Bustin Aurélien, Fuin Niccolo, Botnar René M, Prieto Claudia
Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile.
Front Cardiovasc Med. 2020 Feb 25;7:17. doi: 10.3389/fcvm.2020.00017. eCollection 2020.
Cardiac magnetic resonance (CMR) imaging is an important tool for the non-invasive assessment of cardiovascular disease. However, CMR suffers from long acquisition times due to the need of obtaining images with high temporal and spatial resolution, different contrasts, and/or whole-heart coverage. In addition, both cardiac and respiratory-induced motion of the heart during the acquisition need to be accounted for, further increasing the scan time. Several undersampling reconstruction techniques have been proposed during the last decades to speed up CMR acquisition. These techniques rely on acquiring less data than needed and estimating the non-acquired data exploiting some sort of prior information. Parallel imaging and compressed sensing undersampling reconstruction techniques have revolutionized the field, enabling 2- to 3-fold scan time accelerations to become standard in clinical practice. Recent scientific advances in CMR reconstruction hinge on the thriving field of artificial intelligence. Machine learning reconstruction approaches have been recently proposed to learn the non-linear optimization process employed in CMR reconstruction. Unlike analytical methods for which the reconstruction problem is explicitly defined into the optimization process, machine learning techniques make use of large data sets to learn the key reconstruction parameters and priors. In particular, deep learning techniques promise to use deep neural networks (DNN) to learn the reconstruction process from existing datasets in advance, providing a fast and efficient reconstruction that can be applied to all newly acquired data. However, before machine learning and DNN can realize their full potentials and enter widespread clinical routine for CMR image reconstruction, there are several technical hurdles that need to be addressed. In this article, we provide an overview of the recent developments in the area of artificial intelligence for CMR image reconstruction. The underlying assumptions of established techniques such as compressed sensing and low-rank reconstruction are briefly summarized, while a greater focus is given to recent advances in dictionary learning and deep learning based CMR reconstruction. In particular, approaches that exploit neural networks as implicit or explicit priors are discussed for 2D dynamic cardiac imaging and 3D whole-heart CMR imaging. Current limitations, challenges, and potential future directions of these techniques are also discussed.
心脏磁共振（CMR）成像是非侵入性评估心血管疾病的重要工具。然而，由于需要获取具有高时间和空间分辨率、不同对比度和/或全心覆盖的图像，CMR的采集时间较长。此外，采集过程中心脏的心脏运动和呼吸诱导运动都需要考虑在内，这进一步增加了扫描时间。在过去几十年中，已经提出了几种欠采样重建技术来加速CMR采集。这些技术依赖于采集比所需数据更少的数据，并利用某种先验信息估计未采集的数据。并行成像和压缩感知欠采样重建技术彻底改变了该领域，使2至3倍的扫描时间加速成为临床实践中的标准。CMR重建的最新科学进展取决于蓬勃发展的人工智能领域。最近已经提出了机器学习重建方法来学习CMR重建中使用的非线性优化过程。与在优化过程中明确定义重建问题的分析方法不同，机器学习技术利用大数据集来学习关键重建参数和先验信息。特别是，深度学习技术有望使用深度神经网络（DNN）提前从现有数据集中学习重建过程，提供一种快速高效的重建方法，可应用于所有新采集的数据。然而，在机器学习和DNN能够充分发挥其潜力并进入CMR图像重建的广泛临床常规之前，有几个技术障碍需要解决。在本文中，我们概述了人工智能在CMR图像重建领域的最新进展。简要总结了压缩感知和低秩重建等现有技术的基本假设，同时更关注基于字典学习和深度学习的CMR重建的最新进展。特别是，针对二维动态心脏成像和三维全心CMR成像，讨论了将神经网络用作隐式或显式先验的方法。还讨论了这些技术当前的局限性、挑战和潜在的未来方向。
