Annals of Biomedical Engineering (in review)

Patient-Specific Computational Analysis of Transvenous Defibrillation: A

Comparison to Clinical Metrics in Humans

Daniel Mocanu1, Joachim Kettenbach2, Michael O. Sweeney3, Ron Kikinis2, Bruce H.

KenKnight4, and Solomon R. Eisenberg1

1 Department of Biomedical Engineering, Boston University,

44 Cummington Street, Boston, MA 02215, USA

2 Surgical Planning Laboratory, Brigham and Women’s Hospital,

75 Francis Street, Boston, MA 02215, USA

3 Cardiac Pacing and Implantable Device Therapies, Brigham and Women’s Hospital,

75 Francis Street, Boston, MA 02215, USA

4 Heart Failure Research, Guidant Corporation, St. Paul, MN, USA

Abstract: 220 words

Text: 19 pages

Figures: 18

Tables: 2

Abbreviation: Patient-specific modeling of defibrillation

Correspondence: Solomon R. Eisenberg, Sc. D., Department. of Biomedical Engineering, Boston

University, Boston, MA 02215. Tel: (617) 353-4749, Fax: (617) 353-5929, e-mail: sre@bu.edu

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Abstract

The goal of this study is to assess the predictive capacity of computational models of

transvenous defibrillation by comparing the results of patient-specific simulations to clinically

determined defibrillation thresholds (DFT). Nine three-dimensional patient-specific models of the

thorax and in situ electrodes were created from segmented CT images taken after implantation of the

cardioverter-defibrillator. The defibrillation field distribution was computed using the finite volume

method. The DFTs were extracted from the calculated field distribution using the 95% critical mass

criterion. The model-predicted DFT energies were well matched to the clinically determined values

in four of the nine patients examined (rms difference =1.5 J; correlation coefficient = 0.84). For the

remaining five patients the rms difference was 18.4 J with a correlation coefficient of 0.85.

Inspection of the weak field distribution revealed that patients with the highest clinical DFT exhibit

the most compact weak field regions. The predictive capacity of the patient-specific models was

improved substantially when the compactness of the weak field was used to inform the choice of the

inexcitability threshold used with the critical mass criterion (rms difference =4 J; correlation

coefficient = 0.93). The models were then able to identify the high-threshold patients, and closely

predict their DFTs.

Key Terms

Transvenous defibrillation, critical mass, patient-specific, modeling, finite volume method

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Introduction

Ventricular fibrillation (VF) is a severe heart arrhythmia that can lead to sudden cardiac

death if not treated promptly. The only effective clinical intervention to extinguish VF is electrical

defibrillation. The implantable cardioverter defibrillator (ICD) is an electronic device designed to

detect the onset of VF and to shock the heart back into normal sinus rhythm. ICDs have been shown

to be very effective in protecting against sudden cardiac death and have become the treatment of

choice for patients with drug-resistant arrhythmias. In the standard configuration, the ICD is

surgically implanted subcutaneously in the patient’s chest, with two catheter electrodes inserted in

the superior vena cava (SVC) and the right ventricle (RV) (fig. 1). Typically, the shock energy is

delivered via a dual-current pathway from the RV electrode to the SVC electrode and the metallic

enclosure (CAN) of the ICD. Determining energy settings during ICD implantation requires

repetitive sequences of induction and termination of VF to find the defibrillation threshold (DFT)

energy16. Since each VF induction has some element of risk, including the possibility of non-

conversion and death, the ICD implantation procedure would be substantially improved if an

accurate estimate of the patient’s DFT energy requirement was available prior to implant.

Anatomically realistic computational models have been used previously to model and

explore the current distribution produced by defibrillation shocks1,6,12,18. Jorgenson et al.11 tested the

accuracy of simulated defibrillation fields in six pig thoracic models and found a good correlation

between model predicted voltages and the experimental data measured at 52 sites inside the thorax.

Panescu et al.17 also found a good correlation between measured voltages on the thorax surface and

finite element model predictions. These results suggest that computational models with realistic

geometries can closely simulate the field and current distributions produced by defibrillation shocks.

