The goal of our research in brain imaging to develop, improve, and apply 3D imaging techniques that reveal changes in the brain. These techniques will help neuroscientists understand neural repair and reorganization in an injured brain, and will be useful for validating drug treatments and rehabilitative therapies. Special focus on pediatrics.

Anuj Srivastava Statistics, FSU.
Stochastic processes. What are physically realistic models of diffusion processes in the brain, and how can they be used to improve the display of white matter tracts from Diffusion Weighted Imaging data?

Alan Evans Neurology and Neurosurgery, McGill University
Brain Mapping Data acquisition for the International Consortium for Brain Mapping.

Louis Collins Neurology and Neurosurgery, McGill University
Detection and quantification of brain atrophy. Image analysis of MRI data with application to Multiple Sclerosis.

Hui Song Computer Science, PhD student
Ion transport. How to use interactive 3D graphics in a simulation of flow of potassium and sodium ions across the membrane of an axon.

Kevin Beason Computer Science, MS student
Tissue-constrained diffusion. Computational 3D simulation of diffusion of water constrained by directionally aligned tissue. The purpose is to model (and validate) tissue structure as measured by Diffusion Weighted Imaging.

Wilfredo Blanco Visualization Laboratory Staff
Hardware acceleration for 3D display. How can hardware shading languages be exploited to accelerate the display of 3D brain data?

Charles Mason Computer Science / Math BS student
3D reconstruction. 3D reconstruction of neurons from tissue slices and confocal microscopy data.
Haptic interaction. How to combine 3D force-feedback output and 3D input to allow physical props (plastic models of brains) to guide the examination of physical data.

Neil Druckmann Computer Science, BS student
Technical Illustration. 3D illustrations for computer graphics.