rely on the evaluation of RF scans’ spectral properties. Silverman et al. [2006] used spectral parameter imaging for real-time visualization of HIFU-induced lesions. In both the fundamental and harmonic bands
of the diagnostic probe, the midband-fit (MBF) was calculated, which is the
amplitude of the best-fit equation at the center frequency of the transducer’s
power spectrum. As the product of MBF and bandwidth estimates the integral of the spectrum in the bandwidth of the transducer, MBF is closely related to IBS. MBF images produced from the fundamental and harmonic band
showed higher contrast in attenuated tissue structures than the conventional
B-mode images derived from the backscattered RF signal envelope. Kumon
et al. [2009] characterized HIFU induced lesions in porcine cardiac tissue by
calculating the power spectrum for each RF A-scan within the region of interest. In the spectral parametric image maps, the MBF increased significantly in
the ablated region relative to surrounding tissue, whereas its slope decreased.
These changes are predominantly attributed to the occurrence of larger effective scatterer size during coagulative processes. Amini et al. [2005] proposed
a high-resolution noninvasive temperature estimation method by tracking frequency shifts at the harmonic frequencies. Tissues with quasiregular scatterer
lattice have harmonically related peaks in frequency domain, corresponding
to the average distance between scatterers. Since thermal expansion and contraction of the medium affects the scatterer spacing, monitoring the frequency
5.1. R ELATED R ESEARCH
109
shifts at the fundamental and harmonic frequencies shows potential for noninvasive temperature estimation.
5.1.4.5
Patterns from Time Series
Temporal patterns gained from RF time series also have proven to characterize tissue temperature response during thermal therapy. Imani et al. [2013]
utilized temporal features extracted from ultrasound RF time series to identify
ablated tissue regions following high intensity ultrasound ablation. These features include the sums of the amplitude spectrum in a specific frequency range,
slope of the line fitted to the entire spectrum and the power spectrum density’s
mean central frequency for the RF time series signal. The results showed that
tissue characterization obtained from RF time series features performs better
than spectral features of a single RF frame.
5.1.4.6
Time of Flight
Time of flight variation during thermal therapy can also be used as an indicator
of lesion formation. Anand & Kaczkowski [2004] used a clinical ultrasound
scanner to acquire ultrasound RF data during ex vivo high intensity focused
ultrasound (HIFU) therapy of bovine liver in a heated water tank. They locally
estimated the time of flight change during ablation to obtain a 2D travel time
map. Changes were reported in round-trip travel time in the heated region
before visible changes appeared in B-mode images. At body temperature, rapid
increase of the sound speed was demonstrated. Depending on tissue type, the
speed of sound reached its maximum between 50◦ C and 70◦ C, and decreased
with further increase in temperature.
5.1.4.7
Thermal Strain Imaging
Thermal Strain Imaging (TSI) or Temporal Strain Imaging uses heat-induced
apparent strains to describe tissue elasticity. The temperature distribution during ablation is influenced by direct heating of the treated tissue region and spatial redistribution of heat caused by thermal diffusion. TSI utilizes the slope
of temperature change with time to monitor therapeutic effectiveness. In order
to make sure that the change in temperature with time is constant within the
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region of interest, thermal diffusion is minimized by keeping the heat pulses
short, typically in the range of 1 − 5s. During heat deposition both sound
speed change and tissue expansion occur. By tracking shifts in the RF echo,
the observed shift represents changes in speed of sound and thermal expansion.
Thus, the temperature can be approximated by time-delay estimation.
Seo et al. [2010] proposed an 11MHz linear intracardiac echocardiography
(ICE) array mounted on the tip of an RF ablation catheter for TSI. At first,
a reference recording was taken over a cardiac cycle before treatment. Then,
2D cross-correlation of B-mode images revealed the best local feature matches
between the reference frames and subsequent corresponding frames. The local strain was estimated by performing 2D phase-sensitive correlation-based
speckle tracking and computing the thermal strain. Both in vitro and in vivo
experiments showed that thermal strain curves decreased with increasing temperature, then reached a plateau and changed their slope at 50◦ C. Souchon
et al. [2005] applied HIFU in porcine and bovine liver samples in vitro and
acquired cumulative echo-strain images. They hypothesized that significant
tissue expansion occured with tissue necrosis, which made the thermal lesions
visible in the echo-strain maps.
In most TSI applications the heating period is short enough to provide instantaneous heat transfer with minimal thermal diffusion. However, for cardiac
ablation monitoring in which large temperature changes (typically 15 − 30◦ C)
can occur over a period of 10 − 60s, thermal diffusion may be an issue. Anand
& Kaczkowski [2008] determined the echo strain in situ by solving the heat
transfer equation in order to estimate the thermal diffusivity in the region of
interest. A HIFU transducer operating at subablative intensities was used to
generate localized temperature rise in the range of 10◦ C. The thermal diffusivity was estimated locally by evaluating spatial and temporal rates of heat
dissipation in the beamformed acoustic backscatter data. The time instant of
boiling was determined by measuring the acoustic emissions in the audible
frequency range due to intensive bubble formation. In Anand & Kaczkowski
[2009] they estimated the heating rate by measuring the time required to raise
the temperature of the therapeutic focus of HIFU from a baseline temperature
to boiling. The increase of cumulative energy plot computed from the power
spectrum was found to be a good indicator for the time of boiling. Miller et al.
[2004] also used low power HIFU for TSI and reported that a temperature rise
of 2−5◦ C can be detected. Liang et al. [2009] managed to assess thermal strain
with diagnostic ultrasound that produces relatively lower temperature rises.
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111
Another potential field of use for TSI is volumetric evaluation of speckle displacement as a function of temperature. Arthur et al. [2010] successfully generated 3D data sets from images taken by a 7.5MHz phased-array linear probe.
