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.
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.