Evidence map›Paper›PMID 42829777›Full record

ArticleProceedings. International Conference on Image Processing2026

NOISY MRI RECONSTRUCTION VIA MAP ESTIMATION WITH AN IMPLICIT DEEP-DENOISER PRIOR.

Nikola Janjušević, Amirhossein Khalilian-Gourtani, Yao Wang, Li Feng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings. International Conference on Image Processing, 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

4 authors.

Nikola JanjuševićRadiology Department, New York University Grossman School of Medicine.
Amirhossein Khalilian-GourtaniNeurology Department, New York University Grossman School of Medicine.
Yao WangNew York University Tandon School of Engineering, Electrical and Computer Engineering Department.
Li FengRadiology Department, New York University Grossman School of Medicine.

Funding

TR&D 4: Revealing Microstructure: Biophysical modeling and validation for discovery and clinical careP41EB017183 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Christopher M Collins · 2014 to 2026
$19.3M
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
Rapid Structure-Function MRI of the Lung for Post-COVID-19 ManagementR01EB031083 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI FENG, LI · 2021 to 2021
$1.5M
3D Free-Breathing Fat and Iron Corrected T1 MappingR21EB032917 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI FENG, LI · 2022 to 2023
$465k
NIBIB NIH HHS P41 EB017183NIBIB NIH HHS R01 EB030549NIBIB NIH HHS R01 EB031083NIBIB NIH HHS R21 EB032917
6 · The paper itself

Abstract

Accelerating magnetic resonance imaging (MRI) remains challenging, particularly under realistic acquisition noise. While diffusion models have recently shown promise for reconstructing undersampled MRI data, many approaches lack an explicit link to the underlying MRI physics, and their parameters are sensitive to measurement noise, limiting their reliability in practice. We introduce

Identifiers

PMID42829777
PMCPMC13633827

What OpenQuestion holds

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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.