The critical mass criterion25 has been used in a number of modeling studies7,14,15 to define

successful defibrillation and to extract defibrillation metrics from the calculated field distribution.

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This criterion states that a minimum electric field Eth is necessary throughout a critical mass of the

ventricular myocardium to terminate fibrillation activation waves. These investigators have reported

model DFT values that fall within one standard deviation of the mean DFTs reported in human

clinical studies.

The success of these computational studies suggested that similar computational models may

be useful in the presurgical planning of ICD implantation by providing an estimate of the DFT for

individual patients. Thus, the goal of our research was to assess the predictive capacity of patient-

specific computational models of transvenous defibrillation by comparing patient-specific simulated

and clinical defibrillation metrics. In contrast with previous modeling studies, which have been

based on a single average human thorax, we used a set of nine computational models built from

segmented CT images of patients with an implanted ICD. The clinical defibrillation parameters were

determined for each patient at the time of implantation and then used in the evaluation of the

modeling results. We also present an analysis of the distribution of the weak field and its

relationship to the DFT.

Methods

A. Clinical DFT testing

Nine patients were recruited for implant (patient demographics shown in table 1). ICD

implantation and DFT testing procedures followed a standardized clinical protocol. In all but one of

the patients, the Endotak catheter lead system (Guidant/CPI) was implanted. A similar lead system

(Medtronic) was used in the remaining patient. The ICDs were implanted in the left pectoral region

with venous access via the subclavian vein (fig. 1). Fluoroscopic imaging was used to verify the

correct lead placement. Clinical DFT testing was performed following a step-down procedure20. VF

was induced by applying a pulse of 60 Hz alternating current and the defibrillation shock was

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delivered 10 seconds later. The defibrillation waveform had a biphasic shape, with 60% tilt in the

positive phase and 50% tilt in the negative phase (fig. 2). The corresponding pulse width of the first

and second phases were 60% and 40%, respectively, of the total pulse duration (10-15 milliseconds).

Typical trials started at 20 J (stored energy) and decremented until VF was no longer terminated

using the following step-down scheme: 20J→15J→10J→8J→5J. In cases where a 20 J shock could

not defibrillate, the energy was gradually increased in 5 J increments until defibrillation was

achieved. The ICD output was programmed at the minimum energy that successfully defibrillated

on two occasions, single confirmation, plus a safety margin. The single confirmation could be

consecutive, i.e. two consecutive at 5 J, or by way of a single reversal, i.e. 8 J success, 5 J failure, 8 J

success. However, for the purposes of the modeling study, the minimum delivered energy that

defibrillated on a single occasion was defined as the DFT and used in the comparison with model-

predicted values.

B. CT image acquisition and segmentation

All patients were scanned post-implant on a spiral X-ray CT system (Somatom Plus 4,

Siemens Medical Systems, NJ), with the ICD metallic enclosure, SVC and RV electrodes in place.

The imaging protocol was approved by the Internal Review Board at Brigham and Women’s

Hospital, Boston, and each patient gave informed consent. The scanning procedure13 was

customized for high resolution image acquisition (120 kV, 200 mA, matrix size 512 x 512, 2-3.5

mm/sec table feed). For better differentiation of soft tissues, a contrast agent was delivered to each

patient prior to imaging (Omnipaque 300; Iohexol 300 mg I/ml; Nycomed, Norway). To provide

images of high contrast and a low noise level, an anisotropic filter was applied prior to the

segmentation. Each segmented label was overlaid to the corresponding grayscale images to ensure

accurate positioning. If the results of the semi-automated segmentation were not satisfying, manual

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fine-editing was done with MrX (General Electric Medical Systems, Milwaukee, WI) using a Sun

workstation (UltraSPARC, Sun Microsystems Inc., Mountain View, CA). The following tissue types

were identified: fat, blood, heart muscle, thoracic wall muscle, lung, bone (fig. 3). The RV and SVC

electrodes together with the ICD metallic enclosure were also identified.

