医学
流线、条纹线和路径线
磁共振成像
矢量流
实时核磁共振成像
流量(数学)
放射科
人工智能
计算机科学
图像分割
分割
几何学
数学
热力学
物理
作者
Arshid Azarine,P. Garçon,A. Stansal,Nadia Canepa,Giorgios Angelopoulos,S. Silvera,Daniel Sidi,V. Marteau,Marc Zins
出处
期刊:Radiographics
[Radiological Society of North America]
日期:2019-05-01
卷期号:39 (3): 632-648
被引量:94
标识
DOI:10.1148/rg.2019180091
摘要
In-plane phase-contrast (PC) imaging is now a routine component of MRI of regional blood flow in the heart and great vessels. In-plane PC MRI provides a volumetric, isotropic, time-resolved cine sequence that enables three-directional velocity encoding, a technique known as four-dimensional (4D) flow MRI. Recent advances in 4D flow MRI have shortened imaging times, while progress in big-data processing has improved dataset pre- and postprocessing, thereby increasing the feasibility of 4D flow MRI in clinical practice. Important technical issues include selection of the optimal velocity-encoding sensitivity before acquisition and preprocessing of the raw data for phase-offset corrections. Four-dimensional flow MRI provides unprecedented capabilities for comprehensive analysis of complex blood flow patterns using new visualization tools such as streamlines and velocity vectors. Retrospective multiplanar navigation enables flexible retrospective flow quantification through any plane across the volume with good accuracy. Current flow parameters include forward flow, reverse flow, regurgitation fraction, and peak velocity. Four-dimensional flow MRI also supplies advanced flow parameters of use for research, such as wall shear stress. The vigorous burgeoning of new applications indicates that 4D flow MRI is becoming an important imaging modality for cardiovascular disorders. This article reviews the main technical issues of 4D flow MRI and the different parameters provided by it and describes the main applications in cardiovascular diseases, including congenital heart disease, cardiac valvular disease, aortic disease, and pulmonary hypertension. Online supplemental material is available for this article. ©RSNA, 2019 See discussion on this article by Ordovas.
科研通智能强力驱动
Strongly Powered by AbleSci AI