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

Journal: Abdominal radiology (New York)
Published Date: Jan 20, 2026

Abstract

PURPOSE: This 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 METHODS: This 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. RESULTS: Pancreas-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). CONCLUSION: The 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.

Authors

  • Nobuyuki Kawai
    Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
  • Yoshifumi Noda
    Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
  • Tetsuro Kaga
    Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
  • Kota Matsuoka
    Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan.
  • Shingo Omata
    Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan.
  • Yukiko Takai
    Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
  • Masashi Asano
    Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
  • Toshiharu Miyoshi
    Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
  • Takeshi Iwata
    Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
  • Abdelazim Elsayed Elhelaly
    Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan.
  • Hirohiko Imai
    Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan.
  • Hiroki Kato
    Department of Nuclear Medicine and Tracer Kinetics, Osaka University Graduate School of Medicine, Osaka, Japan.
  • Masayuki Matsuo
    Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.

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