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A statistical model describing combined irreversible electroporation and electroporation-induced blood-brain barrier disruption

Radiology and Oncology
Volume 50 (2016): Issue 1 (March 2016)
By:
Open Access
|Feb 2016

Figures & Tables

Figure 1.

Simulation results. (A) Electric field distribution in the numerical model. The shape of the field assumes a nearly spherical shape. (B) Temperature distribution after 540 pulses. (C) Model geometry including the location of the electrodes (red arrows)
Simulation results. (A) Electric field distribution in the numerical model. The shape of the field assumes a nearly spherical shape. (B) Temperature distribution after 540 pulses. (C) Model geometry including the location of the electrodes (red arrows)

Figure 3. (A)

radii of irreversible damage and BBB disruption calculated from the MRIs, as a function of the number of treatment pulses, and the logarithmic equations fits (B) ratio between rb(N) and rd(N) as a function of number of the number of treatment pulses.
radii of irreversible damage and BBB disruption calculated from the MRIs, as a function of the number of treatment pulses, and the logarithmic equations fits (B) ratio between rb(N) and rd(N) as a function of number of the number of treatment pulses.

Figure 4.

Dependence of Ecd (A) and Ad (B) on the number of treatment pulses. (C) Exponential dependence of Ecd on the number of treatment pulses with N limited to 90 pulses. (D) Correlation between radii obtained from experimental data and radii obtained from the statistical model for IRE. Error bars represent 95% confidence level.
Dependence of Ecd (A) and Ad (B) on the number of treatment pulses. (C) Exponential dependence of Ecd on the number of treatment pulses with N limited to 90 pulses. (D) Correlation between radii obtained from experimental data and radii obtained from the statistical model for IRE. Error bars represent 95% confidence level.

Figure 2.

MRI example. (A) 3 slices of contrast-enhanced T1-weighted MR images of a rat treated with 45 electroporation pulses. The MR images was obtained 30 min post treatment. Each slice is 2mm thick. The enhancing region represents BBB disruption. (B) ROI (green) plotted in the MR image to mark the enhancing region.
MRI example. (A) 3 slices of contrast-enhanced T1-weighted MR images of a rat treated with 45 electroporation pulses. The MR images was obtained 30 min post treatment. Each slice is 2mm thick. The enhancing region represents BBB disruption. (B) ROI (green) plotted in the MR image to mark the enhancing region.

Figure 5.

Dependence of Ecb(A) and Ab(B) on the number of treatment pulses for BBB disruption. Error bars represent 95% confidence level.
Dependence of Ecb(A) and Ab(B) on the number of treatment pulses for BBB disruption. Error bars represent 95% confidence level.

Figure 6.

Electrical field thresholds. (A) IRE thresholds. Dashed line represents published IRE thresholds for white matter for 80 50 μ s pulses at 4 Hz. (B) BBB disruption thresholds. Dashed line represent previously published threshold for 90 50 μ s pulses at 4 Hz.5(C) Thresholds for E(S = 0) for the IRE and E(S = 1) for BBB disruption. (D) Ratio between E(S = 1) and E(S = 0) for IRE and E(BBB = 0) and E(BBB = 1 ) for BBB disruption. Error bars are smaller than markers.
Electrical field thresholds. (A) IRE thresholds. Dashed line represents published IRE thresholds for white matter for 80 50 μ s pulses at 4 Hz. (B) BBB disruption thresholds. Dashed line represent previously published threshold for 90 50 μ s pulses at 4 Hz.5(C) Thresholds for E(S = 0) for the IRE and E(S = 1) for BBB disruption. (D) Ratio between E(S = 1) and E(S = 0) for IRE and E(BBB = 0) and E(BBB = 1 ) for BBB disruption. Error bars are smaller than markers.

Average radii of IRE and BBB disruption for each treatment group_ Each group of 5–7 rats was treated with different number of pulses (10–540) at 600V, 50μ s pulses at 1Hz

# of pulses104590180270450540
IRE radius (mm)0.62 ± 0.151.35 ± 0.180.89 ± 0.201.42 ± 0.151.37 ± 0.161.92 ± 0.071.80 ± 0.21
BBB disruption radius (mm)1.25 ± 0.061.74 ± 0.041.84 ± 0.072.54 ± 0.152.19 ± 0.142.84 ± 0.042.69 ± 0.12

Material properties used for numerical model

Brainσ - basic conductivity0.258[S/m]
k - Thermal conductivity0.0565[W/(m*K)]
Cp - Heat capacity3680 [J/(kg*K)]
- density1039 [kg/m^3]
Q’’’- metabolic heat generation10437 [W/m^3]
T - temperature37°C
BloodCp-heat capacity3840 [J/(kg*K)]
density1060 [kg/m^3]
Wb-Perfusion rate7.15E-3 [1/s]
copperσ - basic conductivity5.998E7 [S/m]
k - thermal conductivity400 [W/(m*K)]
Cp heat capacity385 [J/(kg*K)]
- Density8700 [kg/m^3]
Silverσ - basic conductivity6.273E7 [W/m^3]
k - thermal conductivity429 [W/(m*K)]
Cp heat capacity234 [J/(kg*K)]
- Density10500 [kg/m^3]
DOI: https://doi.org/10.1515/raon-2016-0009 | Journal eISSN: 1581-3207 | Journal ISSN: 1318-2099
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Language: English
Page range: 28 - 38
Submitted on: Oct 23, 2015
Accepted on: Jan 3, 2016
Published on: Feb 16, 2016
Published by: Association of Radiology and Oncology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
electroporarion,
blood brain barrier,
Peleg-Fermi
Related subjects:
Medicine,
Clinical medicine,
Internal medicine,
Haematology, oncology,
Radiology

© 2016 Shirley Sharabi, Bor Kos, David Last, David Guez, Dianne Daniels, Sagi Harnof, Yael Mardor, Damijan Miklavcic, published by Association of Radiology and Oncology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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