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MRI-based quantification of Duchenne muscular dystrophy in a canine model

Duchenne muscular dystrophy (DMD) is a progressive and fatal X-linked disease caused by mutations in the DMD gene. Magnetic resonance imaging (MRI) has shown potential to provide non-invasive and objective biomarkers for monitoring disease progression and therapeutic effect in DMD. In this paper, we propose a semi-automated scheme to quantify MRI features of golden retriever muscular dystrophy (GRMD), a canine model of DMD. Our method was applied to a natural history data set and a hydrodynamic limb perfusion data set. The scheme is composed of three modules: pre-processing, muscle segmentation, and feature analysis. The pre-processing module includes: calculation of T2 maps, spatial registration of T2 weighted (T2WI) images, T2 weighted fat suppressed (T2FS) images, and T2 maps, and intensity calibration of T2WI and T2FS images. We then manually segment six pelvic limb muscles. For each of the segmented muscles, we finally automatically measure volume and intensity statistics of the T2FS images and T2 maps. For the natural history study, our results showed that four of six muscles in affected dogs had smaller volumes and all had higher mean intensities in T2 maps as compared to normal dogs. For the perfusion study, the muscle volumes and mean intensities in T2FS were increased in the post-perfusion MRI scans as compared to pre-perfusion MRI scans, as predicted. We conclude that our scheme successfully performs quantitative analysis of muscle MRI features of GRMD.