光子计数CT（photon-counting computed tomography， PCCT）是近十年来CT成像领域的一项革命性技术突破，相较于传统的能量积分探测器CT，PCCT是在探测器层面对单光子水平进行成像，具备更高的空间分辨率、更少的伪影及更精准的多能谱成像，在心血管疾病诊断方面展现出极大的应用前景，尤其是线束硬化伪影的降低和超高分辨率的实现，可进一步提高冠脉狭窄评估的特异度和阳性预测值，对支架内管腔再狭窄的精准评估、斑块成分的识别及易损斑块的识别表征也得益于此。PCCT可在低辐射剂量下获得稳定的钙化积分，且虚拟非增强算法支持在增强图像中获得可靠的钙化积分，有助于进一步降低辐射剂量。PCCT在冠脉周围脂肪影像组学分析中能够提高特征可重复性，虚拟非增强算法可准确评估心外膜脂肪体积并显著降低辐射剂量；PCCT在高时间分辨率下获取的能谱图像支持单期心肌细胞外容积分数（extracellular volume， ECV）测量，也可为经导管主动脉瓣植入/置换术术前规划及术后复查提供多维度解剖信息和功能参数。尽管PCCT在冠心病诊断及心肌组织定量分析中潜力巨大，其定量结果仍受重建参数（如卷积核、虚拟单能量水平、迭代等级）影响，目前尚缺乏统一的标准和多中心研究的验证，且高分辨率模式的辐射剂量增加等问题仍限制了其广泛应用。未来应进一步开展大样本、多中心前瞻性研究，优化成像参数、标准化后处理流程，并结合人工智能工具以提升PCCT在心血管疾病诊断中的临床应用价值。

Photon-counting computed tomography （PCCT） is a revolutionary technological breakthrough in CT imaging over the past decade. Compared with traditional energy-integrating detector CT, PCCT performs imaging at the single-photon level at the detector layer, offering higher spatial resolution, fewer artifacts, and more accurate spectral imaging. PCCT shows great application potential in the diagnosis of cardiovascular diseases, especially in reducing beam-hardening artifacts and achieving ultra-high spatial resolution, which can further improve the specifi-city and positive predictive value in the assessment of coronary artery stenosis. This also contributes to the accurate evaluation of in-stent restenosis， reliable identification of plaque components, and characterization of vulnerable plaques. PCCT can obtain stable calcium scoring at low radiation doses. The virtual non-contrast （VNC） algorithm supports reliable calcium scoring from contrast-enhanced images, further reducing the radiation dose. PCCT can improve the reproducibility of features in pericoronary fat radiomics analysis. The VNC algorithm can accurately assess epicardial fat volume and significantly reduce radiation dose. Spectral images acquired by PCCT at high temporal resolution enable single-phase measurement of myocardial extracellular volume. They can also provide multidimensional anatomical information and functional parameters for preoperative planning and postoperative follow-up of transcatheter aortic valve implantation/replacement （TAVI/TAVR）. Although PCCT holds great potential in the diagnosis of coronary artery disease and quantitative analysis of myocardial tissues, its quantitative results remain affected by reconstruction parameters such as convolution kernels, virtual monoenergetic levels, and iterative strength. Currently, a lack of unified standards and validation from multicenter studies, along with the increased radiation dose in ultra-high-resolution modes, still limits its wide clinical application. Future studies should focus on large-sample, multicenter prospective studies to optimize imaging parameters, standardize post-processing workflows, and integrate artificial intelligence tools to enhance the clinical application of PCCT in cardiovascular disease diagnosis.