Quantitative MRI outcome measures in CMT1A using automated lower limb muscle segmentation

Background: Lower limb muscle magnetic resonance imaging (MRI) obtained fat fraction (FF) can detect disease progression in patients with Charcot-Marie-Tooth disease 1A (CMT1A). However, analysis is time-consuming and requires manual segmentation of lower limb muscles. We aimed to assess the responsiveness, efficiency and accuracy of acquiring FF MRI using an artificial intelligence-enabled automated segmentation technique.

Methods: We recruited 20 CMT1A patients and 7 controls for assessment at baseline and 12 months. The three-point-Dixon fat water separation technique was used to determine thigh-level and calf-level muscle FF at a single slice using regions of interest defined using Musclesense, a trained artificial neural network for lower limb muscle image segmentation. A quality control (QC) check and correction of the automated segmentations was undertaken by a trained observer.

Results: The QC check took on average 30 seconds per slice to complete. Using QC checked segmentations, the mean calf-level FF increased significantly in CMT1A patients from baseline over an average follow-up of 12.5 months (1.15%±1.77%, paired t-test p=0.016). Standardised response mean (SRM) in patients was 0.65. Without QC checks, the mean FF change between baseline and follow-up, at 1.15%±1.68% (paired t-test p=0.01), was almost identical to that seen in the corrected data, with a similar overall SRM at 0.69.

Conclusions: Using automated image segmentation for the first time in a longitudinal study in CMT, we have demonstrated that calf FF has similar responsiveness to previously published data, is efficient with minimal time needed for QC checks and is accurate with minimal corrections needed.