光子计数CT（photon-counting computed tomography， PCCT）是一种基于新型光子计数探测器的前沿CT成像技术。与传统的能量积分探测器CT（energy-integrating detector ct， EID-CT）相比，PCCT在辐射剂量利用效率、图像空间分辨率及能谱分辨能力等方面具有显著优势，有望革新胰腺疾病的影像学诊断模式。本文聚焦PCCT在胰腺成像中的最新研究进展，重点探讨其在胰腺细微解剖结构显示、胰腺及其周围血管网络与胰胆管系统可视化、良恶性病变评估及肿瘤异质性定量评估等方面的临床应用价值。PCCT的核心技术原理，包括光子计数探测器的高光子通量处理、K边成像以及多能量阈值数据采集模式等物理特性。基于此，本文归纳了针对胰腺成像的优化策略，涵盖扫描参数设置和多模态后处理技术的临床应用。PCCT能够在有效降低辐射剂量的同时突破空间分辨率瓶颈，提升囊性病灶检出率。PCCT低能级单能量重建模式可增强实性肿瘤对比度，并优化分支血管与肿瘤滋养血管的显示。本文综合近年具有代表性的临床研究及初步验证性试验结果，进一步分析PCCT在临床转化过程中面临的关键挑战及应对策略，展望该技术在胰腺疾病精准诊断、个体化治疗决策支持与人工智能-大数据模型构建方面的未来发展方向，旨在为PCCT在胰腺成像领域的推广应用提供理论基础与实践参考。

Photon-counting computed tomography (PCCT) is an advanced CT imaging technology based on novel photon-counting detectors. Compared to traditional energy-integrating detector CT (EID-CT), PCCT demonstrates significant advantages in radiation dose utilization efficiency, image spatial resolution, and spectral resolution, and is expected to revolutionize imaging diagnostic paradigms for pancreatic diseases. This study systematically reviews the latest research advances in PCCT for pancreatic imaging, with a focus on its clinical application value in displaying fine pancreatic anatomical structures, visualizing the pancreas, surrounding vascular networks, and pancreaticobiliary systems, evaluating benign and malignant lesions, and quantitatively assessing tumor heterogeneity. The core technical principles of PCCT include physical characteristics such as high photon-flux processing by photon-counting detectors, K-edge imaging, and multi-energy threshold data acquisition modes. On this basis, this study summarizes optimization strategies for pancreatic imaging, including scan parameter settings and the clinical application of multimodal post-processing techniques. PCCT can effectively reduce radiation dose while overcoming the spatial resolution bottleneck, thereby improving the detection rate of cystic lesions. The low-energy monoenergetic reconstruction mode of PCCT can enhance the contrast of solid tumors and optimize the visualization of branching and tumor-feeding vessels. By integrating representative clinical studies and preliminary validation trials in recent years, this study further analyzes key challenges and corresponding strategies during the clinical translation of PCCT. Additionally, it explores the future directions of this technology in the precise diagnosis of pancreatic diseases, personalized treatment decision support, and the development of AI-big data models, aiming to provide a theoretical foundation and practical reference for promoting PCCT applications in the field of pancreatic imaging.