Evidence map›Paper›PMID 40451846›Full record

ArticleMagnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine2025

The Impact of Model-based Deep-learning Reconstruction Compared with that of Compressed Sensing-Sensitivity Encoding on the Image Quality and Precision of Cine Cardiac MR in Evaluating Left-ventricular Volume and Strain: A Study on Healthy Volunteers.

Satonori Tsuneta, Satoru Aono, Rina Kimura, Jihun Kwon, Noriyuki Fujima, Kinya Ishizaka, Noriko Nishioka, Masami Yoneyama, Fumi Kato, Kazuyuki Minowa and 1 more

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of 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. WCCAN: Windowed Cross-contrast Attention Network for Multi-contrast Brain MR Image Super-resolution.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026
    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

11 authors.

Satonori TsunetaDepartment of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan.
Satoru AonoDepartment of Radiological Technology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan.
Rina KimuraDepartment of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan.
Jihun KwonPhilips Japan, Tokyo, Japan.
Noriyuki FujimaDepartment of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan.
Kinya IshizakaDental Radiology, Department of Oral Health Science, Graduate School of Dental Medicine, Hokkaido University, Sapporo, Hokkaido, Japan.
Noriko NishiokaDepartment of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan.
Masami YoneyamaPhilips Japan, Tokyo, Japan.
Fumi KatoDepartment of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan.
Kazuyuki MinowaDental Radiology, Department of Oral Health Science, Graduate School of Dental Medicine, Hokkaido University, Sapporo, Hokkaido, Japan.
Kohsuke KudoDepartment of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo evaluate the effect of model-based deep-learning reconstruction (DLR) compared with that of compressed sensing-sensitivity encoding (CS) on cine cardiac magnetic resonance (CMR).

methodsCine CMR images of 10 healthy volunteers were obtained with reduction factors of 2, 4, 6, and 8 and reconstructed using CS and DLR. The visual image quality scores assessed sharpness, image noise, and artifacts. Left-ventricular (LV) end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), and ejection fraction (EF) were manually measured. LV global circumferential strain (GCS) was automatically measured using the software. The precision of EDV, ESV, SV, EF, and GCS measurements was compared between CS and DLR using Bland-Altman analysis with full-sampling data as the gold standard.

resultsCompared with CS, DLR significantly improved image quality with reduction factors of 6 and 8. The precision of EDV and ESV with a reduction factor of 8, and GCS with reduction factors of 6 and 8 measurements improved with DLR compared with CS, whereas those of SV and EF measurements were not different between DLR and CS.

conclusionThe effect of DLR on cine CMR's image quality and precision in evaluating quantitative volume and strain was equal or superior to that of CS. DLR may replace CS for cine CMR.

Indexed as

Deep LearningHeartHeart VentriclesImage Processing, Computer-AssistedMagnetic Resonance Imaging, CineAdultArtifactsData CompressionFemaleHealthy VolunteersHumansMaleMiddle AgedReproducibility of ResultsStroke VolumeVentricular Function, Leftartifactdeep learningejection fractionend-diastolic volumehealthy volunteers

Identifiers

PMID40451846
PMCPMC12772260

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.