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

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.