Evidence map›Paper›PMID 38743377›Full record

ArticleMagma (New York, N.Y.)2024

Large-scale 3D non-Cartesian coronary MRI reconstruction using distributed memory-efficient physics-guided deep learning with limited training data.

Chi Zhang, Davide Piccini, Omer Burak Demirel, Gabriele Bonanno, Christopher W Roy, Burhaneddin Yaman, Steen Moeller, Chetan Shenoy, Matthias Stuber, Mehmet Akçakaya

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PubMed Full text at PMC Publisher
In one paragraph

Article in Magma (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

10 authors.

Chi ZhangElectrical and Computer Engineering, University of Minnesota, 200 Union Street S.E., Minneapolis, MN, 55455, USA.
Davide PicciniDepartment of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Omer Burak DemirelElectrical and Computer Engineering, University of Minnesota, 200 Union Street S.E., Minneapolis, MN, 55455, USA.
Gabriele BonannoAdvanced Clinical Imaging Technology, Siemens Healthineers International, Lausanne, Switzerland.
Christopher W RoyDepartment of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Burhaneddin YamanElectrical and Computer Engineering, University of Minnesota, 200 Union Street S.E., Minneapolis, MN, 55455, USA.
Steen MoellerCenter for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA.
Chetan ShenoyDepartment of Medicine (Cardiology), University of Minnesota, Minneapolis, MN, 55455, USA.
Matthias StuberDepartment of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Mehmet AkçakayaElectrical and Computer Engineering, University of Minnesota, 200 Union Street S.E., Minneapolis, MN, 55455, USA. akcakaya@umn.edu.ORCID http://orcid.org/0000-0001-6400-7736

Funding

TRD4 - Ultrahigh Field Engineering and SafetyP41EB027061 · NIBIB · UNIVERSITY OF MINNESOTA · PI Gregory John Metzger · 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
Novel Quantitative MRI Techniques for the Assessment of Cardiac Fibrosis without Gadolinium ContrastR21EB028369 · NIBIB · UNIVERSITY OF MINNESOTA · PI AKCAKAYA, MEHMET · 2020 to 2022
$602k
NHLBI NIH HHS R01 HL153146NIBIB NIH HHS P41 EB027061NIBIB NIH HHS R01 EB032830NIBIB NIH HHS R21 EB028369NIH HHS P41EB027061NIH HHS R01EB032830NIH HHS R01HL153146NIH HHS R21EB028369
6 · The paper itself

Abstract

objectTo enable high-quality physics-guided deep learning (PG-DL) reconstruction of large-scale 3D non-Cartesian coronary MRI by overcoming challenges of hardware limitations and limited training data availability. MATERIALS AND

methodsWhile PG-DL has emerged as a powerful image reconstruction method, its application to large-scale 3D non-Cartesian MRI is hindered by hardware limitations and limited availability of training data. We combine several recent advances in deep learning and MRI reconstruction to tackle the former challenge, and we further propose a 2.5D reconstruction using 2D convolutional neural networks, which treat 3D volumes as batches of 2D images to train the network with a limited amount of training data. Both 3D and 2.5D variants of the PG-DL networks were compared to conventional methods for high-resolution 3D kooshball coronary MRI.

resultsProposed PG-DL reconstructions of 3D non-Cartesian coronary MRI with 3D and 2.5D processing outperformed all conventional methods both quantitatively and qualitatively in terms of image assessment by an experienced cardiologist. The 2.5D variant further improved vessel sharpness compared to 3D processing, and scored higher in terms of qualitative image quality. DISCUSSION: PG-DL reconstruction of large-scale 3D non-Cartesian MRI without compromising image size or network complexity is achieved, and the proposed 2.5D processing enables high-quality reconstruction with limited training data.

Indexed as

Coronary VesselsDeep LearningImage Processing, Computer-AssistedImaging, Three-DimensionalMagnetic Resonance ImagingNeural Networks, ComputerAlgorithmsHumansPhysicsCardiac MRICoronary MRIDeep learningImage reconstructionNon-Cartesian

Identifiers

PMID38743377
PMCPMC11755671

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.