Machine learning (ML) is making a dramatic impact on cardiovascular magnetic resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR where ML, and deep learning in particular, can assist clinicians and engineers in improving imaging efficiency, quality, image analysis and interpretation, as well as patient evaluation. We discuss recent developments in the field of ML relevant to CMR in the areas of image acquisition & reconstruction, image analysis, diagnostic evaluation and derivation of prognostic information. To date, the main impact of ML in CMR has been to significantly reduce the time required for image segmentation and analysis. Accurate and reproducible fully automated quantification of left and right ventricular mass and volume is now available in commercial products. Active research areas include reduction of image acquisition and reconstruction time, improving spatial and temporal resolution, and analysis of perfusion and myocardial mapping. Although large cohort studies are providing valuable data sets for ML training, care must be taken in extending applications to specific patient groups. Since ML algorithms can fail in unpredictable ways, it is important to mitigate this by open source publication of computational processes and datasets. Furthermore, controlled trials are needed to evaluate methods across multiple centers and patient groups.

中文翻译：

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心血管磁共振中的机器学习：基本概念和应用。

机器学习（ML）以多种方式对心血管磁共振（CMR）产生了巨大影响。这篇综述旨在强调CMR的主要领域，其中ML（尤其是深度学习）可以帮助临床医生和工程师提高成像效率，质量，图像分析和解释以及患者评估。我们讨论了与CMR相关的ML领域的最新发展，包括图像采集与重建，图像分析，诊断评估以及预后信息的获取。迄今为止，ML在CMR中的主要影响是显着减少了图像分割和分析所需的时间。现在，在商业产品中可以对左心室和右心室的质量和体积进行精确且可重现的全自动定量分析。积极的研究领域包括减少图像获取和重建时间，提高空间和时间分辨率以及对灌注和心肌作图进行分析。尽管大型队列研究为ML培训提供了有价值的数据集，但在将应用程序扩展到特定患者组时必须小心。由于ML算法可能会以无法预测的方式失败，因此重要的是通过开源发布计算过程和数据集来减轻这种情况。此外，还需要进行对照试验来评估多个中心和患者组的方法。在将应用程序扩展到特定患者组时必须小心。由于ML算法可能会以无法预测的方式失败，因此重要的是通过开源发布计算过程和数据集来减轻这种情况。此外，还需要进行对照试验来评估跨多个中心和患者组的方法。在将应用程序扩展到特定患者组时必须小心。由于ML算法可能会以无法预测的方式失败，因此重要的是通过开源发布计算过程和数据集来减轻这种情况。此外，还需要进行对照试验来评估多个中心和患者组的方法。