Evidence map›Paper›PMID 41843381›Full record

ArticleMagma (New York, N.Y.)2026

Semi-supervision for clinical contrast-weighted image synthesis from magnetic resonance fingerprinting.

Mahmut Yurt, Cagan Alkan, Xiaozhi Cao, Congyu Liao, Zihan Zhou, Tolga Cukur, Ali Syed, John Pauly, Shreyas Vasanawala, Kawin Setsompop

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Article in Magma (New York, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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.

Mahmut YurtElectrical Engineering, Stanford University, Stanford, CA, USA. myurt@stanford.edu.
Cagan AlkanElectrical Engineering, Stanford University, Stanford, CA, USA.
Xiaozhi CaoRadiology, Stanford University, Stanford, CA, USA.
Congyu LiaoRadiology, University of California, San Francisco, San Francisco, CA, USA.
Zihan ZhouRadiology, Stanford University, Stanford, CA, USA.
Tolga CukurElectrical Engineering, Bilkent University, Ankara, Turkey.
Ali SyedRadiology, Stanford University, Stanford, CA, USA.
John PaulyElectrical Engineering, Stanford University, Stanford, CA, USA.
Shreyas VasanawalaRadiology, Stanford University, Stanford, CA, USA.
Kawin SetsompopElectrical Engineering, Stanford University, Stanford, CA, USA.

Funding

Next generation in-vivo diffusion imaging at submillimeter resolutionR01MH116173 · NIMH · STANFORD UNIVERSITY · PI Yogesh Rathi, Kawin Setsompop · 2018 to 2026
$6.2M
fMRI Technologies for Imaging at the Limit of Biological Spatiotemporal Resolution: Administrative SupplementR01EB019437 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI POLIMENI, JONATHAN RIZZO, SETSOMPOP, KAWIN · 2015 to 2024
$5.5M
Rapid MRI acquisition for pediatric low-grade gliomasR01EB020613 · NIBIB · STANFORD UNIVERSITY · PI SETSOMPOP, KAWIN · 2016 to 2019
$2.5M
Robust and precise imaging of infant brain development with magneticresonance imagingR01HD114719 · NICHD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Congyu Liao · 2024 to 2026
$1.6M
Eunice Kennedy Shriver National Institute of Child Health and Human Development R01HD114719NIBIB NIH HHS R01EB019437NIBIB NIH HHS R01EB020613NICHD NIH HHS R01 HD114719NIMH NIH HHS R01MH116173
6 · The paper itself

Abstract

purposeTo facilitate ease of data compilation across diverse populations for training models to synthesize clinical contrast-weighted images from magnetic resonance fingerprinting.

methodsWe leverage a semi-supervised training framework using highly accelerated acquisitions of the target contrasts used as ground truths. We utilize complementary randomized data sampling masks across training subjects and contrasts for homogeneous learning in k-space, together with multi-task learning.

resultsOur experiments indicate that the proposed method achieves high-quality synthesis with networks trained on retrospectively and prospectively undersampled data of the contrast-weighted images, enabling undersampling up to 12-16

conclusionsThe proposed method enables semi-supervised learning for synthesis from MRF with an end-to-end, ultra-fast training data acquisition protocol that is easier to obtain across a large population in clinical settings.

Indexed as

Clinical contrastsMagnetic resonance fingerprintingMRISemi-supervisionSynthesis

Identifiers

PMID41843381

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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.