Introduction

The processing and analysis of diffusion weighted imaging data, is a task considered to be very challenging due to the complex underlying properties of the data, but is becoming more mature owing to major new research contributions from various fields like physics, mathematics, statistics, computing and visualization. Prompted by its growing contribution to disease investigation, there has been an increased interest in addressing the mathematical and technical issues associated with the analysis of such data, by development of sophisticated techniques for the same – a trend that is evident in recent research. In particular, the tutorial would like to address the current divide between the very limited methodology available to clinical research and advanced methodology developed by several research groups.

This tutorial will provide researchers with a comprehensive overview of the current state-of-the-art in all facets of DWI data: its acquisition, processing, analysis and application to clinical data. This will broadly cover aspects related to:

1. acquisition such as the study of appropriate noise models, linear and nonlinear methods for regularization of raw data and the estimation of tensors or more complex models
2. methods in computational neuro-anatomy of diffusion images such as deformable registration, modeling of white matter structure via techniques based on tractography, manifold learning, clustering and PDEs, amongst others
3. methods for comprehensive group-based analysis of diffusion data involving group-wise registration and a framework for high dimensional statistical analysis and hypothesis testing. These methodological advances will also be discussed from the perspective of clinical applications.

This tutorial targets fundamental issues crucial to researchers involved in developing and applying image processing and analysis to DWI data. Potential candidates are graduate students, postdoctoral students, computational researchers and clinically oriented researchers who would like to get a deeper insight in understanding the properties of the data and appropriate methodological framework to be applied to this data.

Organizers

Carl-Fredrik Westin, PhD
Director, Laboratory of Mathematics in Imaging (LMI)
Department of Radiology, Harvard Medical School
Brigham and Women's Hospital, Boston
westin(at)bwh.harvard.edu

Guido Gerig, PhD
Dept. of Computer Science
Dept. of Psychiatry
SCI Institute
University of Utah, Salt Lake City
gerig(at)sci.utah.edu

Ragini Verma, PhD
Section of Biomedical Image Analysis, Dept. of Radiology
University of Pennsylvania, Philadelphia
ragini.verma(at)uphs.upenn.edu