Evidence map›Paper›PMID 39886655›Full record

ArticleProceedings. IEEE International Symposium on Biomedical Imaging2024

A CONVEX COMPRESSIBILITY-INSPIRED UNSUPERVISED LOSS FUNCTION FOR PHYSICS-DRIVEN DEEP LEARNING RECONSTRUCTION.

Yaşar Utku Alçalar, Merve Gülle, Mehmet Akçakaya

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Yaşar Utku AlçalarElectrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota, USA.
Merve GülleElectrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota, USA.
Mehmet AkçakayaElectrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota, USA.

Funding

TRD4 - Ultrahigh Field Engineering and SafetyP41EB027061 · NIBIB · UNIVERSITY OF MINNESOTA · PI Gregory John Metzger, KAMIL UGURBIL · 2019 to 2026
$11.7M
Rapid Comprehensive Cardiac MRI Exam for Diagnosis of Coronary Artery DiseaseR01HL153146 · NHLBI · UNIVERSITY OF MINNESOTA · PI AKCAKAYA, MEHMET · 2020 to 2024
$2.5M
Robust and Efficient Learning of High-Resolution Brain MRI Reconstruction from Small Referenceless DataR01EB032830 · NIBIB · UNIVERSITY OF MINNESOTA · PI Mehmet Akcakaya · 2023 to 2026
$2.0M
NHLBI NIH HHS R01 HL153146NIBIB NIH HHS P41 EB027061NIBIB NIH HHS R01 EB032830
6 · The paper itself

Abstract

Physics-driven deep learning (PD-DL) methods have gained popularity for improved reconstruction of fast MRI scans. Though supervised learning has been used in early works, there has been a recent interest in unsupervised learning methods for training PD-DL. In this work, we take inspiration from statistical image processing and compressed sensing (CS), and propose a novel convex loss function as an alternative learning strategy. Our loss function evaluates the compressibility of the output image while ensuring data fidelity to assess the quality of reconstruction in versatile settings, including supervised, unsupervised, and zero-shot scenarios. In particular, we leverage the reweighted

Indexed as

compressed sensingdeep learningfast MRIImage reconstructionneural networkssupervised learningunsupervised learningzero-shot learning

Identifiers

PMID39886655
PMCPMC11779509

What OpenQuestion holds

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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.