Boctor et al. [2009] demonstrated that parallelized implementation of the 3D
heat-induced echo-strain method on GPUs offers real-time monitoring of HIFU
ablation.
Even though TSI can be used for the monitoring of initial temperature rise to
see if lesion formation takes place, at higher temperatures it is not suitable for
lesion assessment. As tissue coagulation starts and tissue composition changes,
apparent sound-speed change does not directly relate to temperature rise anymore. This phenomenon is assumed to be caused by the change of the tissue’s
mechanical properties during and after necrosis.
Other problems associated with TSI applications in vivo are respiratory- and
cardiac motion (Seo et al. [2011a]). The motion-induced mechanical strains are
typically much larger than their thermal counterparts, therefore they should be
taken into account. By holding the patient’s breath during TSI data acquisition
the respiratory motion can be eliminated, whereas cardiac periodicity offers the
use of electrocardiogram (ECG) to trigger recording.
5.1.4.8
Echo Decorrelation
Echo decorrelation imaging is another technique for identifying transient heatinduced changes in pulse-echo US images. In this method the temporal crosscorrelation between consecutive RF frames is computed. However, unlike echo
strain imaging approaches, echo decorrelation imaging uses the local decoherence between adjacent US scans to identify changes of the tissue properties.
Mast et al. [2008] proposed a technique for assessing echo decorrelation as a
function of temperature change measured locally by a low-noise thermocouple
in vitro. For temperatures over 75◦ C consistent echo decorrelation increases
were reported. One of the possible reasons is bubble formation owing to tissue heating to temperatures approaching or exceeding 100◦ C. Another likely
contributor is the substantial change of the tissue’s microstructure as a result
of heat-induced effects, such as protein denaturation, cell rupture and tissue
cracking. Mast & Subramanian [2010] related the echo decorrelation quantitatively to the local decoherence of the spatial-frequency spectra. The resulting
echo decorrelation values were found to be near zero for positions where the
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medium is unaffected by heating, greater than zero where the tissue is undergoing thermal ablation.
5.1.4.9
Elastography
Elastography is an imaging modality that aims to characterize the elastic properties of soft tissue. Different tissues have different mechanical properties,
which can be determined by evaluating the information encoded in the tissue
motion in response to the application of a small external mechanical stimulus (Ophir et al. [1991]). Assuming a constant stress field, the axial tissue
strains represent a relative measure of tissue stiffness distribution. Various tissue parameters have been successfully estimated, such as velocity of vibration
propagation, shear wave speed, viscosity, Poisson’s ratio and elastic modulus
(Konofagou et al. [2004]; Greenleaf et al. [2003]). Based on these parameters
stiffer tissue regions such as tumors (Lyshchik et al. [2005]) or ablated lesions
(Liu et al. [2004]) can be identified, providing diagnostic information about the
presence or status of disease.
The tissue can be excited by quasistatic compression, harmonic vibration or
acoustic radiation. In order to acquire data of the tissue structure, different
imaging modalities are used, most commonly ultrasound and MRI. Ultrasound
Elasticity Imaging (USEI) has proven to be a promising augmentation to standard US imaging. Estimating local strains and using inverse problem solutions
of assumed volumetric models are the common ways of inferring tissue stiffness.
External compression is a common way of tissue excitation as it provides practicality and simplicity of the system. Kallel et al. [1999] proposed a quasistatic
compression technique for visualizing HIFU-induced thermal lesions in soft
tissues in vitro using elastography. Rabbit muscle samples embedded in gelatin
were excited by a compression apparatus. Measurements were taken at 8 compression levels with a step size of 0.5mm. Cross-correlation method was applied to estimate the displacement between the pre- and postcompression RF
signals for each compression step. The local tissue strains were computed as
the gradients of the measured displacements using a least squares strain estimator (Kallel & Ophir [1997]). The results showed good correlation between
a high stiffness region in the elastogram and the necrotic tissue domain in the
gross pathology photograph. Thittai et al. [2011] have introduced axial-shear
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113
strain elastography (ASSE), an improved technique that takes both axial and
shear strain into consideration. This method uses a 2D block-matching algorithm to compute the displacement map and a 2D kernel least-squares estimator to generate elastograms. Estimation of the lesion boundary using ASSE has
proven to be more robust than exclusively axial strain based methods.
Acoustic Radiation Force Impulse (ARFI) imaging utilizes short-duration localized acoustic radiation force of the US beam to locally excite tissue. Since
the tissue displacement magnitude is inversely proportional to tissue stiffness,
relative changes can be detected by tracking the tissue response immediately
after an elastographic push. Nightingale et al. [2002] used a single transducer
on a modified diagnostic US scanner to demonstrate that displacements on the
order of 10µm can be generated and detected in soft tissues in vivo. They found
that differences in the magnitude of displacement are correlated well with tissue structures in matched B-mode images. The first application of ARFI in a
beating heart was presented by Fahey et al. [2005].
Shear Wave Elasticity Imaging (SWEI) is another method that uses transient
focused pulses of acoustic radiation force (Sarvazyan et al. [1998]). This technique infers elastic properties by locally inducing shear waves and measuring
transient time of travel to nearby locations. In contrast to ARFI imaging, SWEI
uses displacement information from outside the region of excitation (Dumont
et al. [2011]). Typical ultrasonic exposure of SWEI is significantly below the
threshold of damaging effects of focused ultrasound.
