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Proceedings Paper

Robust multi-component modeling of diffusion tensor magnetic resonance imaging data
Author(s): Yasser M. Kadah; Xiangyang Ma; Stephen LaConte; Inas Yassine; Xiaoping Hu
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Paper Abstract

In conventional diffusion tensor imaging (DTI) based on magnetic resonance data, each voxel is assumed to contain a single component having diffusion properties that can be fully represented by a single tensor. In spite of its apparent lack of generality, this assumption has been widely used in clinical and research purpose. This resulted in situations where correct interpretation of data was hampered by mixing of components and/or tractography. Even though this assumption can be valid in some cases, the general case involves mixing of components resulting in significant deviation from the single tensor model. Hence, a strategy that allows the decomposition of data based on a mixture model has the potential of enhancing the diagnostic value of DTI. This work aims at developing a stable solution for the most general problem of multi-component modeling of diffusion tensor imaging data. This model does not include any assumptions about the nature or volume ratio of any of the components and utilizes a projection pursuit based strategy whereby a combination of exhaustive search and least-squares estimation is used to estimate 1D projections of the solution. Then, such solutions are combined to compute the multidimensional components in a fast and robust manner. The new method is demonstrated by both computer simulations and real diffusion-weighted data. The preliminary results indicate the success of the new method and its potential to enhance the interpretation of DTI data sets.

Paper Details

Date Published: 14 April 2005
PDF: 12 pages
Proc. SPIE 5746, Medical Imaging 2005: Physiology, Function, and Structure from Medical Images, (14 April 2005); doi: 10.1117/12.596155
Show Author Affiliations
Yasser M. Kadah, Cairo Univ. (Egypt)
Xiangyang Ma, Emory Univ./Georgia Tech (United States)
Stephen LaConte, Emory Univ./Georgia Tech (United States)
Inas Yassine, Cairo Univ. (Egypt)
Xiaoping Hu, Emory Univ./Georgia Tech (United States)

Published in SPIE Proceedings Vol. 5746:
Medical Imaging 2005: Physiology, Function, and Structure from Medical Images
Amir A. Amini; Armando Manduca, Editor(s)

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