Ismail Tevfik F, Strugnell Wendy, Coletti Chiara, Božić-Iven Maša, Weingärtner Sebastian, Hammernik Kerstin, Correia Teresa, Küstner Thomas
School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Cardiology Department, Guy's and St Thomas' Hospital, London, United Kingdom.
Front Cardiovasc Med. 2022 Mar 3;9:826283. doi: 10.3389/fcvm.2022.826283. eCollection 2022.
Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality, causing over 17. 9 million deaths worldwide per year with associated costs of over $800 billion. Improving prevention, diagnosis, and treatment of CVD is therefore a global priority. Cardiovascular magnetic resonance (CMR) has emerged as a clinically important technique for the assessment of cardiovascular anatomy, function, perfusion, and viability. However, diversity and complexity of imaging, reconstruction and analysis methods pose some limitations to the widespread use of CMR. Especially in view of recent developments in the field of machine learning that provide novel solutions to address existing problems, it is necessary to bridge the gap between the clinical and scientific communities. This review covers five essential aspects of CMR to provide a comprehensive overview ranging from CVDs to CMR pulse sequence design, acquisition protocols, motion handling, image reconstruction and quantitative analysis of the obtained data. (1) The basic MR physics of CMR is introduced. Basic pulse sequence building blocks that are commonly used in CMR imaging are presented. Sequences containing these building blocks are formed for parametric mapping and functional imaging techniques. Commonly perceived artifacts and potential countermeasures are discussed for these methods. (2) CMR methods for identifying CVDs are illustrated. Basic anatomy and functional processes are described to understand the cardiac pathologies and how they can be captured by CMR imaging. (3) The planning and conduct of a complete CMR exam which is targeted for the respective pathology is shown. Building blocks are illustrated to create an efficient and patient-centered workflow. Further strategies to cope with challenging patients are discussed. (4) Imaging acceleration and reconstruction techniques are presented that enable acquisition of spatial, temporal, and parametric dynamics of the cardiac cycle. The handling of respiratory and cardiac motion strategies as well as their integration into the reconstruction processes is showcased. (5) Recent advances on deep learning-based reconstructions for this purpose are summarized. Furthermore, an overview of novel deep learning image segmentation and analysis methods is provided with a focus on automatic, fast and reliable extraction of biomarkers and parameters of clinical relevance.
心血管疾病（CVD）是发病和死亡的首要单一原因，每年在全球导致超过1790万人死亡，相关成本超过8000亿美元。因此，改善心血管疾病的预防、诊断和治疗是全球的优先事项。心血管磁共振成像（CMR）已成为评估心血管解剖结构、功能、灌注和存活能力的一项重要临床技术。然而，成像、重建和分析方法的多样性和复杂性对CMR的广泛应用造成了一些限制。特别是鉴于机器学习领域的最新发展为解决现有问题提供了新的解决方案，弥合临床和科学界之间的差距很有必要。本综述涵盖了CMR的五个基本方面，以提供从心血管疾病到CMR脉冲序列设计、采集协议、运动处理、图像重建以及所得数据的定量分析的全面概述。（1）介绍了CMR的基本磁共振物理原理。展示了CMR成像中常用的基本脉冲序列构建模块。包含这些构建模块的序列被用于参数映射和功能成像技术。讨论了这些方法常见的伪影和潜在的应对措施。（2）阐述了用于识别心血管疾病的CMR方法。描述了基本解剖结构和功能过程，以了解心脏病理状况以及如何通过CMR成像来捕捉它们。（3）展示了针对各自病理情况的完整CMR检查的规划和实施过程。说明了构建模块，以创建高效且以患者为中心的工作流程。还讨论了应对具有挑战性患者的进一步策略。（4）介绍了成像加速和重建技术，这些技术能够获取心动周期的空间、时间和参数动态信息。展示了呼吸和心脏运动策略的处理以及它们如何集成到重建过程中。（5）总结了基于深度学习的用于此目的的重建的最新进展。此外，还提供了新颖的深度学习图像分割和分析方法的概述，重点是自动、快速且可靠地提取具有临床相关性的生物标志物和参数。
