Evidence map›Paper›PMID 39382814›Full record

ArticleMagma (New York, N.Y.)2025

Accelerating multi-coil MR image reconstruction using weak supervision.

Arda Atalık, Sumit Chopra, Daniel K Sodickson

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Magma (New York, N.Y.), 2025. 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

3 authors.

Arda AtalıkCenter for Data Science, New York University, 60 Fifth Ave, New York, NY, 10011, USA. Arda.Atalik@nyu.edu.ORCID http://orcid.org/0000-0003-3439-7838
Sumit ChopraCourant Institute of Mathematical Sciences, New York University, 60 Fifth Ave, New York, NY, 10011, USA.
Daniel K SodicksonCenter for Data Science, New York University, 60 Fifth Ave, New York, NY, 10011, USA.

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
National Science Foundation 1922658NIBIB NIH HHS NIH P41 EB017183NIBIB NIH HHS P41 EB017183
6 · The paper itself

Abstract

Deep-learning-based MR image reconstruction in settings where large fully sampled dataset collection is infeasible requires methods that effectively use both under-sampled and fully sampled datasets. This paper evaluates a weakly supervised, multi-coil, physics-guided approach to MR image reconstruction, leveraging both dataset types, to improve both the quality and robustness of reconstruction. A physics-guided end-to-end variational network (VarNet) is pretrained in a self-supervised manner using a 4

Indexed as

Deep LearningImage Processing, Computer-AssistedMagnetic Resonance ImagingSupervised Machine LearningHumansAccelerated imagingMachine learningMR image reconstructionSelf-supervised learningTransfer learningWeak supervision

Identifiers

PMID39382814
PMCPMC12022963

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.