Localized MRI and histological image correlation in a canine model of duchenne muscular dystrophy

Contrast-Enhanced Near-Infrared Optical Imaging Detects Exacerbation and Amelioration of Murine Muscular Dystrophy

Assessment of muscle pathology is a key outcome measure to measure the success of clinical trials studying muscular dystrophies; however, few robust minimally invasive measures exist. Indocyanine green (ICG)-enhanced near-infrared (NIR) optical imaging offers an objective, minimally invasive, and longitudinal modality that can quantify pathology within muscle by imaging uptake of ICG into the damaged muscles. Dystrophic mice lacking dystrophin (mdx) or gamma-sarcoglycan (Sgcg^−/−) were compared to control mice by NIR optical imaging and magnetic resonance imaging (MRI). We determined that optical imaging could be used to differentiate control and dystrophic mice, visualize eccentric muscle induced by downhill treadmill running, and restore the membrane integrity in Sgcg^−/− mice following adeno-associated virus (AAV) delivery of recombinant human SGCG (desAAV8hSGCG). We conclude that NIR optical imaging is comparable to MRI and can be used to detect muscle damage in dystrophic muscle as compared to unaffected controls, monitor worsening of muscle pathology in muscular dystrophy, and assess regression of pathology following therapeutic intervention in muscular dystrophies.

Assessment of disease severity in a Canine Model of Duchenne Muscular Dystrophy: Classification of Quantitative MRI

Duchenne muscular dystrophy (DMD) is a fatal Xlinked muscle disorder caused by mutations in the dystrophin gene with a consequence of progressive degeneration of skeletal and cardiac muscle. Golden retriever muscular dystrophy (GRMD) is a spontaneous X-linked canine model of DMD with similar effects. Due to high soft-tissue contrast images, MRI is preferred as a non-invasive method to extract information corresponding to biological characteristics. We propose and evaluate non-invasive MRI-based imaging biomarkers to assess the severity of golden retriever muscular dystrophy (GRMD) using 3T and 4.7T MRI data of nine animals. These imaging biomarkers use first order statistics and texture (assessed by wavelets) in quantitative MRI (qMRI). In a leave-one-sampleout cross-validation framework, we use SVM to differentiate between young and old GRMD animals. The preliminary results show good differentiation between young and old animals for different qMRI sequences and based on a different selection of features.

Tissue classification in a canine model of Duchenne Muscular Dystrophy using quantitative MRI parameters

Duchenne Muscular Dystrophy (DMD) is a genetic disorder caused by dystrophin protein deficiency. Muscle biopsy is the gold standard to determine the disease severity and progression. MRI has shown potential for monitoring disease progression or assessing the treatment effectiveness. In this study, multiple quantitative MRI parameters were used to classify the tissue components in a canine model of DMD disease using histoimmunochemistry analysis as a "ground truth". Results show that multiple MRI parameters may be used to reliably classify the muscular tissue and generate a high-resolution tissue type maps, which can be used as potential non-invasive imaging biomarkers for the DMD.