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.