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A computerized MRI biomarker quantification scheme for a canine model of Duchenne muscular dystrophy.
MedLine Citation:
PMID:  23299128     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Purpose   Golden retriever muscular dystrophy (GRMD) is a widely used canine model of Duchenne muscular dystrophy (DMD). Recent studies have shown that magnetic resonance imaging (MRI) can be used to non-invasively detect consistent changes in both DMD and GRMD. In this paper, we propose a semiautomated system to quantify MRI biomarkers of GRMD. Methods   Our system was applied to a database of 45 MRI scans from 8 normal and 10 GRMD dogs in a longitudinal natural history study. We first segmented six proximal pelvic limb muscles using a semiautomated full muscle segmentation method. We then performed preprocessing, including intensity inhomogeneity correction, spatial registration of different image sequences, intensity calibration of T2-weighted and T2-weighted fat-suppressed images, and calculation of MRI biomarker maps. Finally, for each of the segmented muscles, we automatically measured MRI biomarkers of muscle volume, intensity statistics over MRI biomarker maps, and statistical image texture features. Results The muscle volume and the mean intensities in T2 value, fat, and water maps showed group differences between normal and GRMD dogs. For the statistical texture biomarkers, both the histogram and run-length matrix features showed obvious group differences between normal and GRMD dogs. The full muscle segmentation showed significantly less error and variability in the proposed biomarkers when compared to the standard, limited muscle range segmentation. Conclusion   The experimental results demonstrated that this quantification tool could reliably quantify MRI biomarkers in GRMD dogs, suggesting that it would also be useful for quantifying disease progression and measuring therapeutic effect in DMD patients.
Authors:
Jiahui Wang; Zheng Fan; Krista Vandenborne; Glenn Walter; Yael Shiloh-Malawsky; Hongyu An; Joe N Kornegay; Martin A Styner
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2013-1-9
Journal Detail:
Title:  International journal of computer assisted radiology and surgery     Volume:  -     ISSN:  1861-6429     ISO Abbreviation:  Int J Comput Assist Radiol Surg     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2013-1-9     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101499225     Medline TA:  Int J Comput Assist Radiol Surg     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
Department of Psychiatry, University of North Carolina, Chapel Hill, NC, 27599, USA, jh_wang@ieee.org.
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