John Triedman Laboratory, Children's Hospital

Implantable Cardiac Defibrillators (ICDs) save the lives of patients with unstable heart rhythms and 100,000 patients receive these devices per year in the US.  Their use in children is less frequent and less standardized than in adults so that determining efficient electrode placement is challenging and uncertain. We are collaborating with J. Triedman, M.D. at Children's Hospital Boston and M. Jolley, M.D. at Stanford University  to develop interactive finite element (FEM) computational models to test electrode locations for their effectiveness in defibrillation in children. The models come from CT or MRI scans segmented into tissue types and then meshed for FEM.  The system also includes a library of realistically shaped ICD case and wire electrodes and an interactive interface allows the user to easily place and move the electrodes in the model to evaluate different implantation locations.  To date we have fully segmented three CT scans, from 2, 10, and 27 year-old subjects, and have created a database of approximately 100 suitable electrode locations per model, which we are testing for bioelectric field strength and homogeneity.  Initial findings have included evaluating the effectiveness of standard locations in adults and novel locations in children.

Scott Makeig, UCSD
Greg Worrell, Mayo Clinic
Simon Warfield, Harvard

The localization of epileptic sources (electric current dipoles) within the human brain is one of the major applications of inverse methods to electroencephalography (EEG). Independent component analysis (ICA) can be used to separate temporal and spatial EEG data into statistically independent components. From those components, Dr. Makeig and his group select just those that correspond to epileptic signals. They performed ICA on the EEG data from scalp surface electrodes and implanted cortical electrodes for an epilepsy patient; for each source component, they solved an inverse problem to locate the single dipole that best reproduced that component. For these patient-specific simulations, the Center built a heterogeneous, isotropic finite-element volume conductor model based on MRI data from the patient.

Craig Henreiquez, Duke University

This project is an extension of a current collaboration with Dr. Henriquez and Duke University. The collaboration explores the feasibility of creating discrete bidomain models at a cellular level in order to study the effects of tissue structure on the propagation of action potentials in cardiac tissue. The most commonly used models for this type of study employ continuous bidomain models (averaging out the intra- and extracellular spaces to form continuous interleaved volumes separated by a membrane). This type of model, however, does not account for the shape and location of the actual membrane or that cells are discrete entities. In order to further analyze the effect of averaging, and more specifically to tie the averaged properties to the underlying tissue pathology, the project aims at using discrete geometric and computational models at a cellular scale to perform simulations of the propagation of the depolarization front in cardiac tissue.

NCMIR Project, UCSD

The goal of this collaboration with the UCSD National Center for Microscopy and Imaging Research (NCMIR) is to build tools for the analysis of electron microscope tomography data sets. NCMIR develops cutting edge three-dimensional imaging and analysis technologies to help biomedical researchers understand biological structure and function relationships in cells and tissues through a range of scales that encompass macromolecular complexes, organelles, and multi-component structures like synapses.

Mario Capecchi Laboratory, Uof U
Charles Keller, CCRI, UTHSCSA

The field of biological imaging is exploding, stemming, in part, from recent developments in imaging. This expansion also results from new scientific and biological applications for such data. These new applications do not necessarily adhere to the conventional clinical paradigm, in which a highly-trained radiologist looks at individual patients and makes specific diagnoses. Instead, biological imaging deals with populations of subjects, and the goal is typically not diagnosis but rather the evaluation of a particular hypothesis through the quantitative evaluation of images.