Evidence map›Paper›PMID 41557181›Full record

ArticleAbdominal radiology (New York)2026

Improved visualization of pancreas and tumor boundaries using high-frequency kernels with deep-learning image reconstruction at high-strength level.

Nobuyuki Kawai, Yoshifumi Noda, Tetsuro Kaga, Kota Matsuoka, Shingo Omata, Yukiko Takai, Masashi Asano, Toshiharu Miyoshi, Takeshi Iwata, Abdelazim Elsayed Elhelaly and 3 more

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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. 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

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

13 authors.

Nobuyuki KawaiDepartment of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan.
Yoshifumi NodaDepartment of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan. noda.yoshifumi.g9@f.gifu-u.ac.jp.
Tetsuro KagaDepartment of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan.
Kota MatsuokaDepartment of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan.
Shingo OmataDepartment of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan.
Yukiko TakaiDepartment of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan.
Masashi AsanoDepartment of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan.
Toshiharu MiyoshiDepartment of Radiology Services, Gifu University Hospital, 1-1 Yanagido, Gifu, 501-1194, Japan.
Takeshi IwataDepartment of Radiology Services, Gifu University Hospital, 1-1 Yanagido, Gifu, 501-1194, Japan.
Abdelazim Elsayed ElhelalyDepartment of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan.
Hirohiko ImaiDepartment of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan.
Hiroki KatoDepartment of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan.
Masayuki MatsuoDepartment of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study aimed to investigate the feasibility of combining high-frequency reconstruction kernels and deep-learning image reconstruction at high-strength level (DLIR-H) for improving visualization of the pancreas and tumor boundaries on pancreatic protocol CT. MATERIALS AND

methodsThis retrospective study included 30 patients (median age, 75 years; 16 women) who underwent pancreatic protocol CT for assessing pancreatic tumors from January 2024 to July 2024. Four image sets were reconstructed using DLIR-H in combination with either standard, bone, bone-plus, or lung kernels. Edge sharpness between the pancreas and retroperitoneal fat tissue (pancreas-to-fat) and between the pancreas and pancreatic ductal adenocarcinoma (pancreas-to-PDAC) was quantitatively assessed using edge rise slope (ERS) measurements. Two radiologists qualitatively examined the sharpness of the pancreas, tumor boundary, and overall image quality.

resultsPancreas-to-fat ERS was greater in lung kernel images than in standard and bone kernel images (P = 0.001). Pancreas-to-PDAC ERS was greater in lung kernel images than in other kernel images (P < 0.001). Sharpness of the pancreas and tumor boundaries was better in lung kernel images than in other kernel images (P < 0.001 for both). Overall image quality in lung kernel images was comparable to the standard and superior to the bone kernel images (P < 0.001).

conclusionThe combination of lung kernel and DLIR-H in pancreatic protocol CT improves both quantitative and qualitative sharpness of the pancreas and tumor boundaries while maintaining the overall image quality.

Indexed as

Carcinoma, Pancreatic DuctalDeep LearningPancreatic NeoplasmsRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedAgedAged, 80 and overContrast MediaFeasibility StudiesFemaleHumansMaleMiddle AgedPancreasRetrospective StudiesContrast MediaComputed tomographyContrast mediaDeep learningIodine

Identifiers

PMID41557181

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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.