Chaudhari Akshay S, Kogan Feliks, Pedoia Valentina, Majumdar Sharmila, Gold Garry E, Hargreaves Brian A
Department of Radiology, Stanford University, Stanford, California, USA.
Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA.
J Magn Reson Imaging. 2020 Nov;52(5):1321-1339. doi: 10.1002/jmri.26991. Epub 2019 Nov 21.
Osteoarthritis (OA) of the knee is a major source of disability that has no known treatment or cure. Morphological and compositional MRI is commonly used for assessing the bone and soft tissues in the knee to enhance the understanding of OA pathophysiology. However, it is challenging to extend these imaging methods and their subsequent analysis techniques to study large population cohorts due to slow and inefficient imaging acquisition and postprocessing tools. This can create a bottleneck in assessing early OA changes and evaluating the responses of novel therapeutics. The purpose of this review article is to highlight recent developments in tools for enhancing the efficiency of knee MRI methods useful to study OA. Advances in efficient MRI data acquisition and reconstruction tools for morphological and compositional imaging, efficient automated image analysis tools, and hardware improvements to further drive efficient imaging are discussed in this review. For each topic, we discuss the current challenges as well as potential future opportunities to alleviate these challenges. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 3.
膝关节骨关节炎（OA）是导致残疾的主要原因，目前尚无已知的治疗方法或治愈手段。形态学和成分MRI常用于评估膝关节的骨骼和软组织，以加深对OA病理生理学的理解。然而，由于成像采集和后处理工具缓慢且效率低下，将这些成像方法及其后续分析技术扩展到大规模人群队列研究具有挑战性。这可能会在评估早期OA变化和评估新型疗法的反应方面造成瓶颈。这篇综述文章的目的是强调用于提高对研究OA有用的膝关节MRI方法效率的工具的最新进展。本文综述了形态学和成分成像的高效MRI数据采集和重建工具、高效的自动图像分析工具以及进一步推动高效成像的硬件改进。对于每个主题，我们讨论了当前的挑战以及缓解这些挑战的潜在未来机会。证据水平：5 技术疗效阶段：3。
