Material Decomposition in Photon-Counting CT: A Deep Learning Approach Driven by Detector Physics and ASIC Modeling

Photon-counting computed tomography (PCCT) based on photon-counting detectors (PCDs) stands out as a cutting-edge CT technology, offering enhanced spatial resolution, reduced radiation dose, and advanced material decomposition capabilities. Despite its recognized advantages, challenges arise from real-world phenomena such as PCD charge-sharing effects, application-specific integrated circuit (ASIC) pile-up, and spectrum shift, introducing a disparity between actual physical effects and the assumptions made in ideal physics models. This misalignment can lead to substantial errors during image reconstruction processes, particularly in material decomposition. In this paper, we introduce a novel detector physics and ASIC model-guided deep learning system model tailored for PCCT. This model adeptly captures the comprehensive response of the PCCT system, encompassing both detector and ASIC responses. We present experimental results demonstrating the model's exceptional accuracy and robustness. Key advancements include reduced calibration errors, enhanced quality in material decomposition imaging, and improved quantitative consistency. This model represents a significant stride in bridging the gap between theoretical assumptions and practical complexities of PCCT, paving the way for more precise and reliable medical imaging.