Generalized Diffusion Simulation-Based Tractography

Qi Zhuang(1), Brian T. Gold(2), Ruiwang Huang(3), Xuwei Liang(1), Ning Cao(1), Jun Zhang(1)
(1)Laboratory for Computational Medical Imaging & Data Analysis
Department of Computer Science
University of Kentucky
773 Anderson Tower
Lexington, KY 40506-0046, USA

(2)Department of Anatomy and Neurobiology,
MN 214 Chandler Medical Center,
University of Kentucky,
Lexington, KY, 40536-0298, USA

(3)MRI/Brain Imaging Physics Group,
Institute of Neuroscience and Biophysics-Medicine (INB-3),
Research Center Juelich, 52425 Juelich, Germany

Abstract

Diffusion weighted imaging (DWI) techniques have been used to study human brain white matter fiber structures in vivo. Commonly used standard diffusion tensor magnetic resonance imaging (DTI) tractography derived from the second order diffusion tensor model has limitations in its ability to resolve complex fiber tracts. We propose a new fiber tracking method based on the generalized diffusion tensor (GDT) model. This new method better models the anisotropic diffusion process in human brain by using the generalized diffusion simulation-based fiber tractography (GDST). Due to the additional information provided by GDT, the GDST method simulates the underlying physical diffusion process of the human brain more accurately than does the standard DTI method. The effectiveness of the new fiber tracking algorithm was demonstrated via analyses on real and synthetic DWI datasets. In addition, the general analytic expression of high order b matrix is derived in the case of twice refocused spin-echo (TRSE) pulse sequence which is used in the DWI data acquisition. Based on our results, we discuss the benefits of GDT and the second order diffusion tensor on fiber tracking.

Key words: Diffusion tensor imaging, High angular diffusion weighted imaging (HARDI), Human brain, White matter, Tractography.

Mathematics Subject Classification:

Download the PDF file zhuang1.pdf.
Technical Report CMIDA-HiPSCCS 009-08, 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. C. Liu for useful discussions during the early phase of this study.