Medical imaging faces challenges such as limited spatial resolution, interference from electronic noise and poor contrast-to-noise ratios. Photon Counting Computed Tomography (PCCT) has emerged as a solution, addressing these issues with its innovative technology. This review delves into the recent developments and applications of PCCT in pre-clinical research, emphasizing its potential to overcome traditional imaging limitations. For example PCCT has demonstrated remarkable efficacy in improving the detection of subtle abnormalities in breast, providing a level of detail previously unattainable. Examining the current literature on PCCT, it presents a comprehensive analysis of the technology, highlighting the main features of scanners and their varied applications. In addition, it explores the integration of deep learning into PCCT, along with the study of radiomic features, presenting successful applications in data processing. While acknowledging these advances, it also discusses the existing challenges in this field, paving the way for future research and improvements in medical imaging technologies. Despite the limited number of articles on this subject, due to the recent integration of PCCT at a clinical level, its potential benefits extend to various diagnostic applications.

翻译：医学影像面临空间分辨率有限、电子噪声干扰及信噪比不足等挑战。光子计数计算机断层扫描技术作为解决方案应运而生，凭借其创新技术有效应对上述问题。本综述深入探讨光子计数CT在临床前研究中的最新进展与应用，着重阐述其突破传统成像局限的潜力。例如，PCCT在提升乳腺微细异常检测方面展现出卓越效能，可呈现此前难以企及的细节层次。通过梳理PCCT领域现有文献，本文对该技术进行系统分析，重点阐释扫描仪核心特征及其多样化应用场景。此外，本文还探讨了深度学习与PCCT的融合路径，以及影像组学特征研究，展示了数据处理领域的成功应用案例。在肯定技术突破的同时，本文也剖析了该领域现存挑战，为未来医学影像技术改进指明研究方向。尽管受限于PCCT近期才实现临床级应用，相关文献数量尚少，但其潜在价值已可惠及多种诊断场景。