Harmonic motion imaging (HMI) is a technique in which dynamic vibrations
are induced in the tissue for elasticity characterization. Shi et al. [1999] applied external low-frequency vibration (< 5Hz) to HIFU-treated porcine liver
samples and measured the resulting velocity change between the ablated and
non-ablated tissue. Quadrature Doppler spectral analysis was performed on
M-mode US data using discrete Fourier transform. The resulting time-varying
Doppler spectrum depicted the velocity variation, which was used to estimate
the stiffness difference between the lesion and the surrounding soft tissue.
Before HIFU treatment, the tissue samples produced linear velocity distribution along the A-line samples, whereas afterwards the velocity profile became
piecewise linear. Zhang et al. [2008] also induced external harmonic vibration
in order to estimate the elasticity distribution in the HIFU target region. 3D
reconstruction of the velocity distribution gained from B-mode samples made
it possible to estimate the lesion volumes, the results showed good correspondence between 3D sonoelastography and gross pathology images. Maleke &
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Konofagou [2008] presented a harmonic radiation-force technique with oscillatory displacements at the focal zone of the HIFU for real-time monitoring tissue
elasticity during therapy. High intensity amplitude-modulated HIFU with a carrier frequency of 4.68MHz and a modulation frequency of 25Hz was applied
in order to induce harmonic motion in the tissue while measuring peak-to-peak
displacements. At the beginning of the treatment, the HMI displacements increased sharply, indicating decreasing tissue stiffness. At temperatures beyond
50◦ C, tissue displacement reached a peak and decreased rapidly indicating increased stiffness of the coagulative necrosis. When heating was sustained, further decrease of the displacement occured, denoting lesion formation resulting
in tissue hardening.
5.2
5.2.1
Experiments
Contact Conditions on Cardiac Tissue Surface
Even though in Chapter 3 the force sensor has already been characterized under
ideal circumstances, there is still a need to clarify how it performs together with
soft tissue. The multi-axial force sensor built in the catheter head serves two
purposes: firstly, it helps to maintain orthogonal catheter tip orientation; and
secondly, it determines the magnitude of contact force. Orthogonality between
the catheter tip and the tissue surface is required in order to ensure optimal
contact conditions between the integrated US sensor and the tissue. Besides,
in case of orthogonal catheter tip orientation the processed US data directly
correspond to the lesion depth.
To reveal the multi-axial sensing capabilities of the catheter head while palpating cardiac tissue, we made two sets of experiments. In the first case contact
has been established by linear translation of the catheter head along its z axis
at different angle of incidence settings, as can be seen in Fig. 5.2(a). In the
second set of experiments, the catheter head was brought into contact with the
tissue by approaching the tissue surface orthogonally in a preset orientation,
as demonstrated in Fig. 5.2(b). The angle of incidence ranged from 0◦ to 50◦
in 10◦ steps in both cases. The contact force was estimated by evaluating the
recordings of the multi-axial force sensor.
As shown in Fig. 5.2(c), while palpating the tissue by linear translation the
estimated values are close to the actual angle of incidence up to 40◦ . Only
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5.2. E XPERIMENTS
(a)
(b)
(c)
(d)
(e)
(f)
Figure 5.2: Scheme of the experimental setups ((a) and (b)), estimated angle
of incidence ((c) and (d)) and force data displayed in the XY plane ((e) and
(f)) for linear translation and orthogonal palpation, respectively.
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5. L ESION S IZE A SSESSMENT
the angle of 50◦ shows significant deviation from the expected value, probably
because of the inhomogeneous elastic properties of the cardiac tissue. In the
second case, however, higher angle of incidence values are estimated than the
actual ones, see Fig. 5.2(d). On the other hand, these values increase monotonically with the actual angle of incidence. Another important aspect is the
distribution of force data in the XY plane. In both cases the shear force components indicate that the catheter head axis should be tilted in the positive y
direction to achieve orthogonality, see Fig. 5.2(e) and Fig. 5.2(f).
These findings suggest that at the contact point the same catheter head orientation and position may result in different estimated angle of incidence depending
on how the contact has been established. Nonetheless, an incremental direction
of turn to make it more orthogonal can be inferred in any case.
5.2.2
Speed of Sound Variations During Homogeneous Tissue Heating
As described earlier in Section 5.1, the temperature dependence of ultrasound
echo is caused by actual thermal expansion and speed of sound variations. The
contribution of the latter has been characterized by evaluating US data acquired
during homogeneous tissue heating in a water bath, see Fig. 5.3. In order
to achieve homogeneous temperature distribution in the tissue and rapid heat
transfer from tissue to water, the samples were fixed on an aluminium heat
sink. A small fan circulated water while two sinking boilers (150W each) were
raising the temperature from 14◦ C to 75◦ C in 28 minutes. The speed of sound
c = 2dR /tR was obtained by measuring the time of propagation tR of the
ultrasound echo reflected from the flat metal heat sink. First the linear actuator
was brought in contact with the heat sink in order to reference the distance.
After fixing the tissue sample on the heat sink, small contact force of 0.05N
was obtained between the catheter head and the sample, yielding a physical
distance of 16.45mm to the heat sink in the experiment reported below. While
heating the system, temperature data were collected by a K-type thermocouple
inserted into the tissue sample as close as possible to the US beam without
corrupting the US measurement.
For a constant dR , the change of c with temperature results in an echo shift, as
shown in Fig. 5.4. The time of flight tR was determined by tracking the echo
peak with the highest amplitude corresponding to the reflector’s surface.
5.2. E XPERIMENTS
117
Figure 5.3: Illustration of the measurement setup for determining the temperature dependence of sound speed in cardiac tissue samples.
Figure 5.4: Illustration of the echo shift due to the changing speed of sound.
By heating the sample, the increasing sound speed results in shorter time of
flight.
