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Ultra-high resolution computed tomography with deep-learning-reconstruction: diagnostic ability in the assessment of gastric cancer and the depth of invasion

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Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

To evaluate the image quality of ultra-high-resolution CT (U-HRCT) images reconstructed using an improved deep-learning-reconstruction (DLR) method. Additionally, we assessed the utility of U-HRCT in visualizing gastric wall structure, detecting gastric cancer, and determining the depth of invasion.

Methods

Forty-six patients with resected gastric cancer who underwent preoperative contrast-enhanced U-HRCT were included. The image quality of U-HRCT reconstructed using three different methods (standard DLR [AiCE], improved DLR—AiCE-Body Sharp [improved AiCE-BS], and hybrid-IR [AIDR3D]) was compared. Visualization of the gastric wall’s three-layered structure in four regions and the visibility of gastric cancers were compared between U-HRCT and conventional HRCT (C-HRCT). The diagnostic ability of U-HRCT with the improved AiCE-BS for determining the depth of invasion of gastric cancers was assessed using postoperative pathology specimens.

Results

The mean noise level of U-HRCT with the improved AiCE-BS was significantly lower than that of the other two methods (p < 0.001). The overall image quality scores of the improved AiCE-BS images were significantly higher (p < 0.001). U-HRCT demonstrated significantly better conspicuity scores for the three-layered structure of the gastric wall than C-HRCT in all regions (p < 0.001). In addition, U-HRCT was found to have superior visibility of gastric cancer in comparison to C-HRCT (p < 0.001). The correct diagnostic rates for determining the depth of invasion of gastric cancer using C-HRCT and U-HRCT were 80%.

Conclusions

U-HRCT reconstructed with the improved AiCE-BS provides clearer visualization of the three-layered gastric wall structure than other reconstruction methods. It is also valuable for detecting gastric cancer and assessing the depth of invasion.

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Funding

No funding was received for conducting this study.

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Authors and Affiliations

  1. Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, 755-8505, Japan

    Masaya Tanabe, Masahiro Tanabe, Hideko Onoda, Masatoshi Nakashima, Mayumi Higashi, Yosuke Kawano, Keiko Hideura, Takaaki Ueda, Taiga Kobayashi & Katsuyoshi Ito

  2. Department of Radiology, Kanmon Medical Center, Shimonoseki, Yamaguchi, Japan

    Masaya Tanabe

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  1. Masaya Tanabe
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  2. Masahiro Tanabe
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  4. Masatoshi Nakashima
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  6. Yosuke Kawano
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  10. Katsuyoshi Ito
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Corresponding author

Correspondence to Masahiro Tanabe.

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This retrospective study was approved by the institutional review board, and informed consent was waived.

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Tanabe, M., Tanabe, M., Onoda, H. et al. Ultra-high resolution computed tomography with deep-learning-reconstruction: diagnostic ability in the assessment of gastric cancer and the depth of invasion. Abdom Radiol (2024). https://doi.org/10.1007/s00261-024-04363-z

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  • DOI: https://doi.org/10.1007/s00261-024-04363-z

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