Evidence map›Paper›PMID 39698646›Full record

ArticleQuantitative imaging in medicine and surgery2024

Reproducibility of proton density fat fraction assessment of thigh muscle in a multi-site, multi-vendor cohort study at 10 years after anterior cruciate ligament reconstruction.

Brendan L Eck, Sibaji Gaj, Richard Lartey, Mei Li, Jeehun Kim, Carl S Winalski, William Zaylor, Dongxing Xie, Ria Tilve, Kevin D Harkins and 12 more

Abstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Motion-robust proton density fat fraction andMagma (New York, N.Y.) · 2026
    Article
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

22 authors.

Brendan L EckProgram for Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA.ORCID https://orcid.org/0000-0003-0971-4432
Sibaji GajProgram for Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA.ORCID https://orcid.org/0000-0002-6997-5717
Richard LarteyProgram for Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA.ORCID https://orcid.org/0000-0002-8336-7363
Mei LiProgram for Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA.ORCID https://orcid.org/0000-0003-3915-4210
Jeehun KimProgram for Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA.
Carl S WinalskiProgram for Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA.
William ZaylorProgram for Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA.
Dongxing XieProgram for Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA.
Ria TilveDepartment of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
Kevin D HarkinsDepartment of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.ORCID https://orcid.org/0000-0003-3579-9273
Bruce M DamonDepartment of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.ORCID https://orcid.org/0000-0002-2581-302X
Laura J HustonDepartment of Orthopaedic Surgery, Vanderbilt University Medical Center; Nashville, TN, USA.ORCID https://orcid.org/0000-0002-9901-7165
Faysal AltahawiProgram for Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA.
Nancy A ObuchowskiProgram for Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA.
Xiaodong ZhongDepartment of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Kecheng LiuMR R&D Collaborations, Siemens Medical Solutions USA, Inc., Cleveland, OH, USA.
Harry FrielMR Clinical Science, Philips Healthcare, Highland Heights, OH, USA.ORCID https://orcid.org/0000-0003-0399-9641
Dimitrios C KarampinosInstitute of Diagnostic and Interventional Radiology, School of Medicine and Heath, Technical University of Munich, Munich, Germany.ORCID https://orcid.org/0000-0003-4922-3662
Michael V KnoppWright Center of Innovation in Biomedical Imaging, The Ohio State University, Columbus, OH, USA.
Morgan H JonesProgram for Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA.ORCID https://orcid.org/0000-0002-5466-0624
Kurt P SpindlerProgram for Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA.
Xiaojuan LiProgram for Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA.ORCID https://orcid.org/0000-0002-0567-9935

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Dixon-based magnetic resonance imaging (MRI) intramuscular proton density fat fraction (PDFF) is a potentially useful imaging biomarker of muscle quality. However, multi-vendor, multi-site reproducibility of intramuscular PDFF quantification, required for large clinical studies, can be strongly dependent on acquisition and processing. The purpose of this study was (I) to develop a 6-point Dixon MRI-based acquisition and processing technique for reproducible multi-vendor, multi-site quantification of thigh intramuscular PDFF; and (II) to evaluate the ability of the technique to detect differences in thigh muscle status between operated Methods: MRI bilateral mid-thigh data acquisition at 3T was harmonized across three sites and two vendors and included high-resolution axial T1-weighted scans and 6-point Dixon scans. Centralized, vendor-independent PDFF quantification was performed and algorithms were evaluated in phantoms to determine the most reproducible approach. A novel image post-processing method was developed to mitigate scaling errors observed on some scanner platforms to improve reproducibility. PDFF measurements in phantoms and control subjects including traveling controls were obtained for assessment of intra-scanner repeatability as well as inter-scanner, inter-vendor, and inter-site reproducibility. Patients from the Multicenter Orthopedic Outcomes Network ACLR cohort were scanned and intramuscular PDFF was compared between thigh muscles of the operated and contralateral limbs. Standard deviation (SD) of PDFF, within-subject SD (wSD), and intraclass correlation coefficient (ICC) were used to characterize repeatability and reproducibility. Results: The proposed scaling correction method improved overall reproducibility in phantoms and traveling controls and was incorporated as part of the Dixon processing pipeline for subsequent analyses. Intra-scanner phantom repeatability ranged between 0.2-0.9% (SD) PDFF (ICC =0.98-1.00), with overall inter-vendor/inter-site reproducibility of 0.7-1.7% (SD) PDFF (ICC =0.97). Control subject repeatability among all scanners and vendors ranged between 0.2-0.8% (wSD) PDFF (ICC =0.95-0.98) with slightly lower inter-site, inter-vendor reproducibility, 0.8-1.2% (wSD) PDFF (ICC =0.92). Intramuscular PDFF was elevated in ACLR vs contralateral thighs for the hamstrings muscle compartment (6.2%±3.5% Conclusions: Reproducible multi-site, multi-vendor intramuscular PDFF measurement is enabled by 6-point Dixon MRI with standardized acquisition and processing. The method is sensitive enough to detect differences in muscle groups between operated and non-operated thighs in the patient population 10 years after ACLR.

Indexed as

anterior cruciate ligament reconstruction (ACLR)chemical shift imagingDixonFat fractionreproducibility

Identifiers

PMID39698646
PMCPMC11651994

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Registered trials

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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.