For each RF scan we identified the index (m) of the maximal intensity sample
point. Since the resolution of tracking the maximal echo amplitude is limited
by the sampling frequency of the US data acquisition, the time of peak echo
amplitude tR was identified and refined by subsampling through 2nd order
polynomial fitting to the two immediate neighbours of the sample m.
Fig. 5.5 illustrates the time of flight and temperature curves as functions of
time during the experiment. Upon filtering the temperature reading the speed
of sound as a function of temperature is shown in Fig. 5.6. As one can see, the
118
5. L ESION S IZE A SSESSMENT
Figure 5.5: Illustration of the echo shift due to the changing speed of sound
while heating cardiac tissue with a constant distance of 16.45mm between the
transducer and the heat sink.
Figure 5.6: Speed of sound as a function of temperature in porcine cardiac
tissue.
5.2. E XPERIMENTS
119
sound speed in cardiac tissue at 20◦ C temperature was found to be 1623m/s,
which is in line with the previous findings of Masugata et al. [1999] and Seo
et al. [2011b]. To around 65◦ C our results are consistent with those reported
before. First, the speed of sound increases monotonically with temperature to
55◦ C, where it changes its slope indicating tissue denaturation. However, in
contrast to the earlier findings of Lu et al. [1996] we did not observe increase
in speed of sound at temperatures higher than 65◦ C. This variance between the
results can be attributed to differences in study design. We presume that our
setup with submerged tissue samples provides a better model of the intracardiac
environment. We note that over 65◦ C the time of flight curve suffers from
noise. Over 75◦ C the US signal becomes too noisy and further tracking is not
possible.
5.2.3
Ultrasound Data Acquisition During Radiofrequency
Ablation
In our in vitro ablation experiments, 4 × 4cm2 porcine heart samples (N = 35)
from a local slaughterhouse (Zürich) were positioned on a stainless steel patch
and immersed in 0.9% saline solution at 25◦ C temperature, as demonstrated in
Fig. 5.7. The thickness of the myocardium samples varied from 0.5 to 1.3cm.
RF ablations were performed at 12.5W power for intervals ranging from 10
to 100s, while simultaneously acquiring electrical parameters, ultrasound and
contact force data. After ablation, the samples were incised with a sharp scalpel
across the ablation plane, pressed gently against a glass plate and photographed
with a tightly focused high-resolution camera. The ablated region was visually
identified, and the lesion depth was manually marked as shown in Fig. 5.8. The
optical images were evaluated blindly from the ultrasound imaging results. In
order to analyze the influence of the stabilization of contact force (CF) in lesion
size reproducibility the experiments were performed in three test series:
• With undefined contact force and a given ablation time of around 60s
(N = 13).
• With defined 0.4N contact force at ablation times 20s (N = 3), 40s
(N = 3), 60s (N = 3), 80s (N = 3) and 100s (N = 2), in total
N = 14.
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5. L ESION S IZE A SSESSMENT
• With defined 0.04N contact force at ablation times 20s (N = 2), 40s
(N = 1), 60s (N = 1), 80s (N = 3) and 100s (N = 1), in total N = 8.
Figure 5.7: Photo of the experimental setup for Thermal Expansion Imaging.
Figure 5.8: Photo illustrating the cross section of the ablated myocardium.
The lesion boundary is represented by the dotted line.
5.3
5.3.1
Methods
M-mode Imaging
In order to gain enhanced visual representation of the ablated tissue samples,
processing of the digitized US time series was required. First, envelope detec-
121
5.3. M ETHODS
(a)
(b)
(c)
Figure 5.9: Processing steps for ultrasound visualisation: enveloping (a), time
gain compensation (b) and logarithmic compression (c) of the echo intensity
scan.
tion was performed on each US line y by calculating the absolute value of their
discrete Hilbert transform (see Fig. 5.9(a)):
p = |H (y)|.
(5.2)
The attenuation of the medium results in diminished signal intensity amplitude with distance. This phenomenon was taken into account by carrying out
time gain compensation, a technique that enhances the far field visibility at the
cost of increasing noise with the distance from the transducer, as illustrated in
Fig. 5.9(b). The correction can be mathematically modeled as
qi = pi eζ(i−1) ,
ζ > 0,
(5.3)
where ζ is the exponential decay constant. After normalizing the enveloped
and time gain compensated signal
r=
q
,
max(q)
(5.4)
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5. L ESION S IZE A SSESSMENT
logarithmic compression was applied in order to make the otherwise barely
detectable details better observable, as shown in Fig. 5.9(c). The compensated
form can be computed as
si =
ln (ri · e + κ) − ln κ
,
ln (e + κ) − ln κ
κ > 0,
(5.5)
where e is Euler’s number and κ represents the degree of compression.
The resulting baseband echo intensity plot or M-scan can be seen in Fig. 5.10(c).
In addition, to further improve signal quality, state of the art speckle noise
reduction filters (Amplio SDK, Sonix Touch, Analogic Ultrasound, Peabody,
MA, USA) were applied to filter the received echo signal around the transducer
center frequency, before the above mentioned steps. As one can see, it is not
possible to identify the lesion based upon the echo intensity plot. Therefore,
we introduce an alternative method that is based on obtaining instantaneous
strain between consecutive lines.
