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Proceedings Paper

Statistical texture analysis based MRI quantification of Duchenne muscular dystrophy in a canine model
Author(s): Jiahui Wang; Zheng Fan; Krista Vandenborne; Glenn Walter; Yael Shiloh-Malawsky; Hongyu An; Joe N. Kornegay; Martin A. Styner
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Paper Abstract

Golden retriever muscular dystrophy (GRMD) is a canine model of Duchenne muscular dystrophy (DMD) that has been increasingly used in both pathogenetic and therapeutic pre-clinical studies. Recent studies have shown that Magnetic resonance imaging (MRI) has great potential to noninvasively assess muscle disorders and has been increasingly used to monitor disease progression in DMD patients and GRMD dogs. In this study, we developed a statistical texture analysis based MRI quantification framework for GRMD. Our system was applied to a database of 45 MRI scans from 8 normal and 10 GRMD dogs in a natural history study. The dogs were longitudinally scanned at 3, 6 and 9 months of age. We first segmented six proximal limb muscles of each dog using a semi-automated, interpolation-based method and then automatically measured the 3D first-order histogram and novel 3D high-order run-length matrix based texture features within each segmented muscle. Our results indicated that MRI texture features has the ability to distinguish the normal and GRMD muscles at each age. Our experimental results demonstrated the potential of MRI texture measurements to serve as biomarkers to distinguish normal and muscular dystrophic muscles in DMD patients.

Paper Details

Date Published: 29 March 2013
PDF: 8 pages
Proc. SPIE 8672, Medical Imaging 2013: Biomedical Applications in Molecular, Structural, and Functional Imaging, 86720F (29 March 2013); doi: 10.1117/12.2006892
Show Author Affiliations
Jiahui Wang, The Univ. of North Carolina at Chapel Hill (United States)
Zheng Fan, The Univ. of North Carolina at Chapel Hill (United States)
Krista Vandenborne, Univ. of Florida (United States)
Glenn Walter, Univ. of Florida (United States)
Yael Shiloh-Malawsky, The Univ. of North Carolina at Chapel Hill (United States)
Hongyu An, The Univ. of North Carolina at Chapel Hill (United States)
Joe N. Kornegay, The Univ. of North Carolina at Chapel Hill (United States)
Texas A&M Univ. (United States)
Martin A. Styner, The Univ. of North Carolina at Chapel Hill (United States)


Published in SPIE Proceedings Vol. 8672:
Medical Imaging 2013: Biomedical Applications in Molecular, Structural, and Functional Imaging
John B. Weaver; Robert C. Molthen, Editor(s)

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