Ji Sooyeon, Yang Dongjin, Lee Jongho, Choi Seung Hong, Kim Hyeonjin, Kang Koung Mi
Electrical and Computer Engineering, Institute of Engineering Research, Seoul National University, Seoul, Republic of Korea.
Department of Radiology, Daegu Fatima Hospital, Daegu, Republic of Korea.
J Magn Reson Imaging. 2022 Apr;55(4):1013-1025. doi: 10.1002/jmri.27440. Epub 2020 Nov 13.
Synthetic MRI is a technique that synthesizes contrast-weighted images from multicontrast MRI data. There have been advances in synthetic MRI since the technique was introduced. Although a number of synthetic MRI methods have been developed for quantifying one or more relaxometric parameters and for generating multiple contrast-weighted images, this review focuses on several methods that quantify all three relaxometric parameters (T , T , and proton density) and produce multiple contrast-weighted images. Acquisition, quantification, and image synthesis techniques are discussed for each method. We discuss the image quality and diagnostic accuracy of synthetic MRI methods and their clinical applications in neuroradiology. Based on this analysis, we highlight areas that need to be addressed for synthetic MRI to be widely implemented in the clinic. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 1.
合成磁共振成像(Synthetic MRI)是一种从多对比度磁共振成像(MRI)数据中合成对比度加权图像的技术。自该技术问世以来,合成MRI已取得了进展。尽管已经开发了许多合成MRI方法来量化一个或多个弛豫测量参数并生成多个对比度加权图像,但本综述重点关注几种能够量化所有三个弛豫测量参数(T1、T2和质子密度)并产生多个对比度加权图像的方法。针对每种方法,讨论了采集、量化和图像合成技术。我们讨论了合成MRI方法的图像质量和诊断准确性及其在神经放射学中的临床应用。基于此分析,我们强调了合成MRI要在临床中广泛应用需要解决的领域。证据水平:5 技术效能阶段:1。