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.

医学影像学中的光子计数计算机断层扫描（PCCT）是解决现有技术所面临的空间分辨率有限、电子噪声干扰和对比噪声比低的问题的创新性技术。本文综述了PCCT在临床前研究中的最新发展和应用，强调了其克服传统成像局限的潜力，特别是在改善乳腺细微异常检测方面的显著效果。文章通过分析现有的PCCT相关文献，全面阐述了该技术的主要特点以及其广泛的应用领域，并探讨了将深度学习和放射组学特征结合到PCCT中并进行成功数据处理的研究。同时，文章也讨论了该领域存在的挑战，并为未来的研究和改进医学影像技术铺平了道路。尽管目前关于该主题的文章数量有限，但由于PCCT在临床层面的最新整合，其潜在好处可应用于各种诊断应用。

深度光子计数计算机断层扫描深度学习应用综述