Muscle percentage index as a marker of disease severity in golden retriever muscular dystrophy

Aydin Eresen, Noor E. Hafsa, Lejla Alic, Sharla M. Birch, John F. Griffin, Joe N. Kornegay, Jim X. Ji*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)


Introduction: Golden retriever muscular dystrophy (GRMD) is a spontaneous X-linked canine model of Duchenne muscular dystrophy that resembles the human condition. Muscle percentage index (MPI) is proposed as an imaging biomarker of disease severity in GRMD. Methods: To assess MPI, we used MRI data acquired from nine GRMD samples using a 4.7 T small-bore scanner. A machine learning approach was used with eight raw quantitative mapping of MRI data images (T1m, T2m, two Dixon maps, and four diffusion tensor imaging maps), three types of texture descriptors (local binary pattern, gray-level co-occurrence matrix, gray-level run-length matrix), and a gradient descriptor (histogram of oriented gradients). Results: The confusion matrix, averaged over all samples, showed 93.5% of muscle pixels classified correctly. The classification, optimized in a leave-one-out cross-validation, provided an average accuracy of 80% with a discrepancy in overestimation for young (8%) and old (20%) dogs. Discussion: MPI could be useful for quantifying GRMD severity, but careful interpretation is needed for severe cases.

Original languageEnglish
Pages (from-to)621-628
Number of pages8
JournalMuscle and Nerve
Issue number5
Publication statusPublished - 1 Nov 2019


  • DMD
  • GRMD
  • imaging biomarkers
  • machine learning
  • muscle percentage index
  • texture


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