A tract-specific framework for white matter morphometry combining macroscopic and microscopic tract features.

Authors

• Hui Zhang
• Suyash P Awate
• Sandhitsu R Das
• John H Woo
• Elias R Melhem
• James C Gee
• Paul A Yushkevich

Publication/Presentation Date

1-1-2009

Abstract

Diffusion tensor imaging plays a key role in our understanding of white matter (WM) both in normal populations and in populations with brain disorders. Existing techniques focus primarily on using diffusivity-based quantities derived from diffusion tensor as surrogate measures of microstructural tissue properties of WM. In this paper, we describe a novel tract-specific framework that enables the examination of WM morphometry at both the macroscopic and microscopic scales. The framework leverages the skeleton-based modeling of sheet-like WM fasciculi using the continuous medial representation, which gives a natural definition of thickness and supports its comparison across subjects. The thickness measure provides a macroscopic characterization of WM fasciculi that complements existing analysis of microstructural features. The utility of the framework is demonstrated in quantifying WM atrophy in Amyotrophic Lateral Sclerosis, a severe neurodegenerative disease of motor neurons. We show that, compared to using microscopic features alone, combining the macroscopic and microscopic features gives a more holistic characterization of the disease.

Volume

12

Issue

Pt 2

First Page

141

Last Page

149

Published In/Presented At

Zhang, H., Awate, S. P., Das, S. R., Woo, J. H., Melhem, E. R., Gee, J. C., & Yushkevich, P. A. (2009). A tract-specific framework for white matter morphometry combining macroscopic and microscopic tract features. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 12(Pt 2), 141–149. https://doi.org/10.1007/978-3-642-04271-3_18

Disciplines

Business Administration, Management, and Operations | Health and Medical Administration | Management Sciences and Quantitative Methods

PubMedID

20426106

Department(s)

Administration and Leadership

Document Type

Article