Evidence map›Paper›PMID 33226163›Full record

ArticleMagnetic resonance in medicine2021

Varying diffusion time to discriminate between simulated skeletal muscle injury models using stimulated echo diffusion tensor imaging.

David B Berry, Erin K Englund, Vitaly Galinsky, Lawrence R Frank, Samuel R Ward

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

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  11. Compositional and Functional MRI of Skeletal Muscle: A Review.Journal of magnetic resonance imaging : JMRI · 2024
    Review
  12. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

David B BerryDepartment of Nanoengineering, University of California San Diego, San Diego, California, USA.ORCID 0000-0001-8275-8322
Erin K EnglundDepartment of Orthopaedic Surgery, University of California San Diego, San Diego, California, USA.ORCID 0000-0002-0231-1289
Vitaly GalinskyDepartment of Electrical and Computer Engineering, University of California San Diego, San Diego, California, USA.
Lawrence R FrankCenter for Scientific Computation in Imaging, University of California San Diego, San Diego, California, USA.ORCID 0000-0001-7235-587X
Samuel R WardDepartment of Orthopaedic Surgery, University of California San Diego, San Diego, California, USA.ORCID 0000-0002-4470-155X

Funding

TR&D 4: Revealing Microstructure: Biophysical modeling and validation for discovery and clinical careP41EB017183 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Christopher M Collins · 2014 to 2026
$19.3M
New Quantitative Neuroimaging Metrics of Structural and Functional Connectivity of the Locus Coeruleus as a Novel Biomarker of Alzheimer's Disease Pathogenesis and ProgressionR01AG054049 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BONDI, MARK W, FRANK, LAWRENCE R · 2017 to 2021
$4.1M
Non Invasive Measurements of Muscle Microstructures Assessed by Diffusion Tensor ImagingR01AR070830 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI David Barnes Berry, LAWRENCE R FRANK · 2017 to 2026
$4.0M
NIAMS NIH HHS R01 AR070830NIA NIH HHS R01 AG054049NIBIB NIH HHS P41 EB017183
6 · The paper itself

Abstract

purposeEvaluate the relationship between muscle microstructure, diffusion time (Δ), and the diffusion tensor (DT) to identify the optimal Δ where changes in muscle fiber size may be detected.

methodsThe DT was simulated in models with histology informed geometry over a range of Δ with a stimulated echo DT imaging (DTI) sequence using the numerical simulation application DifSim. The difference in the DT at each Δ between healthy and injured skeletal muscle models was calculated, to identify the optimal Δ at which changes in muscle fiber size may be detected. The random permeable barrier model (RPBM) was used to estimate muscle microstructure from the simulated DT measurements, which were compared to the ground truth.

resultsAcross all models, fractional anisotropy provided greater contrast between injured and control models than diffusivity measurements. Compared to control models, in atrophic injury models, the greatest difference in the DT was found between 90 ms and 250 ms. In models with acute edema, the contrast between injured and control muscle increased with increasing diffusion time, although these models had smaller mean fiber areas. RPBM systematically underestimated fiber size but accurately estimated surface area-to-volume ratio of simulated models.

conclusionThese findings may better inform pulse sequence parameter selection when performing DTI experiments in vivo. If only a single diffusion experiment can be performed, the selected Δ should be ~170 ms to maximize the ability to discriminate between different injury models. Ideally several diffusion times between 90 ms and 500 ms should be sampled in order to maximize diffusion contrast, particularly when the disease process is unknown.

Indexed as

Diffusion Tensor ImagingMuscle, SkeletalAnisotropyDiffusion Magnetic Resonance ImagingMuscle Fibers, Skeletaldiffusion tensor imagingmuscle fiber sizerandom permeable barrier modelskeletal musclestimulated echotime-dependent diffusion

Identifiers

PMID33226163
PMCPMC8204931

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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.