Evidence map›Paper›PMID 39126439›Full record

ArticleMagma (New York, N.Y.)2024

Towards assessing and improving the reliability of ultrashort echo time quantitative magnetization transfer (UTE-qMT) MRI of cortical bone: In silico and ex vivo study.

Soo Hyun Shin, Dina Moazamian, Qingbo Tang, Saeed Jerban, Yajun Ma, Jiang Du, Eric Y Chang

Abstract read
In one paragraph

Article in Magma (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Soo Hyun ShinDepartment of Radiology, University of California San Diego, 9452 Medical Center Drive, La Jolla, CA, USA. shs033@health.ucsd.edu.ORCID http://orcid.org/0000-0003-1875-2908
Dina MoazamianDepartment of Radiology, University of California San Diego, 9452 Medical Center Drive, La Jolla, CA, USA.
Qingbo TangRadiology Service, Veterans Affairs San Diego Healthcare System, 3350 La Jolla Village Drive, San Diego, CA, USA.
Saeed JerbanDepartment of Radiology, University of California San Diego, 9452 Medical Center Drive, La Jolla, CA, USA.
Yajun MaDepartment of Radiology, University of California San Diego, 9452 Medical Center Drive, La Jolla, CA, USA.
Jiang DuDepartment of Radiology, University of California San Diego, 9452 Medical Center Drive, La Jolla, CA, USA.
Eric Y ChangDepartment of Radiology, University of California San Diego, 9452 Medical Center Drive, La Jolla, CA, USA. ericchangmd@gmail.com.

Funding

Ultrashort Echo Time (UTE) Magnetic Resonance Imaging of BoneR01AR068987 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI DU, JIANG · 2015 to 2024
$4.9M
Ultrashort Echo Time Magnetic Resonance Imaging of the Lumbar Intervertebral Disc.R01AR079484 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Yajun Ma · 2022 to 2026
$2.7M
Quantitative UTE MR Imaging of Myelin: Novel Biomarkers for Alzheimer's DiseaseRF1AG075717 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI DU, JIANG · 2022 to 2022
$2.3M
Knee evaluation under mechanical loading by cones ultrashort echo time MR imagingK01AR080257 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI saeed jerban · 2022 to 2026
$602k
Multi-scale MRI Assessment of Bone Quality and Function in a Chronic Rat Spinal Cord Injury ModelI01BX005952 · VA · VA SAN DIEGO HEALTHCARE SYSTEM · PI Eric Y Chang · 2023 to 2026
–
Developing MRI Biomarkers of Myelin and Iron in Veterans with Traumatic Brain InjuryI01CX002211 · VA · VA SAN DIEGO HEALTHCARE SYSTEM · PI Jiang Du · 2021 to 2026
–
Quantitative Magnetic Resonance Imaging of Human Rotator Cuff TendonI01CX001388 · VA · VA SAN DIEGO HEALTHCARE SYSTEM · PI CHANG, ERIC Y · 2016 to 2024
–
BLRD VA I01 BX005952CSRD VA I01 CX001388CSRD VA I01 CX002211NIAMS NIH HHS K01 AR080257NIAMS NIH HHS R01 AR068987NIAMS NIH HHS R01 AR079484NIA NIH HHS RF1 AG075717NIH HHS K01AR080257NIH HHS R01AR068987NIH HHS R01AR079484NIH HHS RF1AG075717University of California, San Diego RG114138U.S. Department of Veterans Affairs I01BX005952U.S. Department of Veterans Affairs I01CX001388U.S. Department of Veterans Affairs I01CX002211
6 · The paper itself

Abstract

objectiveTo assess and improve the reliability of the ultrashort echo time quantitative magnetization transfer (UTE-qMT) modeling of the cortical bone. MATERIALS AND

methodsSimulation-based digital phantoms were created that mimic the UTE-qMT properties of cortical bones. A wide range of SNR from 25 to 200 was simulated by adding different levels of noise to the synthesized MT-weighted images to assess the effect of SNR on UTE-qMT fitting results. Tensor-based denoising algorithm was applied to improve the fitting results. These results from digital phantom studies were validated via ex vivo rat leg bone scans.

resultsThe selection of initial points for nonlinear fitting and the number of data points tested for qMT analysis have minimal effect on the fitting result. Magnetization exchange rate measurements are highly dependent on the SNR of raw images, which can be substantially improved with an appropriate denoising algorithm that gives similar fitting results from the raw images with an 8-fold higher SNR. DISCUSSION: The digital phantom approach enables the assessment of the reliability of bone UTE-qMT fitting by providing the known ground truth. These findings can be utilized for optimizing the data acquisition and analysis pipeline for UTE-qMT imaging of cortical bones.

Indexed as

AlgorithmsComputer SimulationCortical BoneImage Processing, Computer-AssistedMagnetic Resonance ImagingPhantoms, ImagingSignal-To-Noise RatioAnimalsRatsReproducibility of ResultsCortical boneDigital phantomMRIQuantitative magnetization transfer (qMT)Ultrashort echo time (UTE)

Identifiers

PMID39126439
PMCPMC11582156

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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.