Evidence map›Paper›PMID 39692759›Full record

ArticleAbdominal radiology (New York)2025

Feasibility/clinical utility of half-Fourier single-shot turbo spin echo imaging combined with deep learning reconstruction in gynecologic magnetic resonance imaging.

Mitsuhiro Kirita, Yuki Himoto, Yasuhisa Kurata, Aki Kido, Koji Fujimoto, Hiroyasu Abe, Yuka Matsumoto, Kumi Harada, Satoshi Morita, Ken Yamaguchi and 3 more

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Article in Abdominal radiology (New York), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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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

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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

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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

13 authors.

Mitsuhiro KiritaKyoto University, Kyoto, Japan.
Yuki HimotoKyoto University, Kyoto, Japan. yhimoto@kuhp.kyoto-u.ac.jp.
Yasuhisa KurataKyoto University, Kyoto, Japan.
Aki KidoUniversity of Toyama, Toyama, Japan.
Koji FujimotoKyoto University, Kyoto, Japan.
Hiroyasu AbeWakayama Medical University, Wakayama, Japan.
Yuka MatsumotoKyoto University, Kyoto, Japan.
Kumi HaradaKyoto University, Kyoto, Japan.
Satoshi MoritaKyoto University, Kyoto, Japan.
Ken YamaguchiKyoto University, Kyoto, Japan.
Dominik NickelSiemens (Germany), Munich, Germany.
Masaki MandaiKyoto University, Kyoto, Japan.
Yuji NakamotoKyoto University, Kyoto, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWhen antispasmodics are unavailable, the periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER; called BLADE by Siemens Healthineers) or half Fourier single-shot turbo spin echo (HASTE) is clinically used in gynecologic MRI. However, their imaging qualities are limited compared to Turbo Spin Echo (TSE) with antispasmodics. Even with antispasmodics, TSE can be artifact-affected, necessitating a rapid backup sequence. PURPOSE: This study aimed to investigate the utility of HASTE with deep learning reconstruction and variable flip angle evolution (iHASTE) compared to conventional sequences with and without antispasmodics. MATERIALS AND

methodsThis retrospective study included MRI scans without antispasmodics for 79 patients who underwent iHASTE, HASTE, and BLADE and MRI scans with antispasmodics for 79 case-control matched patients who underwent TSE. Three radiologists qualitatively evaluated image quality, robustness to artifacts, tissue contrast, and uterine lesion margins. Tissue contrast was also quantitatively evaluated.

resultsQuantitative evaluations revealed that iHASTE exhibited significantly superior tissue contrast to HASTE and BLADE. Qualitative evaluations indicated that iHASTE outperformed HASTE in overall quality. Two of three radiologists judged iHASTE to be significantly superior to BLADE, while two of three judged TSE to be significantly superior to iHASTE. iHASTE demonstrated greater robustness to artifacts than both BLADE and TSE. Lesion margins in iHASTE had lower scores than BLADE and TSE.

conclusioniHASTE is a viable clinical option in patients undergoing gynecologic MRI with anti-spasmodics. iHASTE may also be considered as a useful add-on sequence in patients undergoing MRI with antispasmodics.

Indexed as

Deep LearningImage Interpretation, Computer-AssistedMagnetic Resonance ImagingAdultAgedArtifactsFeasibility StudiesFemaleFourier AnalysisHumansMiddle AgedRetrospective StudiesDeep learningDiagnostic imagingGynecologyMRI scans

Identifiers

PMID39692759

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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.