Evaluation of normalized T1 signal intensity obtained using an automated segmentation model in lower leg MRI as a...

We evaluated the potential utility of imaging parameters derived by normalizing muscle signal intensity on T1-weighted lower leg MRIs in Charcot-Marie-Tooth disease type 1 A (CMT1A) patients, using a deep learning-based automated muscle segmentation model. We retrospectively analyzed lower leg MRI data of 107 CMT1A patients. An automated deep learning-based muscle segmentation model was employed to extract muscle signal intensities from four compartments (anterior, lateral, deep posterior, and superficial posterior) of the lower leg. Mean normalized signal intensities (MNSI) were calculated by dividing the mean signal intensity of each segmented muscle compartment by the reference signal intensity for each patient. Correlations between MNSIs and clinical parameters (Charcot-Marie-Tooth Neuropathy Score version 2, functional disability scale [FDS] score, 10-m walk test time, and 9-hole peg test time) were assessed using partial correlation analysis adjusting for age and body mass index. The MNSIs of the anterior, lateral, deep posterior, and superficial posterior compartments of the lower legs, as well as the total MNSI, showed significant positive correlations with all clinical measures, suggesting that higher MNSI values are associated with more severe disease (p < 0.05). The strongest correlation was observed between the MNSI of anterior compartment and FDS score (r = 0.57). MNSIs of the muscle compartments in lower leg MRI, obtained using an automated segmentation model, demonstrated significant correlations with clinical parameters in CMT1A patients.