C. Image-based computational model

For each patient, the 3-D computational model was constructed directly from the segmented

images (128×128 pixels) using a structured meshing algorithm in which the segmented voxels in the

image data sets were defined as volume elements in the computational domain (fig. 4). The size of

each volume element was roughly 3×3×6 mm3, with slight variations depending on individual

patient geometrical features. The total number of elements in the models varied between 350,000

and 450,000. Electrical conductivities9,21 were assigned to the six tissue types according to table 2.

D. Conduction boundary value problem

In the quasistatic approximation of the electromagnetic field10, the electric potential

distribution during a defibrillation shock is governed by Laplace’s equation subject to Dirichlet

boundary conditions on the electrodes (RV, SVC and CAN) and Neumann homogeneous boundary

condition (no flux) on the thoracic surface. The equation was solved numerically by the finite

volume method using I-DEAS software package (Integrated Design and Engineering Analysis

Software, Structural Dynamics Research Corporation, Milford, OH, USA).

E. Calculation of defibrillation metrics

Four defibrillation metrics were calculated to interpret and evaluate the solutions:

interelectrode impedance Z, the DFT energy, the DFT current Ith and the DFT voltage Vth. For each

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patient-specific simulation, the critical mass hypothesis25 was used to define successful defibrillation

with minimum delivered energy. According to this, a successful shock must expose a critical mass

of the ventricular myocardium to electric fields equal to or greater than the inexcitability threshold

Eth. Following Zhou24, we used a critical mass of 95% and an Eth = 3.5 V/cm (biphasic waveform),

which corresponds to successful defibrillation ~80% of the time. In the simulations, a unit voltage

was applied between the RV cathode and SVC and CAN anodes. The resulting electric field

magnitudes were then scaled so that 95% of the ventricular myocardium was exposed to a minimum

electric field magnitude of Eth = 3.5 V/cm. The simulated DFT voltage and the DFT current are the

corresponding scaled values of the applied voltage and the resulting delivered current. The DFT

energy was calculated based on features of the Guidant/CPI biphasic waveform (fig. 2) by

integrating the power over the pulse duration:

26.1

0

/22

48.0 thtth CVdte

ZV

DFT == ∫ −τ

τ (1)

where Z is the patient-specific interelectrode impedance, Vth is the DFT voltage, C is the capacitance

(150 µF) and τ = ZC is the time constant.

F. Statistical Analysis

Correspondence between the clinical and model predicted defibrillation metrics was assessed

using the correlation coefficient (cc) relating the predicted to measured values and the root mean

square difference (rms) between the predicted and measured values. The paired t-test was used to

compare clinical and predicted DFT energy, and a value of p < 0.05 was used to establish

statistically significant differences between the clinical and model-predicted DFTs.

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Results

Figure 5 shows the clinical DFT testing at the time of ICD implantation for all patients.

Successful trials are shown with empty circles (Yes), while defibrillation failures are shown with

solid circles (No). A demographic profile of the patient population used in this study is shown in

table 1.

The comparison between the model-predicted and clinical interelectrode impedances resulted

in a rms difference of 8.2 Ω and a correlation coefficient of 0.37 (fig. 6). In all patients but one

(patient #1), the model impedances overestimate the measured impedances with cc = 0.7 (fig. 7).

Patient-specific clinical and model-predicted DFT energy values are compared in fig. 8,

where the clinical values correspond to the lowest energy shock that defibrillated on a single

occasion. The overall rms difference (rms) was 12.4 J and the correlation coefficient was cc = 0.05.

Model-predicted DFTs were good estimates of the clinically determined thresholds in patients #1-4

(figs. 8, 9), with rms = 1.5 J and cc = 0.84. The paired t-test for patients #1-4 yielded a p-value of

0.41, indicating that there was not a statistically significant difference between clinical and model-

predicted DFTs. For the second group of patients (#5 - 9), the simulations underestimated the

clinical DFT energy with rms = 18.4 J, cc = 0.85. For patients #5-9, the paired t-test gave p = 0.01,

indicating a statistically significant difference between the clinical and predicted DFT (figs. 8,10).

Similar trends were observed for the DFT voltages and the DFT currents (figs. 11,12).

