Evidence map›Paper›PMID 39385473›Full record

ArticleMagnetic resonance in medicine2025

Improved liver fat and

Shu-Fu Shih, Bilal Tasdelen, Ecrin Yagiz, Zhaohuan Zhang, Xiaodong Zhong, Sophia X Cui, Krishna S Nayak, Holden H Wu

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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  8. Improved liver fat andMagnetic resonance in medicine · 2025
    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

8 authors.

Shu-Fu ShihDepartment of Radiological Sciences, University of California Los Angeles, Los Angeles, California, USA.ORCID 0000-0002-3812-9091
Bilal TasdelenMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.ORCID 0000-0001-6462-3651
Ecrin YagizMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.ORCID 0000-0001-8113-2192
Zhaohuan ZhangDepartment of Radiological Sciences, University of California Los Angeles, Los Angeles, California, USA.ORCID 0000-0001-8086-6522
Xiaodong ZhongDepartment of Radiological Sciences, University of California Los Angeles, Los Angeles, California, USA.ORCID 0000-0001-8355-5279
Sophia X CuiMR R&D Collaborations, Siemens Medical Solutions USA, Inc., Los Angeles, California, USA.ORCID 0000-0002-5133-4903
Krishna S NayakMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.ORCID 0000-0001-5735-3550
Holden H WuDepartment of Radiological Sciences, University of California Los Angeles, Los Angeles, California, USA.ORCID 0000-0002-2585-5916

Funding

Quantitative MRI and Deep Learning Technologies for Classification of NAFLDU01EB031894 · NIBIB · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Maria I. Altbach, VIBHAS DESHPANDE · 2022 to 2026
$3.0M
Quantifying Body Composition and Liver Disease in Children using Free-Breathing MRI and MRER01DK124417 · NIDDK · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CALKINS, KARA LYNNE, WU, HOLDEN H · 2020 to 2023
$2.3M
National Science Foundation 1828736NIBIB NIH HHS U01 EB031894NIDDK NIH HHS R01 DK124417
6 · The paper itself

Abstract

purposeTo improve liver proton density fat fraction (PDFF) and

methodsA Monte Carlo simulation was conducted to design a protocol for PDFF and

resultsIn the phantom, RLLR and RMT denoising improved accuracy in PDFF and

conclusionWe validated an acquisition protocol for improved PDFF and

Indexed as

AlgorithmsLiverMagnetic Resonance ImagingMonte Carlo MethodPhantoms, ImagingAdipose TissueAdultComputer SimulationFatty LiverFemaleHumansImage Processing, Computer-AssistedMaleMiddle AgedReproducibility of ResultsSignal-To-Noise Ratio0.55 T MRIdenoisingliverliver PDFFlocally low‐rank

Identifiers

PMID39385473
PMCPMC11680733

What OpenQuestion holds

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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.