Rodriguez-Lozano Patricia F, Waheed Anam, Evangelou Sotirios, Kolossváry Márton, Shaikh Kashif, Siddiqui Saira, Stipp Lauren, Lakshmanan Suvasini, Wu En-Haw, Nurmohamed Nick S, Orbach Ady, Baliyan Vinit, de Matos Joao Francisco Ribeiro Gavina, Trivedi Siddharth J, Madan Nidhi, Villines Todd C, Ihdayhid Abdul Rahman
Department of Medicine, Cardiovascular Division, University of Virginia Health, Charlottesville, VA, USA; Department of Radiology and Medical Imaging, University of Virginia Health, Charlottesville, VA, USA.
Medstar Heart and Vascular Center, Washington Hospital Center, Georgetown University Hospital, Washington, DC, USA.
J Cardiovasc Comput Tomogr. 2025 Jul-Aug;19(4):397-408. doi: 10.1016/j.jcct.2025.05.241. Epub 2025 Jun 13.
The integration of computed tomography-derived fractional flow reserve (CT-FFR), utilizing computational fluid dynamics and artificial intelligence (AI) in routine coronary computed tomographic angiography (CCTA), presents a promising approach to enhance evaluations of functional lesion severity. Extensive evidence underscores the diagnostic accuracy, prognostic significance, and clinical relevance of CT-FFR, prompting recent clinical guidelines to recommend its combined use with CCTA for selected individuals with with intermediate stenosis on CCTA and stable or acute chest pain. This manuscript critically examines the existing clinical evidence, evaluates the diagnostic performance, and outlines future perspectives for integrating noninvasive assessments of coronary anatomy and physiology. Furthermore, it serves as a practical guide for medical imaging professionals by addressing common pitfalls and challenges associated with CT-FFR while proposing potential solutions to facilitate its successful implementation in clinical practice.
在常规冠状动脉计算机断层血管造影（CCTA）中，利用计算流体动力学和人工智能（AI）整合计算机断层扫描衍生的血流储备分数（CT-FFR），为加强对功能性病变严重程度的评估提供了一种很有前景的方法。大量证据强调了CT-FFR的诊断准确性、预后意义和临床相关性，促使近期临床指南建议，对于CCTA显示中度狭窄且有稳定或急性胸痛的特定个体，将CT-FFR与CCTA联合使用。本文对现有临床证据进行了批判性审视，评估了诊断性能，并概述了整合冠状动脉解剖和生理学无创评估的未来前景。此外，本文通过解决与CT-FFR相关的常见陷阱和挑战，同时提出潜在解决方案以促进其在临床实践中的成功应用，为医学影像专业人员提供了实用指南。