5.3.2
Thermal Expansion Imaging
During ablation, changes in tissue microstructure occur as a result of tissue
necrosis. In order to avoid correlation tracking loss, it is advantageous to
estimate instantaneous displacements between consecutive US lines instead
of using an initial echo frame as reference. To determine the instantaneous
displacement between RF lines corresponding to consecutive time instances,
phase information was extracted from the US time series. The phase variations
correspond to tissue structures that appear to displace during ablation, see the
curved lines in Fig. 5.10(a). The displacements u (see Fig. 5.10(b)) are tracked
by time-domain cross-correlation:
u(i, n) = arg max cy(n),y(n+1) (i, k),
k
(5.6)
123
5.3. M ETHODS
(a)
(b)
(c)
(d)
Figure 5.10: Illustration of US time series (a), M-scan (c), instantaneous displacement (b) and strain (d) profiles during RF ablation. The black line in (d)
identifies the TEB.
cy1,y2 (i, k) =





W/2−k
P
(y2 (i+w+k)−y2 )(y1 (i+w)−y1 ) k = 0, 1, ..., K
w=−W/2
 W/2+k
P



(y1 (i+w−k)−y1 )(y2 (i+w)−y2 ) k = −1, ..., −K
w=−W/2
(5.7)
where i and n are the calculated sample indexes corresponding to tissue depth
x and ablation time t, respectively.
The 80MHz sampling rate at the known speed of sound corresponds to a spatial resolution of ∼10µm. To achieve robust correlation (R2 > 0.8), a block
length W of 600µm with 150µm steps was used with 75% overlap. Since
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5. L ESION S IZE A SSESSMENT
the displacements between consecutive US lines were typically in the range of
1 − 2 samples, no prior estimates were necessary, which improved robustness.
Excessively large or discontinuous displacements correspond to poor correlation estimates. Using a considerably large search window K of 125µm helped
to identify these artifacts.
The instantaneous strain ε = ∂u/∂x (see Fig. 5.10(d)) was calculated from
the displacement profile by utilizing an extension of the least squares estimator
(LSE) introduced by Kallel & Ophir [1997]. To reduce strain noise by temporal
smoothing, our approach takes account of displacements from adjacent tissue
depths i, as well as from adjacent time lines n:
P P P P
P P
î
ûi − Sw
ûi i
ε(i0 , n) =
n∈N i∈I n∈N i∈I
2
P P
î
n∈N i∈I
P P
− Sw
n∈N i∈I
n∈N i∈I
,
(5.8)
i2 wi2
î = iwi2 ,
(5.9)
ûi = ui wi2 ,
(5.10)
XX
(5.11)
Sw =
wi2 ,
n∈N i∈I
N = [n − KN /2, n + KN /2] ,
(5.12)
I = [i − bKI i/Ic, i − bKI i/Ic + KI ] ,
(5.13)
i0 = i − bKI i/Ic + KI /2.
(5.14)
For standard LSE estimation followed by temporal smoothing of the strain images, noisy u values might lead to unbounded ε. In our method, however,
poor u estimates are simply not included in the sums, which improves the robustness. Kernel sizes of KI = 0.9mm and KN = 2.5s have proven to be
5.3. M ETHODS
125
sufficient for robust tissue depth tracking. The weighting function wi makes it
possible to assign a stronger weight to displacement values close to the strain
calculation depth. Although, in the current study
window wi = 1
a rectangular
2
performed similarly to a Gaussian wi,n = exp − (2/KI i) + (2/KN n)2 κ
of equivalent width. To avoid edge artifacts at small tissue depths, an inhomogeneous i0 tissue depth grid has been introduced, see Eq. 5.14.
The current implementation is based on off-line post-processing of recorded
R
code, which runs on a single core of an
data by our non-optimized Matlab
R
Intel CoreTM i7-4770K CPU. However, a runtime less than 20ms has been
achieved per frame, which allows for real-time implementation of lesion depth
assessment with respect to the frame acquisition rate of 20Hz.
The strain profile in Fig. 5.10(d) reveals a band of high positive strain, what we
name thermal expansion boundary (TEB). It is empirically associated with the
boundary of the coagulated zone in the tissue. The first negative zero-crossing
of the TEB (see Fig.5.11) is hypothesized to be the ablation boundary and can
be tracked as function of ablation time. A linear fit of KN previous instants is
used to estimate the actual lesion size. The residuals provide an automatic way
of detecting a discontinuous or noisy TEB.
Figure 5.11: Instantaneous strain curve corresponding to t=50s in Fig. 5.10(d).
The first negative zero-crossing of the thermal expansion boundary (TEB) is
hypothesized to be the ablation boundary.
126
5.4
5. L ESION S IZE A SSESSMENT
Results
Fig. 5.12 shows optical images acquired after 13 different RFA experiments.
The ablation time was 60s, and no contact force CF control was applied, which
led to significant variations (0.5 ... 3.3mm) in the visually identified ablation
depth Depthvisual . On the basis of the data from this study, we disprove the
use of M-mode images to assess the coagulated tissue front (Fig. 5.13). Although some echogenic features can be observed in samples #2 and #13, these
patterns had already been present before the start of the ablation process, and
are probably associated to heterogenic myocardial structures. A closer examination of the M-scans reveals strong phase distortion during the coagulation
process, it can be observed in sample #6 for t > 20s. Such low echogenicity
domain could indicate diminished backscatter similarly like the IBS estimation of lesion dimensions as mentioned earlier (Zhong et al. [2007]). However,
since this echo intensity change actually stems from phase variation, this phenomenon can be optimally extracted by tracking displacements from US time
series. These observations are in accordance with a large body of literature
(Bush et al. [1993], Hynynen [1997], Maleke & Konofagou [2008]), and provide a possible explanation for the very few studies reporting good visibility of
ablative lesions in echo intensity images (Wright et al. [2011]).
