|Fields & Tissues Laboratory|
Ming De Lin
Collaborators: Michael O. Sweeney, M.D. ( Division of Cardiology, Brigham and Women's Hospital, Boston, MA.)
Patient-Specific Modeling of Transvenous Defibrillation
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 metrics. Solutions for seven patient-specific models have been completed. The 3-D models of the thorax and in situ electrodes were created from segmented CT images taken shortly after implant. Each of the 3-D models was created by defining each voxel in the segmented data set as a volume element in the computational model. The electric field distribution during defibrillation was computed using the finite volume method. The critical mass hypothesis was used to define a successful shock and to determine the defibrillation metrics from the calculated field distribution. Simulated defibrillation thresholds yielded good estimates of the clinically determined thresholds in 4 of the 7 patients examined. The model-predicted impedances correlate well with the clinical measurements. These results are promising and provide preliminary support to the potential utility of this modeling approach for patient-specific surgical planning of cardioverter defibrillator implantation and for evaluating new electrode configurations.
A Computational Study of Sequential-Shock Biventricular Defibrillation
Standard transvenous defibrillation is performed with implantable cardioverter defibrillators (ICD) using a dual-current pathway. The defibrillation energy is delivered from the right ventricle (RV) electrode to the superior vena cava (SVC) electrode and the ICD metallic housing. Clinical studies of biventricular defibrillation, which uses an additional electrode, placed on the left ventricular (LV) free wall, in conjunction with sequential shocks, have reported a 50% reduction in defibrillation threshold (DFT) energy. The goal of our study is to use computational methods to examine the biventricular defibrillation fields together with their corresponding DFTs, and to compare to standard defibrillation. Thoracic models derived from 5 patients were used in this study. The computational models were created from segmented CT images. The electric field distribution during defibrillation was computed using the finite volume method. The critical mass hypothesis was used to define a successful shock and to calculate the DFT. Our simulations show that the biventricular lead system reduces the DFT by 30% in comparison to standard configuration in 3 of the models and increases DFT up to 12% in the remaining 2. These results are consistent with clinical reports and suggest that patient-specific computational models may be able to identify those patients who could benefit from biventricular defibrillation.