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Purpose <p>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.</p> Materials and methods <p>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.</p> Results <p>Pancreas-to-fat ERS was greater in lung kernel images than in standard and bone kernel images (<i>P</i> = 0.001). Pancreas-to-PDAC ERS was greater in lung kernel images than in other kernel images (<i>P</i> &lt; 0.001). Sharpness of the pancreas and tumor boundaries was better in lung kernel images than in other kernel images (<i>P</i> &lt; 0.001 for both). Overall image quality in lung kernel images was comparable to the standard and superior to the bone kernel images (<i>P</i> &lt; 0.001).</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2026年1月20日

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  • Nobuyuki Kawai,
  • Yoshifumi Noda,
  • Tetsuro Kaga,
  • Kota Matsuoka,
  • Shingo Omata,
  • Yukiko Takai,
  • Masashi Asano,
  • Toshiharu Miyoshi,
  • Takeshi Iwata,
  • Abdelazim Elsayed Elhelaly,
  • Hirohiko Imai,
  • Hiroki Kato,
  • Masayuki Matsuo

摘要

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.