In contrast, the TEI scans (Fig. 5.14) have reproducibly revealed a traceable
thermal expansion boundary (TEB), that correlates well with the visually identified lesion depth. Instead of progressing smoothly in the myocardium, TEB
frequently exhibits a stepwise behavior, for example in samples #5, #6, #7,
#8, #9, #13. A possible explanation for the varying speed of the advancing ablation front may be the cardiac tissue structure. The connective tissue
lining the inner heart and the different bundles of the cardiac muscle cause
echo patterns in Fig. 5.10(c) even before the start of ablation and may form
high-resistance gradients between different tissue domains. As a consequence,
ablating different samples with the same power settings, contact conditions and
for the same time period may result in significant lesion depth variations depending on the tissue layering around the point of contact, for instance samples
#2 to #13. For in vivo heart ablation, the convective heat exchange caused by
blood vessels might make the depth-dependent front propagation speed vary
even more, thereby emphasizing the need for direct imaging methods like ours
to monitor lesion depth.
5.4. R ESULTS
127
Figure 5.12: Tissue optical images acquired after 13 different ablation experiments (ablation time ∼ 60s, no contact force CF control). The visually
identified lesion depths are marked with white arrows.
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5. L ESION S IZE A SSESSMENT
Figure 5.13: M-scan images calculated for 13 different ablation experiments
(ablation time ∼ 60s, no contact force CF control). The dotted lines indicate
the start and end of ablation, whereas the white circle represents the observed
depth at the time of cessation.
5.4. R ESULTS
129
Figure 5.14: TEI images for the experiments of Fig. 5.13. The identified thermal expansion boundary is plotted on the images, together with the visually
identified and US estimated ablation depths.
130
5. L ESION S IZE A SSESSMENT
(a)
(b)
(c)
(d)
Figure 5.15: Linear regression between measured variables. US estimated
DepthU S versus visually identified Depthvisual ablation depth (with undefined contact force CF data of Fig. 5.12 and with CF = 0.04N and CF = 0.4N )
(a), lesion depth for given ablation duration with CF = 0.4N (b), electrical
impedance magnitude decrement -∆Z/Z (c) and phase decrement -∆φ/φ (d).
5.4. R ESULTS
131
As shown in Fig. 5.15(a), TEI achieves a root mean square error (RMSE) of
0.5mm and R2 = 0.85 in the US estimated lesion depth values DepthU S
with respect to the visual ablation observations Depthvisual . There is a tradeoff between TEB contrast and spatial/temporal resolution of its localization
due to the applied strain estimation kernels. The time of flight mapping at a
tissue depth is affected by the temperature dependence of sound speed. As
demonstrated in Fig. 5.6, the speed of sound variation in cardiac tissue during
ablative therapy is below 50m/s, hence the uncertainty of depth estimation is
less than 3%, well below the RMSE.
One problem inherent in a study of this kind is the reliability of visual identification. The soft tissue samples were pressed to the patch electrode during the experiments, whereas after cutting and photographing them they were
not. Another possible source of deformation is the cutting process itself. Even
though the samples were cut by a sharp scalpel, deformation can not be entirely excluded, however, within the ablated area the stiffer tissue yields little
deformation. Pressing the samples against the glass in order to guarantee a
flat surface may also result in deformation. Finally, since there is a smooth
transition between the visually observable coagulated tissue domain and the
non-ablated adjacent region, the visual evaluation of the photos might diminish the accuracy of determination too. The sum of the aforementioned errors
might be as big as the RMSE of the TEI method in our experiments (estimated
to be 0.5mm).
Regulated contact force (CF) improves the reproducibility of lesion depth given
the ablation time, in agreement with the previous findings of Yokoyama et al.
[2008] and Shah et al. [2010]. In comparison with the large Depthvisual variations observed in Fig. 5.14, regulated CF = 0.4N made the Depthvisual values
for the same ablation time lengths (20, 40, ..., 100s) lie closer to each other,
as demonstrated in Fig. 5.15(b). The reproducibility worsens with the lesion
depth, probably because tissue heterogenicity plays a more significant role, as
discussed above.
The change of electrical parameters during ablation shows consistently lower
coefficient of determination R2 than TEI. As demonstrated in Fig. 5.15(c) and
Fig. 5.15(d), the impedance drop (R2 = 0.72) performs better than the phase
decrement (R2 = 0.59) at CF = 0.4N . In addition, electrical parameters are
highly dependent on CF. For instance, with CF = 0.04N , the EI magnitude
varies by 200%, thus R2 drops to R2 < 0.01 for impedance drop and R2 <
0.35 for phase decrement if CF = 0.4N and CF = 0.04N data are evaluated
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5. L ESION S IZE A SSESSMENT
together. In contrast, TEI estimated lesion depth does not seem to be influenced
by CF. The difference between RMSE = 0.46 for CF = 0.4N (see Fig. 5.15(b))
and RMSE = 0.50 if all DepthU S data (with and without force regulation, see
Fig. 5.15(a)) are evaluated together is not significant. These results indicate
that TEI is highly robust to contact force, to ablation time, as well as to tissue
heterogeneity to a large extent.
5.5
Discussion
In order to gain a deeper understanding of the thermal expansion boundary, the
underlying mechanisms have to be discussed. As demonstrated in Section 5.2,
the speed of sound in a uniformly heated tissue sample increases with temperature to 55◦ C where it changes its slope. This is in agreement with previous
observations in muscle tissue (Seo et al. [2011b]; Lu et al. [1996]). A similar
phenomenon can be observed in water, however, in that case the slope change
takes place at ∼ 75◦ C. Accordingly, if SOS variations were the dominant effect
in the strain images, an apparent negative instantaneous strain (SOS increase)
should be observed at the beginning of ablation, followed by a positive strain
for T > 55◦ C (SOS decrease).
