Fiber Direction Estimation in Diffusion MRI

Abstract:

The recent advancement of neuroimaging technology has generated a huge amount of brain imaging data. These images are not only large, but also complex. They carry the mission of understanding the incredibly complicated brain structures. The analysis of such data poses many challenging statistical problems that require both accurate modeling and fast algorithms.

In this talk, I will focus on diffusion magnetic resonance imaging (dMRI), which is an emerging medical imaging technique for probing anatomical architectures of biological samples. It is widely used to reconstruct white matter fiber tracts in brains. I will begin by introducing dMRI data and a technique called diffusion tensor imaging (DTI) that is commonly used for analyzing these dMRI data. Then I will explain why most existing DTI methods would fail in regions when multiple fibers share the same voxel (a 3D imaging pixel). To overcome this issue, I will introduce a fundamentally different route to reconstruct crossing fibers.  An important ingredient is a nonconventional spatial smoothing procedure that can be applied to improve the estimates of diffusion direction. Finally, I will illustrate the proposed technique on a real dMRI data set.