Evidence map›Paper›PMID 39257789›Full record

ArticlebioRxiv : the preprint server for biology2024

A Comparison of Skeletal Muscle Diffusion Tensor Imaging Tractography Seeding Methods.

Bruce M Damon, Roberto Pineda Guzman, Carly A Lockard, Xingyu Zhou

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Bruce M DamonCarle Clinical Imaging Research Program, Stephens Family Clinical Research Institute, Carle Health, Urbana IL USA 61801.ORCID 0000-0002-2581-302X
Roberto Pineda GuzmanCarle Clinical Imaging Research Program, Stephens Family Clinical Research Institute, Carle Health, Urbana IL USA 61801.
Carly A LockardCarle Clinical Imaging Research Program, Stephens Family Clinical Research Institute, Carle Health, Urbana IL USA 61801.
Xingyu ZhouCarle Clinical Imaging Research Program, Stephens Family Clinical Research Institute, Carle Health, Urbana IL USA 61801.

Funding

Development and Application of Muscle Diffusion Tensor MRIR01AR073831 · NIAMS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI DAMON, BRUCE M. · 2019 to 2024
$2.1M
NIAMS NIH HHS R01 AR073831
6 · The paper itself

Abstract

The internal arrangement of a muscle's fibers with respect to its mechanical line of action (muscle architecture) is a major determinant of muscle function. Muscle architecture can be quantified using diffusion tensor magnetic resonance imaging-based tractography, which propagates streamlines from a set of seed points by integrating vectors that represent the direction of greatest water diffusion (and by inference, the local fiber orientation). Previous work has demonstrated that tractography outcomes are sensitive to the method for defining seed points, but this sensitivity has not been fully examined. To do so, we developed a realistic simulated muscle architecture and implemented four novel methods for tract seeding: seeding along the muscle-aponeurosis boundary with an updated procedure for rounding seed points prior to lookup in the muscle boundary mask and diffusion tensor matrix (APO-3); voxel-based seeding throughout the muscle volume at a user-specified spatial frequency (VXL-1); voxel-based seeding throughout the muscle volume at a variable spatial frequency (VXL-2), and seeding near external and internal muscle boundaries (VXL-3). We then implemented these methods in an example human dataset. The updated aponeurosis seeding procedures allow more accurate and robust tract propagation from seed points. The voxel-based seeding methods had quantification outcomes that closely matched the updated aponeurosis seeding method. Further, the voxel-based methods can accelerate the overall workflow and may be beneficial in high throughput analysis of multi-muscle datasets. Continued evaluation of these methods in a wider range of muscle architectures is warranted.

Indexed as

DTIfiber-trackingfreewaremuscle architecturesimulationskeletal muscle

Identifiers

PMID39257789
PMCPMC11383979

What OpenQuestion holds

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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.