Abstract
BackgroundLittle is known about the disease distribution and severity detected by T1-mapping in Duchenne muscular dystrophy (DMD). Furthermore, the correlation between skeletal muscle T1-values and clinical assessments is less studied. Hence, the purposes of our study are to investigate quantitative T1-mapping in detecting the degree of disease involvement by detailed analyzing the hip and thigh muscle, future exploring the predicting value of T1-mapping for the clinical status of DMD.MethodsNinety-two DMD patients were included. Grading fat infiltration and measuring the T1-values of 19 pelvic and thigh muscles (right side) in axial T1-weighted images (T1WI) and T1-maps, respectively, the disease distribution and severity were evaluated and compared. Clinical assessments included age, height, weight, BMI, wheelchair use, timed functional tests, NorthStar ambulatory assessment (NSAA) score, serum creatine kinase (CK) level. Correlation analysis were performed between the muscle T1-value and clinical assessments. Multiple linear regression analysis was conducted for the independent association of T1-value and motor function.ResultsThe gluteus maximus had the lowest T1-value, and the gracilis had the highest T1-value. T1-value decreased as the grade of fat infiltration increased scored by T1WI (P < 0.001). The decreasing of T1-values was correlated with the increase of age, height, weight, wheelchair use, and timed functional tests (P < 0.05). T1-value correlated with NSAA (r = 0.232-0.721, P < 0.05) and CK (r = 0.208-0.491, P < 0.05) positively. T1-value of gluteus maximus, tensor fascia, vastus lateralis, vastus intermedius, vastus medialis, and adductor magnus was independently associated with the clinical motor function tests (P < 0.05). Interclass correlation coefficient (ICC) analysis and Bland-Altman plots showed excellent inter-rater reliability of T1-value region of interest (ROI) measurements.ConclusionT1-mapping can be used as a quantitative biomarker for disease involvement, further assessing the disease severity and predicting motor function in DMD.
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