Evidence map›Paper›PMID 42453557›Full record

ArticleProceedings. IEEE International Symposium on Biomedical Imaging2026

CODE: A SELF-SUPERVISED CONSISTENCY MODEL FRAMEWORK FOR MRI DENOISING.

Junying Li, Qingyang Hou, Kaifeng Pang, Qi Miao, Alex Ling Yu Hung, Elif Aygun, Shu-Fu Shih, Qing Dai, Holden H Wu, Kyunghyun Sung

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Junying LiDepartment of Radiological Sciences, UCLA, Los Angeles, CA, USA 90095.
Qingyang HouDepartment of Radiological Sciences, UCLA, Los Angeles, CA, USA 90095.
Kaifeng PangDepartment of Radiological Sciences, UCLA, Los Angeles, CA, USA 90095.
Qi MiaoDepartment of Radiological Sciences, UCLA, Los Angeles, CA, USA 90095.
Alex Ling Yu HungDepartment of Radiological Sciences, UCLA, Los Angeles, CA, USA 90095.
Elif AygunDepartment of Radiological Sciences, UCLA, Los Angeles, CA, USA 90095.
Shu-Fu ShihDepartment of Radiological Sciences, UCLA, Los Angeles, CA, USA 90095.
Qing DaiDepartment of Radiological Sciences, UCLA, Los Angeles, CA, USA 90095.
Holden H WuDepartment of Radiological Sciences, UCLA, Los Angeles, CA, USA 90095.
Kyunghyun SungDepartment of Radiological Sciences, UCLA, Los Angeles, CA, USA 90095.

Funding

Integrating Quantitative MRI and Artificial Intelligence to Improve Prostate Cancer ClassificationR01CA248506 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SUNG, KYUNG HYUN, WU, HOLDEN H · 2020 to 2024
$3.0M
Racially-associated MRI analysis and modeling for predicting aggressive prostate cancerR01CA272702 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Harrison Kim, Kyung Hyun Sung · 2023 to 2026
$2.4M
NCI NIH HHS R01 CA248506NCI NIH HHS R01 CA272702
6 · The paper itself

Abstract

Magnetic Resonance Imaging (MRI) is great for visualizing soft tissues and quantitative assessment of tissue properties, but its acquisitions are often limited by signal noise originating from the patient's thermal and physiological processes, motion, and hardware limitations/imperfections. We propose

Indexed as

Consistency modelDiffusion MRIMRI DenoisingRandom Matrix TheorySelf-supervised Learning

Identifiers

PMID42453557
PMCPMC13365742

What OpenQuestion holds

Textmetadata
LicenceTDM
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.