The instantaneous strain profile in Fig. 5.16, however, shows a more complex
behavior. Shortly after the start of ablation a high strain domain (A) appears
close to the catheter-tissue interface, where the hottest point is located. As
the temperature further increases, over 55◦ C the same domain shows negative
strain (B). These phenomena can be explained by SOS variations. In contrast,
proceeding with the ablation exhibits high positive strain (C) followed in depth
by a negative strain region (E). It is assumed to be caused by structural changes
indicating tissue coagulation. For (T > 20s) a positive strain band (D) can
be observed, that progresses in accordance with the visually identified lesion
size. We hypothesize that the aforementioned high strain band is associated to
the thermal expansion of the coagulating tissue, similarly to the hypothesis of
Souchon et al. [2005] for HIFU ablated liver tissue. As the distance between
the catheter and reference patch is constant during the experiments, the thermally expanding band compresses the adjacent tissue region, hence negative
strain (E) can be observed below the presumed TEB. As a consequence, the
zero-crossing between the aforementioned positive and negative strain zones is
capable of representing the ablation boundary.
5.5. D ISCUSSION
133
Figure 5.16: Illustration of the instantaneous strain changes during RF ablation. After the start of ablation a high strain domain (A) appears close to the
catheter-tissue interface. Over 55◦ C the same domain shows negative strain
(B), probably because of SOS variations. Proceeding further with the ablation,
structural changes take place due to tissue coagulation, resulting in a positive
strain region (C). For (T > 20s) a positive strain band (D) can be observed,
indicating thermal expansion of the coagulating tissue. This thermally expanding band compresses the adjacent region, resulting in a negative strain domain
(E) deeper in the tissue.
This hypothesis is further supported by the results of an experiment, in which
the time of flight associated with the reflected echo from the patch electrode
was measured through a 10mm thick tissue sample during ablation. Owing to
the fix distance between the catheter and the patch electrode, any time of flight
variation was caused exclusively by aggregated SOS changes through the entire
sample. As shown in Fig. 5.17, initially the cumulative sound speed increases
monotonically, followed by a plateau and change of slope sign, indicating the
beginning of ablation. This observation is consistent with the findings of Seo
et al. [2011b], who attributed the SOS slope change to the start of tissue necrosis. Upon cessation of power delivery the SOS increases suddenly, due to a
permanent increase of stiffness in the necrotic tissue (Eyerly et al. [2010]).
Eventually, the cooling down of non ablated tissue makes the SOS decrease.
134
5. L ESION S IZE A SSESSMENT
Figure 5.17: Effective SOS for the RFA experiment of Fig. 5.7, based on echo
reflection at the patch electrode plate.
6
Conclusion and Future Work
6.1
Summary
One of the main contributions of this thesis was the development of two novel
piezoresistive tri-axial force sensing devices. The proposed sensors can be
manufactured by the combination of conventional fabricating technologies,
electrical discharge machining and the assembly of strain gauges. The high
fabrication expenses of the prototypes can be decreased significantly by automation of the assembly and large scale production. In comparison with other
force sensing devices that employ the same principle, our sensor models are
associated with remarkable mechanical robustness. We introduced two calibration methods that allowed for achieving high angular and magnitudinal accuracy, which makes it possible to use the sensors in any application, in which
both precision and small sensor size play a significant role. Despite their miniature size, the sensors’ measurement performance is comparable to large size,
commercial 6 DoF sensors (e.g. ATI Nano 17). Certain aspects of the sensing performance were favored to others. The Euler-Bernoulli sensor is easy to
manufacture and it has smaller diameter, whereas the Butterfly concept offers
superior isotropy. Hence, we decided to use the latter one for our purposes.
In addition, the design of a RF catheter head with built-in single crystal US
sensor and Butterfly force sensor was presented. The US pulse generation,
signal processing and data acquisition were carried out by using commercially
available devices, whereas other components of the system were our own developments. As commercially available RF ablation tools are expensive and
offer limited synchronization capabilities, we developed a transformer coupled voltage-switching Class-D power amplifier that can be operated in power
control mode. Additionally, it is capable of measuring and recording basic
136
6. C ONCLUSION AND F UTURE W ORK
electrical parameters and allows for simultaneous force data acquisition too.
Synchronized data acquisition of the electrical parameters, force and US data
were implemented on a computer. To provide mechanical support and precise
control over the position of the catheter tip for the purposes of our validation
experiments, we have built a platform consisting of a frame, a linear actuator
and a rotating unit that can be set up in arbitrary direction.
Then, the force sensing capability of the catheter head with soft tissue was
verified. The primary purpose of the built-in multi-axial force sensor is to help
maintain orthogonal catheter tip orientation, that is required in order to ensure
optimal contact conditions between the integrated US sensor and the tissue.
Experiments were performed to verify the multi-axial sensing capabilities of
the catheter head while palpating cardiac tissue. It has been shown that the
same catheter head orientation and position may result in different estimated
angle of incidence depending on how the contact has been established; even so
the orthogonal position can be determined. In conclusion, the force feedback
facilitates maintaining orthogonal tissue contact.
Finally, the developed system was used to perform RF ablation experiments
in vitro with porcine heart samples while simultaneously acquiring electrical
parameters, ultrasound and contact force data. In order to extend the system’s
US imaging capabilities beyond those of the traditional sonography, we have
proposed a method named thermal expansion imaging (TEI) to determine the
instantaneous strain between consecutive US scans. The information gained
from the US time series was compared with the visually identified lesion depth.
6.2
Discussion
Based upon the results presented in this thesis, we disagree with previously
published recommendations that the ablation front can be identified from Mmode intensity images (Wright et al. [2010]). On the other hand, our study
confirms that the lesion depth can be gained from apparent tissue local strains
extracted from the phase information of the US time series.
The results indicate that TEI is highly robust to contact force, to ablation time,
and even to tissue heterogeneity. It has been shown that the ablation front
through heterogeneous tissue such as cardiac muscle progresses discontinuously, thus a direct lesion depth assessment methodology is required for RFA.
