Evidence map›Paper›PMID 41068347›Full record

ArticleJournal of imaging informatics in medicine2026

Improved Visualization of Pancreatic Cystic Lesions on Magnetic Resonance Cholangiopancreatography Using Super-Resolution Deep Learning Reconstruction.

Jun Kanzawa, Koichiro Yasaka, Yusuke Asari, Mai Sato, Saori Koshino, Yuki Sonoda, Shigeru Kiryu, Osamu Abe

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Jun KanzawaDepartment of Radiology, University of Tokyo Hospital, Tokyo, Japan.
Koichiro YasakaDepartment of Radiology, University of Tokyo Hospital, Tokyo, Japan. koyasaka@gmail.com.ORCID http://orcid.org/0000-0002-0324-6562
Yusuke AsariDepartment of Radiology, University of Tokyo Hospital, Tokyo, Japan.
Mai SatoDepartment of Radiology, University of Tokyo Hospital, Tokyo, Japan.
Saori KoshinoDepartment of Radiology, University of Tokyo Hospital, Tokyo, Japan.
Yuki SonodaDepartment of Radiology, University of Tokyo Hospital, Tokyo, Japan.
Shigeru KiryuDepartment of Radiology, International University of Health and Welfare, Chiba, Japan.
Osamu AbeDepartment of Radiology, University of Tokyo Hospital, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To assess the efficacy of super-resolution deep learning reconstruction (SR-DLR) in enhancing the visualization of pancreatic cystic lesions (PCLs) on magnetic resonance cholangiopancreatography (MRCP). This retrospective study included 85 patients who underwent MRCP, comprising 52 patients with PCLs and 33 without. Images reconstructed using SR-DLR were compared with original images. Quantitative metrics included signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the common bile duct (CBD) and PCLs, as well as full width at half maximum (FWHM), edge rise distance (ERD), and edge rise slope (ERS) of the CBD and main pancreatic duct (MPD). Qualitative evaluation was conducted by three radiologists, assessing the depiction of PCLs and the MPD, image sharpness, noise, artifacts, overall image quality, and the connection of PCLs and MPD. Quantitative and qualitative metrics were compared using paired t-test and the Wilcoxon signed rank test. SR-DLR significantly enhanced SNR and CNR (p < 0.001). Image sharpness was also enhanced, as shown by lower ERD and higher ERS in both CBD and MPD, together with reduced FWHM of the MPD (p < 0.005). Qualitative assessments indicated improved depiction of PCLs and image sharpness with SR-DLR across all readers (p ≤ 0.017). Most readers also reported improved visualization of the MPD and reduced noise, and overall quality. There was no statistically significant difference in determining the connectivity between PCLs and the MPD. SR-DLR significantly enhances image quality in MRCP, improving visualization of PCLs. These findings suggest that SR-DLR can contribute to appropriate management of PCLs.

Indexed as

Cholangiopancreatography, Magnetic ResonanceDeep LearningImage Interpretation, Computer-AssistedPancreatic CystAdultAgedFemaleHumansMaleMiddle AgedRetrospective StudiesSignal-To-Noise RatioDeep learningMagnetic resonance cholangiopancreatographyMagnetic resonance imagingPancreatic cystic lesionsSuper-resolution deep learning reconstruction

Identifiers

PMID41068347
PMCPMC13481936

What OpenQuestion holds

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Registered trials

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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.