Velasco Carlos, Fletcher Thomas J, Botnar René M, Prieto Claudia
School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.
Front Cardiovasc Med. 2022 Sep 20;9:1009131. doi: 10.3389/fcvm.2022.1009131. eCollection 2022.
Magnetic resonance fingerprinting (MRF) is a fast MRI-based technique that allows for multiparametric quantitative characterization of the tissues of interest in a single acquisition. In particular, it has gained attention in the field of cardiac imaging due to its ability to provide simultaneous and co-registered myocardial T and T mapping in a single breath-held cardiac MRF scan, in addition to other parameters. Initial results in small healthy subject groups and clinical studies have demonstrated the feasibility and potential of MRF imaging. Ongoing research is being conducted to improve the accuracy, efficiency, and robustness of cardiac MRF. However, these improvements usually increase the complexity of image reconstruction and dictionary generation and introduce the need for sequence optimization. Each of these steps increase the computational demand and processing time of MRF. The latest advances in artificial intelligence (AI), including progress in deep learning and the development of neural networks for MRI, now present an opportunity to efficiently address these issues. Artificial intelligence can be used to optimize candidate sequences and reduce the memory demand and computational time required for reconstruction and post-processing. Recently, proposed machine learning-based approaches have been shown to reduce dictionary generation and reconstruction times by several orders of magnitude. Such applications of AI should help to remove these bottlenecks and speed up cardiac MRF, improving its practical utility and allowing for its potential inclusion in clinical routine. This review aims to summarize the latest developments in artificial intelligence applied to cardiac MRF. Particularly, we focus on the application of machine learning at different steps of the MRF process, such as sequence optimization, dictionary generation and image reconstruction.
磁共振指纹识别（MRF）是一种基于快速磁共振成像的技术，能够在一次采集过程中对感兴趣的组织进行多参数定量表征。特别是，由于其能够在单次屏气心脏MRF扫描中同时提供心肌T1和T2图谱以及其他参数，它在心脏成像领域受到了关注。在小型健康受试者群体和临床研究中的初步结果已经证明了MRF成像的可行性和潜力。目前正在进行研究以提高心脏MRF的准确性、效率和稳健性。然而，这些改进通常会增加图像重建和字典生成的复杂性，并引入序列优化的需求。这些步骤中的每一步都会增加MRF的计算需求和处理时间。人工智能（AI）的最新进展，包括深度学习的进步以及用于磁共振成像的神经网络的发展，现在为有效解决这些问题提供了一个机会。人工智能可用于优化候选序列，并减少重建和后处理所需的内存需求和计算时间。最近，所提出的基于机器学习的方法已被证明能够将字典生成和重建时间减少几个数量级。人工智能的此类应用应有助于消除这些瓶颈并加快心脏MRF的速度，提高其实用性，并使其有可能纳入临床常规。本综述旨在总结应用于心脏MRF的人工智能的最新进展。特别是，我们关注机器学习在MRF过程不同步骤中的应用，例如序列优化、字典生成和图像重建。
