A Physics-ASIC Architecture-Driven Deep Learning Photon-Counting Detector Model Under Limited Data

Photon-counting computed tomography (PCCT) based on photon-counting detectors (PCDs) represents a cutting-edge CT technology, offering higher spatial resolution, reduced radiation dose, and advanced material decomposition capabilities. Accurately modeling complex and nonlinear PCDs under limited calibration data becomes one of the challenges hindering the widespread accessibility of PCCT. This paper introduces a physics-ASIC architecture-driven deep learning detector model for PCDs. This model adeptly captures the comprehensive response of the PCD, encompassing both sensor and ASIC responses. We present experimental results demonstrating the model's exceptional accuracy and robustness with limited calibration data. Key advancements include reduced calibration errors, reasonable physics-ASIC parameters estimation, and high-quality and high-accuracy material decomposition images.