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Updated: Aug 15, 2026

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
Published on: December 18, 2016
Effects of Lipid-Induced Magnetic Microstructure on Fat Fraction Quantification in Muscular Dystrophies
Pierre-Yves Baudin1, Harmen Reyngoudt1, Valentina Schunk2
1Institute of Myology, Neuromuscular Investigation Center, NMR Laboratory, Paris, France.
Purpose:
To study the impact of mesoscopic magnetic susceptibility heterogeneity on chemical shift-encoded (CSE) proton density fat-fraction (PDFF) quantification in muscular dystrophies, a subgroup of neuromuscular disorders.
Theory And Methods:
In MRI, extramyocellular lipid deposits induce orientation-dependent Larmor frequency variations due to microstructural anisotropy, resulting in spatially varying frequency shifts between fat and water and increased transverse relaxation rates. A newly developed PDFF quantification method accounting for resonance shifts and dual R2* rates was applied on standard 6-point CSE acquisitions of Duchenne (n = 15), Becker (n = 31), and facioscapulohumeral (n = 30) muscular dystrophy patients, and control subjects (n = 40). The impact of frequency shifts, decay functions, and lipid models on PDFF estimation was systematically assessed.
Results:
Accounting for resonance shifts resulted in large PDFF quantification differences compared to a reference method (-3.8% [-14.8%, 7.2%]), significantly improved fitting quality (Bayesian Information Criterion (BIC) difference ≥ 10), and reduced fat/water separation artifacts, confirming predictions by numerical simulations. Bias and variability due to the lipid model were reduced to less than 1%. Fitting quality in high R2* regions was further improved using a dual relaxation model with linear/quadratic decay (BIC difference ≥ 2). Sensitivity to change was improved on the tested cohorts (SRM increased by 0.18). DTI-estimated angular dependencies reflected theoretical and numerical predictions for elongated axially symmetric lipid deposits.
Conclusion:
The proposed approach improvements could enhance the PDFF quantification reliability in neuromuscular disorders studies and support more accurate monitoring of myosteatosis.

