Effects of Lipid‐Induced Magnetic Microstructure on Fat Fraction Quantification in Muscular Dystrophies
Pierre‐Yves Baudin, Harmen Reyngoudt, Valentina Schunk, Sina Graf, Anna‐Lena Mayer, Anika Starke, Frank Roemer, Regina Trollmann, Matthias Türk, Arnd Dörfler, Michael Uder, Armin M. Nagel, Susanne S. Rauh, Elisabetta Gazzerro, Benjamin Marty, Teresa GerhalterABSTRACT
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 R 2 * 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 R 2 * 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.