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Mechanical property changes associated with inflammation, necrosis, fibrosis, and fatty deposition in dystrophic muscle may be quantitatively evaluated by Quantitative Viscoelastic Response (QVisR) ultrasound. QVisR uses a machine learning (ML) framework that takes as input tissue displacement in response to two consecutive and co-located acoustic radiation force (ARF) excitations and yields as output estimates of shear elastic and shear viscous moduli. QVisR imaging was performed in the vastus lateralis (VL) muscles of 11 boys with Duchenne muscular dystrophy (DMD) aged 5 to 12 years and of 8 age-matched boys with no known neuromuscular disorders, who served as controls. QVisR measures of elastic moduli differed between DMD and control VL in boys aged less than six years and six-to-seven years. Similarly, QVisR measures of viscous moduli differed between DMD and control VL in boys aged six-to-seven years. These results demonstrate that QVisR measures of elastic and viscous moduli differentiate dystrophic from control muscle. The findings suggest that QVisR may be relevant to monitoring dystrophic muscle degeneration and response to intervention, particularly in early stages when interventions are most likely to be impactful.

Quantitative Viscoelastic Response (QVisR) ultrasound is a new machine learning-based elasticity imaging method in which elastic and viscous moduli are estimated from tissue deformation in response to two consecutive acoustic radiation force (ARF) excitations. The estimated moduli are rendered into two-dimensional parametric images of elastic and viscous modulus. From QVisR images, the spatial distribution of elastic and viscous properties may be evaluated using established computational texture analysis tools. In this study, computational texture analysis by the omnidirectional gray-level run-length matrix (GLRLM) was applied to QVisR images. The images were obtained in the vastus lateralis (VL) muscles of 11 boys with Duchenne muscular dystrophy, aged 5-12 years, and 8 age-matched boys with no known neuromuscular disorders, who served as controls. GLRLM-derived elastic entropy in QVisR images was statistically higher (Wilcoxin, p