Authors
Xiaopeng Yu, Qianyu Wu, Wenhui Qin, Tao Zhong, Mengqing Su, Jinglu Ma, Yikun Zhang, Xu Ji, Wenying Wang, Guotao Quan, Yanfeng Du, Yang Chen, Xiaochun Lai
Published in
IEEE transactions on medical imaging. Volume PP. Sep 04, 2025. Epub Sep 04, 2025.
Abstract
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.
PMID:
40907044
Bibliographic data and abstract were imported from PubMed on 05 Sep 2025.
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