Evidence map›Paper›PMID 40622613›Full record

ArticleJapanese journal of radiology2025

Usefulness of compressed sensing coronary magnetic resonance angiography with deep learning reconstruction.

Kohei Tabo, Tomoyuki Kido, Megumi Matsuda, Shota Tokui, Genki Mizogami, Yoshihiro Takimoto, Masaki Matsumoto, Mitsuharu Miyoshi, Teruhito Kido

Abstract read
In one paragraph

Article in Japanese journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

9 authors.

Kohei TaboDepartment of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, Japan. zinpan1219@gmail.com.ORCID http://orcid.org/0009-0005-9583-5661
Tomoyuki KidoDepartment of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, Japan.
Megumi MatsudaDepartment of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, Japan.
Shota TokuiDepartment of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, Japan.
Genki MizogamiDepartment of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, Japan.
Yoshihiro TakimotoEhime University Hospital, Shitsukawa, Toon, Ehime, Japan.
Masaki MatsumotoEhime University Hospital, Shitsukawa, Toon, Ehime, Japan.
Mitsuharu MiyoshiGE HealthCare, Hino, Tokyo, Japan.
Teruhito KidoDepartment of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeCoronary magnetic resonance angiography (CMRA) scans are generally time-consuming. CMRA with compressed sensing (CS) and artificial intelligence (AI) (CSAI CMRA) is expected to shorten the imaging time while maintaining image quality. This study aimed to evaluate the usefulness of CS and AI for non-contrast CMRA. MATERIALS AND

methodsTwenty volunteers underwent both CS and conventional CMRA. Conventional CMRA employed parallel imaging (PI) with an acceleration factor of 2. CS CMRA employed a combination of PI and CS with an acceleration factor of 3. Deep learning reconstruction was performed offline on the CS CMRA data after scanning, which was defined as CSAI CMRA. We compared the imaging time, image quality, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and vessel sharpness for each CMRA scan.

resultsThe CS CMRA scan time was significantly shorter than that of conventional CMRA (460 s [343,753 s] vs. 727 s [567,939 s], p < 0.001). The image quality scores of the left anterior descending artery (LAD) and left circumflex artery (LCX) were significantly higher in conventional CMRA (LAD: 3.3 ± 0.7, LCX: 3.3 ± 0.7) and CSAI CMRA (LAD: 3.7 ± 0.6, LCX: 3.5 ± 0.7) than the CS CMRA (LAD: 2.9 ± 0.6, LCX: 2.9 ± 0.6) (p < 0.05). The right coronary artery scores did not vary among the three groups (p = 0.087). The SNR and CNR were significantly higher in CSAI CMRA (SNR: 12.3 [9.7, 13.7], CNR: 12.3 [10.5, 14.5]) and CS CMRA (SNR: 10.5 [8.2, 12.6], CNR: 9.5 [7.9, 12.6]) than conventional CMRA (SNR: 9.0 [7.8, 11.1], CNR: 7.7 [6.0, 10.1]) (p < 0.01). The vessel sharpness was significantly higher in CSAI CMRA (LAD: 0.87 [0.78, 0.91]) (p < 0.05), with no significant difference between the CS CMRA (LAD: 0.77 [0.71, 0.83]) and conventional CMRA (LAD: 0.77 [0.71, 0.86]).

conclusionCSAI CMRA can shorten the imaging time while maintaining good image quality.

Indexed as

Coronary AngiographyData CompressionDeep LearningImage Processing, Computer-AssistedMagnetic Resonance AngiographyAdultCoronary VesselsFemaleHumansMaleMiddle AgedSignal-To-Noise RatioCcardiovascular magnetic resonanceCompressed sensingCoronary magnetic resonance angiographyDeep learning reconstruction

Identifiers

PMID40622613
PMCPMC12575453

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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.