We examined the electric field magnitude distribution associated with the clinical

defibrillation threshold for each patient by applying the clinically measured DFT voltage to the

corresponding model. The resulting cumulative histograms of the ventricular field magnitudes are

shown in fig. 13. We also examined the distribution of the weak field in each patient (fig. 14-16),

where the weak field is defined as the lowest 5% (< 3.5 V/cm) of the electric field magnitudes in the

ventricles. Volume elements in the weak field are indicated by a black cone (fixed volume) placed in

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the center of each volume element. The size of the largest cluster (percentage of ventricle volume) is

also shown for each patient.

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Discussion

Our study investigates the degree to which patient-specific computational models can predict

defibrillation threshold for individual patients. To our knowledge, this is the first study to directly

compare clinical and model-predicted DFTs on a patient-specific basis.

The first step in our simulations was to compute the electric field distribution produced by a

defibrillation shock using the finite volume method. We believe that this computational technique,

like many other numerical methods (e.g. finite element method, finite difference method), is able to

closely simulate the electric field distribution in the heart and thorax provided the model geometry

closely approximates the patient’s anatomy and accurate tissue conductivity values are used. The

second step of the simulation was to extract the defibrillation thresholds from the calculated cardiac

electric fields. To do this, we used the critical mass criterion, a simple phenomenological model of

defibrillation. Although debated3,19, the critical mass hypothesis for defibrillation is supported by

experimental evidence8,23,25 and provides a simple means to relate the shock field at the continuum

scale to the cellular-based events through which defibrillation is achieved.

The interelectrode impedance is independent of the defibrillation assumptions used and is

determined by the geometry and tissue properties. In all patients but one (patient #1) the model-

predicted impedance overestimates the measured impedance (to a lesser extent for patient #5) (fig.

6). The reasonable correlation obtained between the predicted and clinically measured values (fig. 7)

suggests the presence of a systematic error, which may originate in the tissue conductivity values

used or in the reconstruction of the anatomical structure. Overestimates of the measured impedance

were also obtained by Jorgenson et al.11 for their subject-specific finite element models based on

similar mesh resolution and tissue conductivity values.

Model predicted DFTs were well matched to the clinically determined DFTs in four of the

nine patients examined (first group: patients #1 - 4). It is important to note that the respective

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clinical DFT energy values for these patients spanned an approximately 2-fold range. This suggests

that the goodness of the achieved match was not due to the similarity of the patients examined but is

a reflection of the ability of the modeling approach to capture the inherent anatomical variations in

this group of patients. For this group the difference between clinical and predicted values was not

statistically significant (p = 0.41), indicating that the achieved match was not random. The clinical

DFTs of the remaining five patients (second group: patients #5 - 9) were not well matched by the

simulated DFTs. We could not identify a consistent set of patient characteristics (e.g. heart volume,

heart disease or drug regimen) that were able to differentiate between these two groups (table 1).

The cumulative histograms of the ventricular field magnitudes corresponding to the clinical

DFTs (fig 13) suggest that the inexcitability threshold Eth varies significantly between the two

groups of patients. While patients #1-4 cluster around Eth= 3.5 V/cm, patients #5, 7, 8, and 9 cluster

around 7.5 V/cm. Furthermore, the computed field distributions for three of the four patients that

require Eth = 7.5 V/cm shared a common feature associated with the distribution of the weak field in

the ventricles. This will be discussed next.

In all our simulations, the weak field is located in the posterolateral left ventricle (figs 14-

16). This result is in agreement with experimental studies22, and further supports the capability of

the computational approach to simulate electric field distributions during defibrillation shocks.

These small regions, exposed to the weakest potential gradients, are thought to play a key role in the

reinitiation of fibrillation2,5.

Our simulations show interesting differences in the patterns in the weak field distribution

that suggest a possible connection between the dispersion of the weak field and the clinical DFT.

More specifically, the weak field regions for patients that exhibited the highest clinical DFTs (fig.

14) were the most focal and compact (patients #7 - 9), and consisted of a single contiguous region.

