心血管疾病(CVDs)是由心脏或血管结构功能异常导致的循环系统疾病，影像学检查是其核心诊断手段。传统影像技术(如超声心动图、血管造影CT、磁共振成像及正电子发射断层扫描)在CVDs诊断中发挥关键作用，但受限于分辨率、侵入性或功能评估能力。新兴的血管内超声成像与光学相干断层扫描技术通过融合声学与光学成像优势，实现了血管深层结构可视化及斑块微病变的精准检测，显著提升了CVDs早期诊断与治疗指导效能。近年来，人工智能(AI)驱动的多模态影像融合分析进一步突破技术瓶颈：通过整合多源数据构建风险预测模型，不仅优化诊断准确性，更实现标准化、自动化输出，推动CVDs筛查向大规模、高精度、高效率方向发展。本文系统综述传统及AI赋能影像技术在CVDs诊断中的研究进展，旨在为临床研究提供新思路，并为未来精准诊疗方案的开发提供理论依据。

Cardiovascular diseases (CVDs) are circulatory system disorders caused by abnormal structures or functions of the heart or blood vessels, and imaging examinations are the core diagnostic methods for them. Traditional imaging techniques (such as echocardiography, angiography CT, magnetic resonance imaging, and positron emission tomography) play a key role in the diagnosis of CVDs, but they are limited by resolution, invasiveness or functional assessment capabilities. The emerging intravascular ultrasound imaging and optical coherence tomography technologies, by integrating the advantages of acoustic and optical imaging, have enable visualization of deep vascular structures and the precise detection of plaque microlesions, significantly enhancing the early diagnosis and the efficiency of treatment guidance of CVDs. In recent years, artificial intelligence (AI)-driven multimodal image fusion analysis has further broken through technical bottlenecks: by integrating multi-source data to build risk prediction models, it not only optimizes diagnostic accuracy but also achieves standardized and automated reporting, promoting the development of CVDs screening towards large-scale, high-precision, and high-efficiency directions. This article systematically reviews the research progress of traditional and AI-enabled imaging techniques in the diagnosis of CVDs, aiming to provide new ideas for clinical research and theoretical basis for the development of future precise diagnosis and treatment plans.