Evidence map›Paper›PMID 42287433›Full record

ArticleAbdominal radiology (New York)2026

Super-resolution deep learning reconstruction improves the depiction of peripancreatic arteries and image quality in pancreatic cancer CT.

Rin Tsujimoto, Koichiro Yasaka, Takatoshi Kubo, Satoru Kamio, Akira Katayama, Yusuke Asari, Ako Negishi, Osamu Abe

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Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

8 authors.

Rin TsujimotoThe University of Tokyo, Tokyo, Japan.
Koichiro YasakaThe University of Tokyo, Tokyo, Japan. koyasaka@gmail.com.
Takatoshi KuboThe University of Tokyo, Tokyo, Japan.
Satoru KamioThe University of Tokyo, Tokyo, Japan.
Akira KatayamaThe University of Tokyo, Tokyo, Japan.
Yusuke AsariThe University of Tokyo, Tokyo, Japan.
Ako NegishiThe University of Tokyo, Tokyo, Japan.
Osamu AbeThe University of Tokyo, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeAccurate identification of peripancreatic arteries in pancreatic ductal adenocarcinoma (PDAC) is essential for determining surgical strategy and prognosis. This study compared super-resolution deep learning reconstruction (SR-DLR) with normal-resolution deep learning reconstruction (NR-DLR) and hybrid iterative reconstruction (HIR) in the depiction of peripancreatic arteries in patients with PDAC undergoing pancreatic dynamic CT.

methodsThis retrospective study included 40 patients (mean age, 70.8 ± 9.3 years) with pathologically confirmed PDAC who underwent pancreatic dynamic CT. Images were reconstructed using SR-DLR, NR-DLR, and HIR. In quantitative image analyses, CT attenuation, image noise, contrast-to-noise ratio (CNR), edge rise slope (ERS), and edge rise distance (ERD) were assessed. In qualitative image analyses, five radiologists independently evaluated arterial depiction of major arteries and small peripancreatic arteries, tumor-vessel interface, image noise, image sharpness, diagnostic acceptability, and tumor conspicuity. Statistical analyses were performed using paired t-tests and the Wilcoxon signed-rank test with Bonferroni correction.

resultsSR-DLR showed higher CT attenuation, lower image noise, and higher CNR than NR-DLR and HIR (all p < 0.001). For both the superior mesenteric artery and pancreatic parenchyma, ERS was higher with SR-DLR than with NR-DLR and HIR (all p ≤ 0.002), whereas ERD was lower than with NR-DLR and HIR (all p ≤ 0.001). In qualitative image analyses, SR-DLR achieved the highest scores for small peripancreatic arteries and all qualitative items except for the depiction of major peripancreatic arteries (p < 0.001).

conclusionSR-DLR improved quantitative image quality and enhanced the qualitative depiction of small peripancreatic arteries and the tumor-vessel interface in pancreatic dynamic CT for patients with PDAC.

Indexed as

ArteriesComputer-assistedDeep learningImage processingPancreatic neoplasmsTomographyX-ray computed

Identifiers

PMID42287433

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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.