Rogers Albert J, Reynbakh Olga, Ahmed Adnan, Chung Mina K, Charate Rishi, Yarmohammadi Hirad, Gopinathannair Rakesh, Khan Hassan, Lakkireddy Dhanunjaya, Leal Miguel, Srivatsa Uma, Trayanova Natalia, Wan Elaine Y
Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA.
Division of Cardiology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA.
Nat Cardiovasc Res. 2025 May;4(5):514-525. doi: 10.1038/s44161-025-00648-8. Epub 2025 May 13.
Rapid technological advancements in noninvasive and invasive imaging including echocardiography, computed tomography, magnetic resonance imaging and positron emission tomography have allowed for improved anatomical visualization and precise measurement of cardiac structure and function. These imaging modalities allow for evaluation of how cardiac substrate changes, such as myocardial wall thickness, fibrosis, scarring and chamber enlargement and/or dilation, have an important role in arrhythmia initiation and perpetuation. Here, we review the various imaging techniques and modalities used by clinical and basic electrophysiologists to study cardiac arrhythmia mechanisms, periprocedural planning, risk stratification and precise delivery of ablation therapy. We also review the use of artificial intelligence and machine learning to improve identification of areas for triggered activity and isthmuses in reentrant arrhythmias, which may be favorable ablation targets.
包括超声心动图、计算机断层扫描、磁共振成像和正电子发射断层扫描在内的无创和有创成像技术的快速发展,使得心脏结构和功能的解剖可视化得到改善,测量更加精确。这些成像方式能够评估心脏基质的变化,如心肌壁厚度、纤维化、瘢痕形成以及心房和/或心室扩大,这些变化在心律失常的发生和持续中起着重要作用。在此,我们综述了临床和基础电生理学家用于研究心律失常机制、围手术期规划、风险分层以及精确实施消融治疗的各种成像技术和方式。我们还综述了人工智能和机器学习在改善识别触发活动区域和折返性心律失常中的峡部方面的应用,这些区域可能是有利的消融靶点。