AI-based denoising improves image quality in HCC volume perfusion CT without affecting Milan classification

Abstract Background Noise and artifacts often compromise image quality and diagnostic accuracy in liver Volume Perfusion CT (VPCT), which can impact clinical decision-making in the diagnosis of hepatocellular carcinoma (HCC). AI-based denoising can potentially improve image quality in computed tomography imaging. Therefore, we aimed to evaluate the effects of AI denoising (AID) on image quality and Milan classification in VPCT compared to standard methods. Methods VPCT examinations from 100 patients acquired between 2017 and 2021 were retrospectively included in the analysis. Perfusion maps were reconstructed using original data (Origin), vendor-specific median filtration (Vendor), and AID. Two radiologists independently scored subjective image quality (image quality, diagnostic confidence, contrast, and sharpness). Objective image quality parameters (CT numbers, noise, contrast-to-noise ratios) and diameters of all HCC lesions were measured across all datasets. Additionally, the Milan classification was determined for each patient. Results AID enhanced subjective image quality (0.48 ± 0.29) compared to Origin (-0.31 ± 0.29, p-value < 0.001) and Vendor (-0.17 ± 0.24, p-value < 0.001). CNR was higher in AID (27.40 ± 2.98) compared to Origin (16.98 ± 1.54, p < 0.001) and Vendor (19.43 ± 1.79, p < 0.001). No significant differences were found regarding lesion diameter between Origin, Vendor, and AID (p > 0.999). The number and localization of HCC lesions were equal between Origin, Vendor, and AID. The different reconstruction methods did not affect Milan classification. Conclusions AID enhances the image quality of HCC liver VPCT without compromising diagnostic capabilities and may support the evaluation of VPCT in the diagnosis of HCC.