Fully Automated Whole-Leg multiparametric quantitative MRI processing, segmentation and analysis.

Motivation: Quantitative magnetic resonance imaging (qMRI) is a common tool for assessing neuromuscular disorders, but its quantitative parameters often lack specificity and generally do not directly relate to muscle function. Goal(s): In our ongoing MOTION study, we are collecting whole-leg qMRI data and assessing muscle structure, function, and lifestyle in a large cross-sectional cohort to identify confounding factors in qMRI evaluation. Approach: To streamline data analysis for this cohort, we've developed a fully automated muscle-Bids-based data analysis pipeline, including automated muscle segmentation. Results: Here, we introduce our data analysis pipeline, demonstrated using repeated scans of one volunteer. Impact: The implementation of fully automated qMRI data processing streamlines large-scale studies and enhances its integration into clinical workflows. This standardization we expect to reduces variability for more dependable and reproducible outcomes.