Soft Reconstruction Kernels Improve HCC Imaging on a Photon-Counting Detector CT

Rationale and Objectives: Hepatocellular carcinoma (HCC) is the only tumor entity that allows non-invasive diagnosis based on imaging without further histological proof. Therefore, excellent image quality is of utmost importance for HCC diagnosis. Novel photon-counting detector (PCD) CT improves image quality via noise reduction and higher spatial resolution, inherently providing spectral information. The aim of this study was to investigate these improvements for HCC imaging with triple-phase liver PCD-CT in a phantom and patient population study focusing on identification of the optimal reconstruction kernel. Materials and Methods: Phantom experiments were performed to analyze objective quality characteristics of the regular body and quantitative reconstruction kernels, each with four sharpness levels (36-40-44-48). For 24 patients with viable HCC lesions on PCD-CT, virtual monoenergetic images at 50 keV were reconstructed using these kernels. Quantitative image analysis included contrast-to-noise ratio (CNR) and edge sharpness. Three raters performed qualitative analyses evaluating noise, contrast, lesion conspicuity, and overall image quality. Results: In all contrast phases, the CNR was highest using the kernels with a sharpness level of 36 (all p < 0.05), with no significant influence on lesion sharpness. Softer reconstruction kernels were also rated better regarding noise and image quality (all p < 0.05). No significant differences were found in image contrast and lesion conspicuity. Comparing body and quantitative kernels with equal sharpness levels, there was no difference in image quality criteria, neither regarding in vitro nor in vivo analysis. Conclusion: Soft reconstruction kernels yield the best overall quality for the evaluation of HCC in PCD-CT. As the image quality of quantitative kernels with potential for spectral post-processing is not restricted compared to regular body kernels, they should be preferred.