DTI Fiber Tract-Oriented Quantitative and Visual Analysis
of White Matter Integrity

Xuwei Liang, Ning Cao, Jun Zhang
Laboratory for Computational Medical Imaging & Data Analysis
Department of Computer Science
University of Kentucky
773 Anderson Tower
Lexington, KY 40506-0046, USA


A new fiber tract-oriented quantitative and visual analysis scheme using diffusion tensor imaging (DTI) is developed to study the regional micro structural white matter changes along major fiber bundles which may not be effectively revealed by existing methods due to the curved spatial nature of neuronal paths. Our technique is based on DTI tractography and geodesic path mapping, which establishes correspondences to allow cross-subject evaluation of diffusion properties by parameterizing the fiber pathways as a function of geodesic distance. A novel isonodes visualization scheme is proposed to render regional statistical features along the fiber pathways. Assessment of the technique reveals specific anatomical locations along the genu of the corpus callosum paths with significant diffusion property changes in the amnestic mild cognitive impairment subjects. The experimental results show that this approach is promising and may provide a sensitive technique to study the integrity of neuronal connectivity in human brain.

Key words: Diffusion tensor imaging, tractography, isonodes, geodesic distance, mild cognitive impairment.

Mathematics Subject Classification:

Download the PDF file liang2.pdf.
Technical Report CMIDA-HiPSCCS 002-08, Department of Computer Science, University of Kentucky, Lexington, KY, 2008.

The research work of J. Zhang was supported in part by the US National Science Foundation under grant CCF-0527967 and CCF-0727600, in part by the National Institutes of Health under grant 1R01HL086644-01, in part by the Kentucky Science and Engineering Foundation under grant KSEF-148-502-06-186, and in part by the Alzheimer's Association under grant NIRG-06-25460. The authors would like to thank Dr. Stephen Rose for providing DTI data used in the work.