Patients with more moderate clinical DFTs (#4, 6) had weak field regions that were still rather focal,

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but aggregated into two separate clusters (fig. 15), with the largest being ~90% of the weak field

region. Finally, patients with the lowest clinical DFTs (#1-3, 5) exhibited a scattered distribution of

the weak field regions (fig. 16). For these patients, the largest cluster is less than 90%, with the

exception of patient #3. Hence, patients with the most compact weak field regions were the most

difficult to defibrillate, and exhibited the highest clinical DFTs.

These results suggest that large and compact weak field regions favor the reinitiation of

fibrillation, and that patients with weak fields exhibiting this feature require a higher shock energy

to defibrillate. This correspondence may provide a predictive means of identifying high DFT

patients prior to implant. The histograms in fig. 13 show that patients #7 – 9, the patients exhibiting

the most compact weak field, require a minimum electric field strength of 7.5 V/cm to defibrillate.

We rescaled the electric field magnitudes for these patients to Eth = 7.5 V/cm (instead of 3.5 V/cm)

and recomputed the DFT energies. The entire population was then compared to the clinical values

(figs 17, 18). The overall rms difference was reduced from 12.4 J to 4 J and the correlation

coefficient increased from 0.05 to 0.93. Hence, the predictive capacity of the patient-specific models

was improved substantially when the compactness of the weak field in the ventricles was used to

inform the choice of Eth used in conjunction with the 95% critical mass criterion: patients exhibiting

a single contiguous weak field region required Eth = 7.5 V/cm; Eth = 3.5 V/cm was used for all other

patients.

Limitations

The modeling approach used in this study uses a simple phenomenological model to relate

the computed field distributions to defibrillation threshold values. Factors relating to the underlying

cellular electrophysiology enter the model only through the inexcitability threshold Eth and the

critical mass criterion (expressed as a percentage of the ventricular mass). To the extent that the

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empirically-based model captures normative behavior, the model cannot be expected to predict

defibrillation parameters for patients with cellular electrophysiology that differs substantially from

the norm with a single Eth. Similarly, we did not account for the presence of infarct regions,

myocardial ischemia or the effect of patient drug regimens in our models.

The modeling approach is also inherently deterministic, and ignores the known probabilistic

nature of defibrillation. Additionally, we have treated the lowest energy that defibrillates for each

patient as the corresponding DFT, without taking into account the discrete nature of the clinical

search protocol used to find the DFT. The truly lowest defibrillation energy could be somewhat

lower than that found by the search protocol.

Conclusions

This paper presents comparisons between model predicted and clinical defibrillation metrics

determined for individual patients with implanted ICDs. Model predicted parameters were extracted

from the calculated cardiac electric field distribution using the 95% critical mass criterion and an

inexcitability threshold of 3.5 V/cm for biphasic waveforms. The patient-specific simulations

produced good estimates of the clinical DFTs in four of the nine patients investigated. Clinical DFTs

and model predictions were well correlated for both well-matched and poorly-matched groups of

patients. The 95% critical mass criterion and the inexcitability threshold of 3.5 V/cm used to extract

defibrillation parameters was not able to consistently predict individual patient thresholds.

However, inspection of the weak field distribution in all nine patients revealed a relationship

between the weak field distribution and the clinical DFT that could potentially be exploited to

identify high DFT patients prior to implant. The predictive capacity of the patient-specific models

was improved substantially when the compactness of the weak field in the ventricles was used to

inform the choice of Eth used with the 95% critical mass criterion. When an Eth = 7.5 V/cm was used

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for patients exhibiting a single contiguous weak field region, and an Eth = 3.5 V/cm was used for all

other patients, the overall rms difference between the clinical and model predicted DFTs was

reduced from 12.4 J to 4 J and the correlation coefficient increased from 0.05 to 0.93. With this

modification to the critical mass criterion, the models were able to identify the high threshold

patients, and closely predict their DFTs. A larger patient sample is necessary to confirm the

hypothesis that patients with a compact weak field distribution require a larger Eth to defibrillate.

Acknowledgements. This study was supported in part by a grant from Guidant Corporation., St. Paul,

MN, and the Trustees of Boston University. The authors would like to thank to Dr. Michael Benser

from the Cardiac Rhythm Management Laboratory (Guidant Corporation) for his valuable

suggestions and comments. The excellent support of the Scientific Computing and Visualization

Group at Boston University is also acknowledged.

