Evidence map›Paper›PMID 40518300›Full record

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

Utility of Thin-slice Single-shot T2-weighted MR Imaging with Deep Learning Reconstruction as a Protocol for Evaluating Pancreatic Cystic Lesions.

Kumi Ozaki, Hanae Hasegawa, Jihun Kwon, Yasutomo Katsumata, Masami Yoneyama, Shota Ishida, Takafumi Iyoda, Masataka Sakamoto, Shuuhei Aramaki, Yukichi Tanahashi and 1 more

Abstract read
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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. Enhanced Visualization of Intracranial Cortical Arteries Using Deep Learning Reconstruction in Vessel Wall MR Imaging.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.

Kumi OzakiDepartment of Radiology, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan.ORCID http://orcid.org/0000-0002-1454-7512
Hanae HasegawaDepartment of Radiology, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan.
Jihun KwonPhilips Japan, Tokyo, Japan.
Yasutomo KatsumataPhilips Japan, Tokyo, Japan.
Masami YoneyamaPhilips Japan, Tokyo, Japan.
Shota IshidaDepartment of Radiological Technology, Faculty of Medical Science, Kyoto College of Medical Science, Nantan, Kyoto, Japan.
Takafumi IyodaDepartment of Radiology, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan.
Masataka SakamotoDepartment of Radiology, Hamamatsu University Hospital, Hamamatsu, Shizuoka, Japan.
Shuuhei AramakiRadiation Oncology, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan.
Yukichi TanahashiDepartment of Radiology, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan.
Satoshi GoshimaDepartment of Radiology, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo assess the effects of industry-developed deep learning reconstruction with super resolution (DLR-SR) on single-shot turbo spin-echo (SshTSE) images with thickness of 2 mm with DLR (SshTSE

methodsThirty consecutive patients who underwent abdominal MRI examinations because of pancreatic cystic lesions under observation between June 2024 and July 2024 were enrolled. We qualitatively and quantitatively evaluated the image qualities of SshTSE

resultsThe SNRs of the pancreas, spleen, paraspinal muscle, peripancreatic fat, and pancreatic cystic lesions of SshTSE

conclusionsSshTSE

Indexed as

Deep LearningImage Processing, Computer-AssistedMagnetic Resonance ImagingPancreatic CystAdultAgedAged, 80 and overFemaleHumansImage Interpretation, Computer-AssistedMaleMiddle AgedPancreasRetrospective Studiesdeep learning reconstructionmagnetic resonance imagingpancreatic cystic lesionsingle-shot turbo spin echothin-slice

Identifiers

PMID40518300
PMCPMC12772264

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.