Catapano F, Lisi C, Figliozzi S, Scialò V, Politi L S, Francone M
Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy.
Echocardiography. 2025 Jul;42(7):e70148. doi: 10.1111/echo.70148.
Advancements in coronary computed tomography angiography (CCTA) facilitated the transition from traditional histological approaches to comprehensive plaque burden assessment. Recent updates in the European Society of Cardiology (ESC) guidelines emphasize CCTA's role in managing chronic coronary syndrome by enabling detailed monitoring of atherosclerotic plaque progression. Limitations of conventional CCTA, such as spatial resolution challenges in accurately characterizing plaque components like thin-cap fibroatheromas and necrotic lipid-rich cores, are addressed with photon-counting detector CT (PCD-CT) technology. PCD-CT offers enhanced spatial resolution and spectral imaging, improving the detection and characterization of high-risk plaque features while reducing artifacts. The integration of artificial intelligence (AI) in plaque analysis enhances diagnostic accuracy through automated plaque characterization and radiomics. These technological advancements support a comprehensive approach to plaque assessment, incorporating hemodynamic evaluations, morphological metrics, and AI-driven analysis, thereby enabling personalized patient care and improved prediction of acute clinical events.
冠状动脉计算机断层扫描血管造影（CCTA）的进展推动了从传统组织学方法向全面斑块负荷评估的转变。欧洲心脏病学会（ESC）指南的最新更新强调了CCTA在管理慢性冠状动脉综合征中的作用，它能够对动脉粥样硬化斑块进展进行详细监测。传统CCTA存在局限性，比如在准确识别薄帽纤维粥样瘤和富含坏死脂质核心等斑块成分时面临空间分辨率挑战，而光子计数探测器CT（PCD-CT）技术解决了这些问题。PCD-CT提供了更高的空间分辨率和光谱成像，在减少伪影的同时，提高了对高危斑块特征的检测和识别能力。人工智能（AI）在斑块分析中的整合通过自动斑块特征描述和放射组学提高了诊断准确性。这些技术进步支持了一种全面的斑块评估方法，包括血流动力学评估、形态学指标和AI驱动的分析，从而实现个性化患者护理并改善急性临床事件的预测。
