Evidence map›Paper›PMID 42156376›Full record

ArticleNature communications2026

Dynamic breast MRI with Flexible Temporal Resolution Aided by Deep Learning.

Sungheon Gene Kim, Jonghyun Bae, Linda Moy, Laura Heacock, Li Feng, Eddy Solomon

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Sungheon Gene KimMRI Research Institute, Department of Radiology, Weill Cornell Medical College, Cornell University, 407 East 61st Street, New York, NY, USA.ORCID http://orcid.org/0000-0002-6288-0678
Jonghyun BaeMRI Research Institute, Department of Radiology, Weill Cornell Medical College, Cornell University, 407 East 61st Street, New York, NY, USA.
Linda MoyCenter for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University, 660 First Avenue, New York, NY, USA.
Laura HeacockCenter for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University, 660 First Avenue, New York, NY, USA.
Li FengCenter for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University, 660 First Avenue, New York, NY, USA.
Eddy SolomonFaculty of Biomedical Engineering, Technion-Israel Institute of Technology, Haifa, Israel. eddy.solomon@technion.ac.il.ORCID http://orcid.org/0000-0001-9204-4518

Funding

DCE MRI Study for Breast Cancer.R01CA160620 · NCI · WEILL MEDICAL COLL OF CORNELL UNIV · PI KIM, SUNGHEON GENE · 2012 to 2023
$5.4M
Gradient-Echo Spectroscopic Imaging Study of Saturated Fat and Breast CancerR01CA219964 · NCI · WEILL MEDICAL COLL OF CORNELL UNIV · PI KIM, SUNGHEON GENE, MOY, LINDA · 2018 to 2022
$2.3M
Rapid Motion-Robust and Easy-to-Use Dynamic Contrast-Enhanced MRI for Liver Perfusion QuantificationR01EB030549 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI FENG, LI · 2021 to 2024
$1.7M
Diffusion MRI of Treatment Response for De-escalation of Radiation TherapyUH3CA228699 · NCI · WEILL MEDICAL COLL OF CORNELL UNIV · PI KIM, SUNGHEON GENE · 2021 to 2023
$1.2M
NCI NIH HHS R01 CA160620NCI NIH HHS R01 CA219964NCI NIH HHS UH3 CA228699NIBIB NIH HHS R01 EB030549
6 · The paper itself

Abstract

MRI is the most effective method for screening high-risk breast cancer patients. While current exams rely on the qualitative evaluation of morphological features before and after contrast administration and less on contrast kinetic information, recent developments in fast acquisition methods aim to combine both. However, balancing spatial resolution, temporal resolution and scan time poses a considerable challenge in dynamic MRI. Here, we introduce a radial MRI reconstruction framework for Dynamic Contrast Enhanced (DCE) imaging, termed Enhanced Locally low-rank Imaging for Tissue contrast Enhancement (ELITE), to address these limitations. ELITE combines locally low-rank subspace modeling to capture spatially localized tissue dynamics with deep learning. We evaluate its effectiveness using the publicly available fastMRI breast initiative, demonstrating substantial improvements in CNR and noise reduction while enabling flexible temporal resolution down to 1 second. ELITE also shows benefits in neck and brain imaging, making it a viable alternative for other DCE-MRI applications.

Indexed as

BreastBreast NeoplasmsDeep LearningMagnetic Resonance ImagingContrast MediaDynamic Contrast Enhanced Magnetic Resonance ImagingFemaleHumansImage Processing, Computer-AssistedContrast Media

Identifiers

PMID42156376
PMCPMC13381800

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.