6.3. O UTLOOK
137
As the proposed technique allows for direct mapping of myocardial tissue in
function of depth, such parameters as cavitary blood flow or catheter – tissue
contact force are not expected to affect lesion depth assessment significantly.
Consequently, our approach accounts for diminished complications and shows
a high potential for in vivo lesion depth and transmurality assessment.
The results of TEI compare favorably with those originated from the electrical
impedance or contact force (CF) measurements. It has been shown that CF
regulation improved the reproducibility of lesion depth assessment, however
for larger lesion depths the repeatability worsens. Electrical parameters such as
impedance drop and phase change have proven to be highly dependent on CF,
and therefore are not capable of lesion depth assessment. However, monitoring
electrical parameters still plays an important role in preventing potentially lifethreatening phenomena, such as coagulum formation and vaporization.
An additional finding is that the thermal expansion boundary appears to be associated with structural modifications in the tissue due to coagulation, whereas
the effect of sound speed changes is subsidiary. The occurrence of spatially
adjacent positive and negative strain regions strongly suggest that at higher
temperatures thermal expansion is the main factor in the apparent strain, causing compression of the adjacent non-ablated tissue domain.
It can be concluded that the aim of this dissertation, lesion size assessment with
an RF catheter head featuring ultrasound (US) and multi-axial force measurement capabilities, has been achieved.
6.3
Outlook
Even though the results presented in this thesis are promising, there is room for
further improvement. In future research several aspects should be investigated,
including device and application specific ones.
The presented catheter head is not entirely water-resistant, thus special care
was taken to ensure that the integrated force sensor did not submerge in the
saline. Therefore, further improvements such as encapsulation of the force
sensing unit and sealing of the assembly’s joint points have to be undertaken.
Another possible direction of future work is the extension of the system by
employing external US imaging for validation of the TEI method. B-mode
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6. C ONCLUSION AND F UTURE W ORK
scans taken during RF ablation would allow for 2D cross-correlation between
consecutive samples, which may improve the accuracy and robustness of the
instantaneous displacement- and strain estimation. Alternatively, it might be
interesting to explore the potential benefits of replacing the single crystal US
transducer by a US array. Additional focus of future research should be put on
the effects of increasing the resonant frequency of the US transducer. Higher
temporal resolution might lead to enhanced accuracy. The current implementation uses a non-causal strain filter (using future time points) considering offline image processing, however it can easily be made in a fashion only using
past values for real-time monitoring.
The main challenges associated with US based strain imaging applications in
vivo are respiratory- and cardiac motion (Seo et al. [2011a]). As the motioninduced mechanical strains are typically much larger than their thermal counterparts, they should be taken into consideration. The respiratory motion can be
eliminated by holding the patient’s breath during US data acquisition, whereas
cardiac periodicity offers the use of electrocardiogram (ECG) to trigger recording. With this respect, the reliability of TEI imaging while externally introducing motion-induced strain deserves further investigation. Another aspect in
this direction is the change of contact conditions resulting from cardiac muscle contraction, which may diminish the reproducibility of TEI. Whether our
approach will be able to replace currently used lesion assessment methods will
depend highly on how the system performs in case of slight contact position
and orientation variations.
Additionally, the effect of increasing the RF scans’ sampling rate should be
investigated. Higher temporal resolution would improve correlation tracking
robustness, as the signal would decorrelate less between consecutive samples.
Moreover, increased resolution would enable the use of greater filter kernel
size, which would also enhance robustness.
Finally, it deserves further investigation to reveal if the presented method is
applicable to other RFA applications, such as tumor treatment in the lung, liver
or kidney.
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List of Publications
Journal Publication
1. P. Baki, G. Székely, G. Kósa. Design and characterization of a novel,
robust, tri-axial force sensor. Sensors and Actuators A: Physical, 2013.
192(0), 101-110.
2. P. Baki, S.J. Sanabria, G. Kósa, G. Székely, O. Göksel. Thermal expansion imaging for monitoring lesion depth using M-mode ultrasound
during cardiac RF ablation: in vitro study. International Journal of Computer Assisted Radiology and Surgery, 2015. 1-13.
Refereed Conference Proceeding
1. P. Baki, G. Székely, G. Kósa. Miniature tri-axial force sensor for feedback in minimally invasive surgery. In Proceeding IEEE Conference on
Biomedical Robotics and Biomechatronics (BioRob), 2012. 805-810.
2. P. Baki, G. Székely, O. Göksel. Thermal expansion imaging for realtime lesion depth assessment during RF catheter ablation. In Proceeding
International Tissue Elasticity Conference (ITEC), 2013. 70.
Patent
1. P. Baki, G. Kósa and G. Székely. Force Sensor. Submitted by ETH
Zurich in January, 2012. EP12002987.1
Curriculum Vitae
Personal Data
Name
Date of birth
Place of birth
Citizenship
Péter Sándor Baki
4th May 1984
Veszprém, Hungary
Hungarian
Education
2009 – 2015
2002 – 2007
1998 – 2002
ETH Zurich, Computer Vision Laboratory, Switzerland
Doctoral studies
Budapest University of Technology and Economics, Hungary
Studies of Electrical Engineering
Graduation with the degree MSc.
Lovassy László Gimnázium, Veszprém
Work Experience
2015 –
2014 – 2015
2009 – 2013
2008 – 2009
2007 – 2008
Nomoko AG, Switzerland
Prototyping officer
Qiagen Instruments AG, Switzerland
Embedded systems engineer
ETH Zurich, Computer Vision Laboratory, Switzerland
Teaching and research assistant
ETH Zurich, Computer Vision Laboratory, Switzerland
Applied engineer
Holografika kft., Hungary
R&D engineer

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