Evidence map›Paper›PMID 40353517›Full record

ArticleMagnetic resonance in medicine2025

Groupwise image registration with edge-based loss for low-SNR cardiac MRI.

Xuan Lei, Philip Schniter, Chong Chen, Rizwan Ahmad

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2025. 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

4 authors.

Xuan LeiElectrical & Computer Engineering, The Ohio State University, Columbus, Ohio.ORCID https://orcid.org/0009-0009-0878-240X
Philip SchniterElectrical & Computer Engineering, The Ohio State University, Columbus, Ohio.ORCID https://orcid.org/0000-0003-0939-7545
Chong ChenBiomedical Engineering, The Ohio State University, Columbus, Ohio.ORCID https://orcid.org/0000-0002-4965-5564
Rizwan AhmadBiomedical Engineering, The Ohio State University, Columbus, Ohio.ORCID https://orcid.org/0000-0002-5917-3788

Funding

A comprehensive valvular heart disease assessment with stress cardiac MRIR01HL151697 · NHLBI · OHIO STATE UNIVERSITY · PI AHMAD, RIZWAN · 2021 to 2025
$3.3M
A comprehensive deep learning framework for MRI reconstructionR01EB029957 · NIBIB · OHIO STATE UNIVERSITY · PI AHMAD, RIZWAN, SCHNITER, PHILIP · 2021 to 2024
$2.3M
NHLBI NIH HHS R01EB029957NHLBI NIH HHS R01 HL151697NHLBI NIH HHS R01HL151697NIBIB NIH HHS R01 EB029957
6 · The paper itself

Abstract

purposeThe purpose of this study is to perform image registration and averaging of multiple free-breathing single-shot cardiac images, where the individual images may have a low signal-to-noise ratio (SNR).

methodsTo address low SNR encountered in single-shot imaging, especially at low field strengths, we propose a fast deep learning (DL)-based image registration method, called Averaging Morph with Edge Detection (AiM-ED). AiM-ED jointly registers multiple noisy source images to a noisy target image and utilizes a noise-robust pre-trained edge detector to define the training loss. We validate AiM-ED using synthetic late gadolinium enhanced (LGE) images from the MR extended cardiac-torso (MRXCAT) phantom and free-breathing single-shot LGE images from healthy subjects (24 slices) and patients (5 slices) under various levels of added noise. Additionally, we demonstrate the clinical feasibility of AiM-ED by applying it to data from patients (6 slices) scanned on a 0.55T scanner.

resultsCompared with a traditional energy-minimization-based image registration method and DL-based VoxelMorph, images registered using AiM-ED exhibit higher values of recovery SNR and three perceptual image quality metrics. An ablation study shows the benefit of both jointly processing multiple source images and using an edge map in AiM-ED.

conclusionFor single-shot LGE imaging, AiM-ED outperforms existing image registration methods in terms of image quality. With fast inference, minimal training data requirements, and robust performance at various noise levels, AiM-ED has the potential to benefit single-shot CMR applications.

Indexed as

HeartImage Processing, Computer-AssistedMagnetic Resonance ImagingAlgorithmsContrast MediaDeep LearningGadoliniumHumansImage Interpretation, Computer-AssistedPhantoms, ImagingSignal-To-Noise RatioContrast MediaGadoliniumcardiovascular MRIedge detectionimage registrationLGEVoxelMorph

Identifiers

PMID40353517
PMCPMC12202741

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.