Evidence map›Paper›PMID 40285832›Full record

ArticleJapanese journal of radiology2025

Improvement of image quality of diffusion-weighted imaging (DWI) with deep learning reconstruction of the pancreas: comparison with respiratory-gated conventional DWI.

Kazuki Oyama, Fumihito Ichinohe, Yasuo Adachi, Yoshihiro Kito, Katsuya Maruyama, Minoru Mitsuda, Thomas Benkert, Omar Darwish, Yasunari Fujinaga

Abstract readComparative Study
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 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

9 authors.

Kazuki OyamaDepartment of Radiology, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan.
Fumihito IchinoheDepartment of Radiology, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan.
Yasuo AdachiRadiology Division, Shinshu University Hospital, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan.
Yoshihiro KitoRadiology Division, Shinshu University Hospital, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan.
Katsuya MaruyamaMR Research and Collaboration Department, Siemens Healthcare K.K, Tokyo, Japan.
Minoru MitsudaMagnetic Resonance Department, Siemens Healthcare K.K, Tokyo, Japan.
Thomas BenkertMR Application Predevelopment, Siemens Healthcare AG, Forchheim, Germany.
Omar DarwishMR Application Predevelopment, Siemens Healthcare AG, Forchheim, Germany.
Yasunari FujinagaDepartment of Radiology, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan. fujinaga@shinshu-u.ac.jp.ORCID http://orcid.org/0000-0002-3752-257X

Funding

the Japan Society for the Promotion of Science 23K07057
6 · The paper itself

Abstract

purposeThis study aimed to evaluate the efficacy of deep learning-based reconstruction (DLR) in improving pancreatic diffusion-weighted imaging (DWI) quality. MATERIALS AND

methodsIn total, 117 patients (mean age of 68.0 ± 12.9 years) suspected of pancreatic diseases underwent magnetic resonance imaging (MRI) between July and December 2023. MRI sequences included respiratory-gated conventional diffusion-weighted images (RGC-DWIs), respiratory-gated diffusion-weighted images with deep learning-based reconstruction (DLR) (RGDLR-DWIs), and breath-hold diffusion-weighted images with DLR (BHDLR-DWIs) (short TE and long TE equal to other DWIs) at a 3 T MR system. Among these patients, 27 had solid lesions. Two radiologists qualitatively assessed pancreatic shape, main pancreatic duct (MPD) visualization, and solid lesion conspicuity using a 5-point scale. Quantitative analysis included apparent diffusion coefficient (ADC) values for pancreatic parenchyma and solid lesions, signal-to-noise ratio (SNR), pancreas-to-muscle signal-intensity ratio (PM-SIR) and lesion-to-pancreas signal-intensity ratio (LP-SIR). Differences among DWI sequences were analyzed using Friedman's and Bonferroni's tests.

resultsQualitatively, BHDLR-DWIs (short TE) had the highest scores for pancreatic shape and MPD but lowest for solid lesions visibility, whereas RGDLR-DWIs had the highest score for solid lesions. Quantitatively, BHDLR-DWIs (short TE) had the lowest ADC values for pancreatic parenchyma and solid lesions, with the highest PM-SIR. There was no significant difference between BHDLR-DWIs (short TE) and RGDLR-DWIs for solid lesion ADC values. RGC-DWIs had the highest SNR, though differences from RGDLR-DWIs and BHDLR-DWIs (short TE) were not significant. Although LP-SIR in RGDLR-DWIs were the lowest, the difference was not significant.

conclusionBHDLR-DWIs (short TE) provided the best pancreatic morphology image quality, whereas RGDLR-DWIs were superior for solid lesion detection.

Indexed as

Deep LearningDiffusion Magnetic Resonance ImagingImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedPancreasPancreatic DiseasesRespiratory-Gated Imaging TechniquesAgedAged, 80 and overFemaleHumansMaleMiddle AgedRetrospective StudiesDeep learning-based reconstructionDiffusion-weighted imagingMagnetic resonance imagingPancreasSolid lesions

Identifiers

PMID40285832
PMCPMC12396992

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.