Biomedical Commons^™

Ablation Of Myocardial Tissue With Nanosecond Pulsed Electric Fields, Fei Xie, Frency Varghese, Andrei G. Pakhomov, Iurii Semenov, Shu Xiao, Jonathan Philpott, Christian Zemlin

Bioelectrics Publications

Background

Ablation of cardiac tissue is an essential tool for the treatment of arrhythmias, particularly of atrial fibrillation, atrial flutter, and ventricular tachycardia. Current ablation technologies suffer from substantial recurrence rates, thermal side effects, and long procedure times. We demonstrate that ablation with nanosecond pulsed electric fields (nsPEFs) can potentially overcome these limitations.

Methods

We used optical mapping to monitor electrical activity in Langendorff-perfused New Zealand rabbit hearts (n = 12). We repeatedly inserted two shock electrodes, spaced 2–4 mm apart, into the ventricles (through the entire wall) and applied nanosecond pulsed electric fields (nsPEF) (5–20 kV/cm, 350 ns duration, …

A Comparative Study Of Two Prediction Models For Brain Tumor Progression, Deqi Zhou, Loc Tran, Jihong Wang, Jiang Li, Karen O. Egiazarian (Ed.), Sos S. Agaian (Ed.), Atanas P. Gotchev (Ed.)

Electrical & Computer Engineering Faculty Publications

MR diffusion tensor imaging (DTI) technique together with traditional T1 or T2 weighted MRI scans supplies rich information sources for brain cancer diagnoses. These images form large-scale, high-dimensional data sets. Due to the fact that significant correlations exist among these images, we assume low-dimensional geometry data structures (manifolds) are embedded in the high-dimensional space. Those manifolds might be hidden from radiologists because it is challenging for human experts to interpret high-dimensional data. Identification of the manifold is a critical step for successfully analyzing multimodal MR images.

We have developed various manifold learning algorithms (Tran et al. 2011; Tran et al. …

Multi-Surface Simplex Spine Segmentation For Spine Surgery Simulation And Planning, Rabia Haq

Computational Modeling & Simulation Engineering Theses & Dissertations

This research proposes to develop a knowledge-based multi-surface simplex deformable model for segmentation of healthy as well as pathological lumbar spine data. It aims to provide a more accurate and robust segmentation scheme for identification of intervertebral disc pathologies to assist with spine surgery planning. A robust technique that combines multi-surface and shape statistics-aware variants of the deformable simplex model is presented. Statistical shape variation within the dataset has been captured by application of principal component analysis and incorporated during the segmentation process to refine results. In the case where shape statistics hinder detection of the pathological region, user-assistance is …