Evidence map›Paper›PMID 38222819›Full record

ArticleJOR spine2024

Thresholding approaches for estimating paraspinal muscle fat infiltration using T1- and T2-weighted MRI: Comparative analysis using water-fat MRI.

Jessica Ornowski, Lucas Dziesinski, Madeline Hess, Roland Krug, Maryse Fortin, Abel Torres-Espin, Sharmila Majumdar, Valentina Pedoia, Noah B Bonnheim, Jeannie F Bailey

Open access · goldAbstract read
In one paragraph

Article in JOR spine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
2.7field-weighted citation impact, top 10% of its field
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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Spinal mobility and paraspinal muscle degeneration: a cross-sectional analysis from the Berlin Back Study.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026
    Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Myosteatosis: diagnostic significance and assessment by imaging approaches.Quantitative imaging in medicine and surgery · 2024
    Review
  12. Article
  13. Article
  14. Artificial Intelligence in Spine and Paraspinal Muscle Analysis.Advances in experimental medicine and biology · 2024
    Review
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

10 authors at 3 institutions in 2 countries.

Jessica OrnowskiDepartment of Orthopaedic Surgery University of California San Francisco California USA.ORCID https://orcid.org/0000-0002-4259-6506
Lucas DziesinskiDepartment of Orthopaedic Surgery University of California San Francisco California USA.
Madeline HessDepartment of Radiology and Biomedical Imaging University of California San Francisco California USA.ORCID https://orcid.org/0000-0002-3313-932X
Roland KrugDepartment of Radiology and Biomedical Imaging University of California San Francisco California USA.
Maryse FortinDepartment of Health, Kinesiology, and Applied Physiology Concordia University Montreal Québec Canada.
Abel Torres-EspinSchool of Public Health Sciences Faculty of Health University of Waterloo Waterloo Ontario Canada.
Sharmila MajumdarDepartment of Radiology and Biomedical Imaging University of California San Francisco California USA.
Valentina PedoiaDepartment of Radiology and Biomedical Imaging University of California San Francisco California USA.
Noah B BonnheimDepartment of Orthopaedic Surgery University of California San Francisco California USA.ORCID https://orcid.org/0000-0003-2191-180X
Jeannie F BaileyDepartment of Orthopaedic Surgery University of California San Francisco California USA.
University of California, San Francisco · USConcordia University · CAUniversity of Waterloo · CA

Funding

UCSF Core Center for Patient-centric Mechanistic Phenotyping in Chronic Low Back PainU19AR076737 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LOTZ, JEFFREY C. · 2019 to 2023
$34.1M
NIAMS NIH HHS U19 AR076737
6 · The paper itself

Abstract

Background: Paraspinal muscle fat infiltration is associated with spinal degeneration and low back pain, however, quantifying muscle fat using clinical magnetic resonance imaging (MRI) techniques continues to be a challenge. Advanced MRI techniques, including chemical-shift encoding (CSE) based water-fat MRI, enable accurate measurement of muscle fat, but such techniques are not widely available in routine clinical practice. Methods: To facilitate assessment of paraspinal muscle fat using clinical imaging, we compared four thresholding approaches for estimating muscle fat fraction (FF) using T1- and T2-weighted images, with measurements from water-fat MRI as the ground truth: Gaussian thresholding, Otsu's method, K-mean clustering, and quadratic discriminant analysis. Pearson's correlation coefficients ( Results: We found that for all muscles combined, FF measurements from T1- and T2-weighted images were strongly positively correlated with measurements from the water-fat images for all thresholding techniques ( Conclusion: We conclude that muscle FF can be quantified using thresholded T1- and T2-weighted MRI images with relatively low bias and absolute error in relation to water-fat MRI, particularly in the MF and ES, and the choice of thresholding technique should depend on the muscle and clinical MRI sequence of interest.

Indexed as

fat infiltrationlow back painMRImuscle qualityparaspinal musclesthresholdingwater–fat MRI

Identifiers

PMID38222819
PMCPMC10782057
OpenAlexW4389219660

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.