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

Zhang, H; Awate, SP; Das, SR; Woo, JH; Melhem, ER; Gee, JC; Yushkevich, PA; (2009) A tract-specific framework for white matter morphometry combining macroscopic and microscopic tract features. Med Image Comput Comput Assist Interv , 12 (Pt 2) 141 - 149.

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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.

Type:Article
Title:A tract-specific framework for white matter morphometry combining macroscopic and microscopic tract features.
Location:Germany
Language:English
Keywords:Algorithms, Brain, Diffusion Tensor Imaging, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Nerve Fibers, Myelinated, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity
UCL classification:UCL > School of BEAMS > Faculty of Engineering Science > Computer Science

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