Evidence map›Paper›PMID 41964083›Full record

ArticleMagnetic resonance in medicine2026

Enhanced Quantitative Phosphocreatine MR Imaging of Skeletal Muscle Using a Global-Local Two-Branch Deep Learning Model.

Malvika Viswanathan, Leqi Yin, Yashwant Kurmi, Shuyang Chai, Xiaoyu Jiang, Yuankai Huo, Junzhong Xu, Limin Chen, John C Gore, Zhongliang Zu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Magnetic resonance in medicine, 2026. 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

10 authors.

Malvika ViswanathanVanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.ORCID https://orcid.org/0009-0001-9522-0780
Leqi YinVanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Yashwant KurmiVanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.ORCID https://orcid.org/0000-0003-4986-2106
Shuyang ChaiSchool of Engineering, Vanderbilt University, Nashville, Tennessee, USA.
Xiaoyu JiangVanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.ORCID https://orcid.org/0000-0002-0369-6301
Yuankai HuoDepartment of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.
Junzhong XuVanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.ORCID https://orcid.org/0000-0001-7895-4232
Limin ChenVanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
John C GoreVanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Zhongliang ZuVanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.ORCID https://orcid.org/0000-0001-7361-7480

Funding

Nuclear Overhauser enhancement (NOE) MR imaging of choline phospholipids and their metabolismR01EB029443 · NIBIB · VANDERBILT UNIVERSITY MEDICAL CENTER · PI ZU, ZHONGLIANG · 2021 to 2024
$1.5M
Advancing Machine Learning for CEST MRI Analysis through Partially Synthetic DataR01EB036574 · NIBIB · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Zhongliang Zu · 2025 to 2026
$843k
Developing a Novel Machine Learning Method with Partially Synthetic Data for Rapid and Precise pH Mapping in Ischemic StrokeR01NS140757 · NINDS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Zhongliang Zu · 2025 to 2026
$782k
Development of an innovative Non-Invasive MR Imaging Technique for assessing Membrane Lipids and Their Properties in Alzheimer's DiseaseR21AG089699 · NIA · VANDERBILT UNIVERSITY MEDICAL CENTER · PI ZU, ZHONGLIANG · 2024 to 2024
$481k
NIA NIH HHS R21 AG089699NIBIB NIH HHS R01 EB029443NIBIB NIH HHS R01 EB036574NIH HHS R01EB029443NIH HHS R01EB036574NIH HHS R01NS140757NIH HHS R21AG089699NINDS NIH HHS R01 NS140757
6 · The paper itself

Abstract

purposePhosphocreatine (PCr) is an essential marker of muscle metabolism, and accurate quantification of its (f

methodsThis study introduces a global-local two-branch DL model to effectively eliminate confounding effects and capture subtle variations in the PCr CEST effect. Furthermore, our model was trained on partially synthetic data that offers both simulation flexibility and fidelity. Model accuracy was evaluated by using both digital and physical phantoms, and the model was applied to skeletal muscle of healthy rats and rats with amyotrophic lateral sclerosis (ALS).

resultsPhantom experiments demonstrate that our approach surpasses all fitting methods, the state-of-the-art model, and other combinations of DL models and training data. In vivo, the model identified a significant reduction in PCr f

conclusionsOur global-local two-branch DL model trained using partially synthetic data enhances PCr quantification in skeletal muscle.

Indexed as

Deep LearningImage Processing, Computer-AssistedMagnetic Resonance ImagingMuscle, SkeletalPhosphocreatineAlgorithmsAnimalsMalePhantoms, ImagingRatsRats, Sprague-DawleyPhosphocreatineamyotrophic lateral sclerosischemical exchange saturation transferdeep learningphosphocreatine

Identifiers

PMID41964083
PMCPMC13269197

What OpenQuestion holds

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LicenceCC BY
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.