Diffusion-Based Population Statistics Using Tract Probability Maps
We present a novel technique for the tract-based statistical analysis of diffusion imaging data. In our technique, we represent each white matter (WM) tract as a tract probability map (TPM): a function mapping a point to its probability of belonging to the tract. We start by automatically clustering the tracts identified in the brain via tractography into TPMs using a novel Gaussian process framework. Then, each tract is modeled by the skeleton of its TPM, a medial representation with a tubular or sheet-like geometry. The appropriate geometry for each tract is implicitly inferred from the data instead of being selected a priori, as is done by current tract-specific approaches. The TPM representation makes it possible to average diffusion imaging based features along directions locally perpendicular to the skeleton of each WM tract, increasing the sensitivity and specificity of statistical analyses on the WM. Our framework therefore facilitates the automated analysis of WM tract bundles, and enables the quantification and visualization of tract-based statistical differences between groups. We have demonstrated the applicability of our framework by studying WM differences between 34 schizophrenia patients and 24 healthy controls.
Demian Wassermann Efstathios Kanterakis Ruben C.Gur Rachid Deriche Ragini Verma
Athena project-team, INRIA Sophia Antipolis-Mediterranee, 2004 rt des Lucioles, 06902, FR Section of Biomedical Image Analysis, Radiology, UPENN, PA 19104, USA Department of Psychiatry, University of Pennsylvania Medical Center, PA 19104, USA
国际会议
北京
英文
631-639
2010-09-01（万方平台首次上网日期，不代表论文的发表时间）