Overcoming pitfalls in multi-stack diffusion MRI for tractography reconstruction of skeletal muscles

Abstract Accurate in vivo characterization of skeletal muscle structure is essential for understanding muscle function, assessing pathologies, and developing musculoskeletal models. Magnetic resonance imaging (MRI) and diffusion MRI enable visualization of muscle morphology and fibre architecture in vivo, but imaging skeletal muscles often requires an extended field of view. At large distances from the magnet isocentre, gradient non-linearities and magnetic field inhomogeneities can cause severe image distortions. Here, we present multi-stack structural and diffusion-tensor echo-planar images of the lower limb using selected stack lengths and acquisition parameters to provide realistic examples. Geometric distortions, fractional anisotropy maps, and tractography-reconstructed fibre tracts are used to demonstrate anatomical plausibility and consistency across overlapping slices. In the presented examples, pronounced distortions, compromised fat suppression, and fibre reconstruction errors are observed under off-isocentre imaging conditions. While shorter stack lengths may help reduce distortions, our examples also indicate that factors such as shimming strategy, participant positioning, and post-processing can influence image quality. Our work highlights key pitfalls in muscle diffusion MRI, providing practical guidance grounded in MRI physics to support the design of multi-stack MRI protocols. These considerations support more reliable multi-stack diffusion MRI for musculoskeletal research and biomechanical modelling.

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Publication Details

Journal
Scientific Reports
Published
2026-07-22
DOI
https://doi.org/10.1038/s41598-026-63269-6
Primary Topic
Advanced Neuroimaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
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article

Overcoming pitfalls in multi-stack diffusion MRI for tractography reconstruction of skeletal muscles

Manuela Zimmer, Flavio Dell’Acqua, Paul Condron, Samantha Holdsworth et al.
Scientific Reports
Advanced Neuroimaging Techniques and Applications
article

Overcoming pitfalls in multi-stack diffusion MRI for tractography reconstruction of skeletal muscles

Manuela Zimmer, Flavio Dell’Acqua, Paul Condron, Samantha Holdsworth, Filiz Ateş, Geoffrey Handsfield
article en

Abstract

Abstract Accurate in vivo characterization of skeletal muscle structure is essential for understanding muscle function, assessing pathologies, and developing musculoskeletal models. Magnetic resonance imaging (MRI) and diffusion MRI enable visualization of muscle morphology and fibre architecture in vivo, but imaging skeletal muscles often requires an extended field of view. At large distances from the magnet isocentre, gradient non-linearities and magnetic field inhomogeneities can cause severe image distortions. Here, we present multi-stack structural and diffusion-tensor echo-planar images of the lower limb using selected stack lengths and acquisition parameters to provide realistic examples. Geometric distortions, fractional anisotropy maps, and tractography-reconstructed fibre tracts are used to demonstrate anatomical plausibility and consistency across overlapping slices. In the presented examples, pronounced distortions, compromised fat suppression, and fibre reconstruction errors are observed under off-isocentre imaging conditions. While shorter stack lengths may help reduce distortions, our examples also indicate that factors such as shimming strategy, participant positioning, and post-processing can influence image quality. Our work highlights key pitfalls in muscle diffusion MRI, providing practical guidance grounded in MRI physics to support the design of multi-stack MRI protocols. These considerations support more reliable multi-stack diffusion MRI for musculoskeletal research and biomechanical modelling.

Scientific Reports
University of Stuttgart (DE), University of North Carolina at Chapel Hill (US), University of Auckland (NZ), Wellcome Centre for Human Neuroimaging (GB), Medical Research Institute of New Zealand (NZ), Mātai Medical Research Institute
Openalex Percentile: Top 8%
Advanced Neuroimaging Techniques and Applications
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