ReviewNMR in biomedicine2017
Skeletal muscle diffusion tensor-MRI fiber tracking: rationale, data acquisition and analysis methods, applications and future directions.
Review in NMR in biomedicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
61 citing papers in PubMed.
- A diffusion tensor-based method facilitating volumetric assessment of fiber orientations in skeletal muscle.PloS one · 2022Trial
- Trial
- Overcoming pitfalls in multi-stack diffusion MRI for tractography reconstruction of skeletal muscles.Scientific reports · 2026Article
- Architecture of lower leg muscles in children: Reference curves and potential mechanisms of growth.Journal of anatomy · 2026Article
- A Novel 9-ChannelMagnetic resonance in medicine · 2026Article
- Quantitative evaluation of cervical muscles in patients with degenerative cervical myelopathy using diffusion tensor imaging metrics.Bone & joint research · 2026Article
- Diffusion tractography outside the brain: the road less travelled.Brain structure & function · 2026Review
- MRI-based 3D estimation of skeletal muscle architecture and strain during contraction.Journal of biomechanics · 2026Article
- A Comparison of Skeletal Muscle Diffusion Tensor Imaging Tractography Seeding Methods.NMR in biomedicine · 2025Article
- Statistical Shape and Fibre Orientation Model for Muscle Architecture Characterisation of the Medial Gastrocnemius.Annals of biomedical engineering · 2025Article
- Improved Strength Prediction Combining MRI Biomarkers of Muscle Quantity and Quality.NMR in biomedicine · 2025Article
- Improved DTI-Based Skeletal Muscle Architecture Estimation via Diffusion Weighted Image Denoising and First Eigenvector Map Smoothing.NMR in biomedicine · 2025Article
- Quantifying the Fascicular Changes in Recovered Achilles Tendon Patients Using Diffusion Magnetic Resonance Imaging and Tractography.Journal of engineering and science in medical diagnostics and therapy · 2025Article
- Quantifying structural properties of forearm flexor muscles in individuals with hemiparetic cerebral palsy using diffusion tensor imaging.Physiological reports · 2025Article
- From Voxels to Physiology: A Review of Diffusion Magnetic Resonance Imaging Applications in Skeletal Muscle.Journal of magnetic resonance imaging : JMRI · 2025Review
- Alterations in whole muscle quality and physiological cross-sectional area measured with quantitative MRI following ACL injury.Journal of translational medicine · 2025Article
- A registration strategy to characterize DTI-observed changes in skeletal muscle architecture due to passive shortening.PloS one · 2025Article
- Optimal Setup and Parameters of Diffusion-Weighted Magnetic Resonance Imaging for Translational Evaluation of a Tumor Progression Model for Soft Tissue Sarcomas.Advances in radiation oncology · 2025Article
- Anatomy-based diffusion-weighted MRI quality metric: a proof-of-concept for deriving accurate muscle fiber orientation.Physics in medicine and biology · 2024Article
- The impact of diffusion tensor imaging tractography settings on muscle fascicle architecture and diffusion parameter estimates: Tract length, completion, and curvature are most sensitive to tractography settings.NMR in biomedicine · 2024Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
The mechanical functions of muscles involve the generation of force and the actuation of movement by shortening or lengthening under load. These functions are influenced, in part, by the internal arrangement of muscle fibers with respect to the muscle's mechanical line of action. This property is known as muscle architecture. In this review, we describe the use of diffusion tensor (DT)-MRI muscle fiber tracking for the study of muscle architecture. In the first section, the importance of skeletal muscle architecture to function is discussed. In addition, traditional and complementary methods for the assessment of muscle architecture (brightness-mode ultrasound imaging and cadaver analysis) are presented. Next, DT-MRI is introduced and the structural basis for the reduced and anisotropic diffusion of water in muscle is discussed. The third section discusses issues related to the acquisition of skeletal muscle DT-MRI data and presents recommendations for optimal strategies. The fourth section discusses methods for the pre-processing of DT-MRI data, the available approaches for the calculation of the diffusion tensor and the seeding and propagating of fiber tracts, and the analysis of the tracking results to measure structural properties pertinent to muscle biomechanics. Lastly, examples are presented of how DT-MRI fiber tracking has been used to provide new insights into how muscles function, and important future research directions are highlighted. Copyright © 2016 John Wiley & Sons, Ltd.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.