AI Driven Analysis of MRI To Measure Health and Disease Progression in FSHD

Authors

Lara Riem
Olivia DuCharme
Matthew Cousins
Xue Feng
Allison Kenney
Jacob Morris
Stephen J Tapscott
Rabi Tawil
Jeff Statland
Dennis Shaw
Leo Wang
Michaela Walker
Leann Lewis
Michael A Jacobs
Doris G Leung
Seth D Friedman
Silvia S Blemker

Publication Date

7-5-2024

Journal

Scientific Reports

Abstract

Facioscapulohumeral muscular dystrophy (FSHD) affects roughly 1 in 7500 individuals. While at the population level there is a general pattern of affected muscles, there is substantial heterogeneity in muscle expression across- and within-patients. There can also be substantial variation in the pattern of fat and water signal intensity within a single muscle. While quantifying individual muscles across their full length using magnetic resonance imaging (MRI) represents the optimal approach to follow disease progression and evaluate therapeutic response, the ability to automate this process has been limited. The goal of this work was to develop and optimize an artificial intelligence-based image segmentation approach to comprehensively measure muscle volume, fat fraction, fat fraction distribution, and elevated short-tau inversion recovery signal in the musculature of patients with FSHD. Intra-rater, inter-rater, and scan-rescan analyses demonstrated that the developed methods are robust and precise. Representative cases and derived metrics of volume, cross-sectional area, and 3D pixel-maps demonstrate unique intramuscular patterns of disease. Future work focuses on leveraging these AI methods to include upper body output and aggregating individual muscle data across studies to determine best-fit models for characterizing progression and monitoring therapeutic modulation of MRI biomarkers.

Keywords

MRI, FSHD, Fat fraction, Muscle volume, Progression, Atrophy, Volumetric analysis, Neuromuscular disease, Skeletal muscle, Biomedical engineering

Comments

Supplementary Materials

PMID: 38965267