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Table 1: Patient demographics. Patient

# Gender Age Heart volume*

(cm3) Heart

disease Heart

arrhythmia Drugs

1 M 66 610 CAD Primary prevention B-blocker

2 M 58 530 CAD Spontaneous sustained VT

Amiodarone Digitalis

B-blocker

3 M 65 500 CAD VF or cardiac arrest B-blocker

4 M 73 644 CAD Primary prevention Amiodarone

5 M 35 545 NDCP Spontaneous syncopal VT −

6 M 76 680 NDCP Spontaneous syncopal VT Digitalis

7 F 65 477 CAD VF or cardiac arrest

Digitalis B-blocker

8 M 81 667 CAD Spontaneous sustained VT −

9 M 48 694 NDCP Spontaneous sustained VT

Amiodarone B-blocker

CAD – coronary artery disease; NDCP – nonischemic dilated cardiomyopathy; VF – ventricular fibrillation; VT – ventricular tachycardia. *The heart volume was estimated based on the number and volume of the heart voxels in the segmented CT images.

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Table 2: Tissue conductivities.

Tissue type Electrical conductivityσ (mS/cm)

Heart muscle 2.5

Thoracic wall muscle 2.5

Blood 8

Lung 0.7

Bone 0.1

Fat 0.5

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Figure 1: X-ray image showing the ICD metallic enclosure and the catheter RV and SVC

electrodes.

Figure 2: CPI/Guidant biphasic defibrillation waveform with 60% tilt in the positive phase and 50%

tilt in the negative phase. The pulse width of the first phase is 60% of the total pulse duration

(typically 10-15 msec)

Figure 3: Segmented CT image.

Figure 4: Voxel-based finite volume mesh. For clarity, only the bone structure and the lungs are

shown.

Figure 5: Clinical DFT testing for all nine patients. = successful defibrillation attempt; = failed

defibrillation attempt. For the modeling purposes, the lowest energy that defibrillated on a single

occasion was defined as the DFT energy.

Figure 6: Model-predicted and clinical interelectrode impedances.

Figure 7: Regression of model-predicted and clinical impedances.

Figure 8: Model-predicted and clinical DFT energy. The overall rms difference and correlation

coefficient were 12.4 J and 0.05 respectively.

Figure 9: Regression of the model-predicted and clinical DFT energy for the first group of patients

(#1 – 4).

Figure 10: Regression of the model-predicted and clinical DFT energy for the second group of

patients (#5 – 9).

Figure 11: Model-predicted and clinical DFT voltages.

Figure 12: Model-predicted and clinical DFT currents.

Figure 13: Cumulative histograms for the electric field distribution. In the models, the electric field

magnitude were scaled to obtain the clinical DFT voltage.

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Figure 14: Weak field distribution for patients with high clinical DFT. Patient #7: DFT=19 J (the

ICD pulse generator and the catheter are not shown for this patient); patient #8: DFT=27 J; patient

#9: DFT=30 J.

Figure 15: Weak field distribution for patients with moderate clinical DFT. Patient #4: DFT=9.8 J;

patient #6: DFT=14 J.

Figure 16: Weak field distribution for patients with low clinical DFT. Patient #1: DFT=5 J; patient

#2: DFT=4.8 J; patient #3: DFT=7.4 J; patient #5: DFT=7.7 J.

Figure 17: Model-predicted and clinical DFT energy. The electric field magnitude for patients #7-9

were scaled such that 95% of the ventricles was exposed to a minimum electric field of 7.5 V/cm.

Figure 18: Regression of the model-predicted and clinical DFT energy. An Eth = 7.5 V/cm was used

for patients #7 – 9.

22

Figure 1

Vth

V=V eth-t/τ

Figure 2

23

Figure 3

Figure 4

24

Figure 5

25

Figure 6

Figure 7

26

Figure 8

Figure 9

27

Figure 10

Figure 11

Figure 12

28

Figure 13

Figure 14

29

Figure 15

30

Figure 16

31

Figure 17 (p = 0.12)

Figure 18