Photon-counting CT versus conventional CT for COPD quantifications: intra-scanner optimization and inter-scanner assessments using virtual imaging trials.

Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease and a major cause of death and disability worldwide. Quantitative CT is a powerful tool to better understand the heterogeneity and severity of this disease. Quantitative CT is being increasingly used in COPD research, and the recent advancements in CT technology have made it even more encouraging. One recent advancement has been the development of photon-counting detectors, offering higher spatial resolution, higher image contrast, and lower noise levels in the images. However, the quantification performance of this new technology compared to conventional scanners remains unknown. Additionally, different protocol settings (e.g., different dose levels, slice thicknesses, reconstruction kernels and algorithms) affect quantifications in an unsimilar fashion. This study investigates the potential advantages of photon-counting CT (PCCT) against conventional energy-integrating detector (EID) CT and explores the effects of protocol settings on lung density quantifications in COPD patients. This study was made possible using a virtual imaging platform, taking advantage of anthropomorphic phantoms with COPD (COPD-XCAT) and a scanner-specific CT simulator (DukeSim). Having the physical and geometrical properties of three Siemens commercial scanners (Flash, Force for EID and NAEOTOM Alpha for PCCT) modeled, we simulated CT images of ten COPD-XCAT phantoms at 0.63 and 3.17 mGy dose levels and reconstructed at three levels of kernel sharpness. The simulated CT images were quantified in terms of "Lung mean absolute error (MAE)," "LAA -950," "Perc 15," "Lung mass" imaging biomarkers and compared against the ground truth values of the phantoms. The intra-scanner assessment demonstrated the superior qualitative and quantitative performance of the PCCT scanner over the conventional scanners (21.01% and 22.74% mean lung MAE improvement, and 53.97% and 68.13% mean LAA -950 error improvement compared to Flash and Force). The results also showed that higher mAs, thinner slices, smoother kernels, and iterative reconstruction could lead to more accurate and precise quantification scores. This study underscored the qualitative and quantitative benefits of PCCT against conventional EID scanners as well as the importance of optimal protocol choice within scanners for more accurate quantifications.