Evidence map›Paper›PMID 41085013›Full record

ArticleJournal of magnetic resonance imaging : JMRI2026

Accelerating 2D Kidney Magnetic Resonance Fingerprinting Using Deep Learning Based Tissue Quantification.

Zhiqing Yin, Huay Din, Jessie E P Sun, Christina J MacAskill, Sree Harsha Tirumani, Pew-Thian Yap, Mark Griswold, Chris A Flask, Yong Chen

Abstract read
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In one paragraph

Article in Journal of magnetic resonance imaging : JMRI, 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

9 authors.

Zhiqing YinDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.ORCID https://orcid.org/0009-0006-1596-7013
Huay DinDepartment of Mathematics, Case Western Reserve University, Cleveland, Ohio, USA.
Jessie E P SunDepartment of Radiology, Case Western Reserve University, Cleveland, Ohio, USA.
Christina J MacAskillDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
Sree Harsha TirumaniDepartment of Radiology, Case Western Reserve University, Cleveland, Ohio, USA.
Pew-Thian YapDepartment of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Mark GriswoldDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
Chris A FlaskDepartment of Radiology, Case Western Reserve University, Cleveland, Ohio, USA.
Yong ChenDepartment of Radiology, Case Western Reserve University, Cleveland, Ohio, USA.

Funding

Development of fast diffusion magnetic resonance fingerprinting of the prostate to avoid unnecessary biopsiesR01CA292091 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI Yong Chen, Leonardo Kayat Bittencourt · 2024 to 2026
$3.2M
Development of Magnetic Resonance Fingerprinting (MRF) to Assess Response to Neoadjuvant Chemotherapy in Breast CancerR01CA282516 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI Yong Chen, Dan Ma · 2023 to 2026
$2.4M
Development of Magnetic Resonance Fingerprinting in Kidney for Evaluation of Renal Cell CarcinomaR01CA266702 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI CHEN, YONG, TIRUMANI, SREE HARSHA · 2022 to 2025
$2.0M
Optimized High-Resolution Fast Magnetic Resonance Fingerprinting with Cloud-Based ReconstructionR01NS134849 · NINDS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Yong Chen, Pew-Thian Yap · 2024 to 2026
$1.9M
NCI NIH HHS R01 CA266702NCI NIH HHS R01 CA282516NCI NIH HHS R01 CA292091NIH HHS R01CA266702NIH HHS R01CA282516NIH HHS R01CA292091NIH HHS R01NS134849NINDS NIH HHS R01 NS134849Siemens Healthineers
6 · The paper itself

Abstract

backgroundMagnetic Resonance Fingerprinting (MRF) is a technique that can provide rapid quantification of multiple tissue properties. Deep learning may potentially contribute to an accelerated acquisition of MRF. PURPOSE: (1) To develop a deep learning method to accelerate the acquisition for kidney MRF; (2) to evaluate its performance in healthy subjects and patients with renal masses. STUDY TYPE: Retrospective and based on internal reference data. SUBJECTS: Development set was 36 healthy subjects and 20 patients with renal masses. The testing set: 4 healthy subjects and 16 patients. FIELD STRENGTH/SEQUENCE: 3T, Steady-State Free Precession (FISP)-based MRF. ASSESSMENT: Quantification accuracy was evaluated in healthy kidneys and renal masses using quantitative metrics including normalized root-mean-square error (NRMSE) calculated based on reference maps generated using the standard template matching approach with all acquired MRF time frames. STATISTICAL TESTS: Paired Student's t-test. p < 0.05 was considered statistically significant.

resultsAccurate quantification in both T

conclusionA deep learning-based method was developed to accelerate acquisition without compromising the accuracy of relaxation time mapping using kidney MRF. These results demonstrate reliable tissue quantification with at least a two-fold acceleration for both healthy kidneys and renal masses with various subtypes and histopathological grades. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 1.

Indexed as

Deep LearningImage Processing, Computer-AssistedKidneyKidney NeoplasmsMagnetic Resonance ImagingAdultAgedAlgorithmsFemaleHumansImage Interpretation, Computer-AssistedMaleMiddle AgedReproducibility of ResultsRetrospective Studiesdeep learningmagnetic resonance fingerprintingquantitative imagingrenal cell carcinomaT 1 and T 2 relaxation times

Identifiers

PMID41085013
PMCPMC12810997

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LicenceCC BY-NC-ND
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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.