Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.
Memorial Sloan-Kettering Cancer Center, New York, New York, USA.
J Magn Reson Imaging. 2018 Nov;48(5):1185-1198. doi: 10.1002/jmri.26274. Epub 2018 Oct 8.
More than a decade after the introduction of compressed sensing (CS) in MRI, researchers are still working on ways to translate it into different research and clinical applications. The greatest advantage of CS in MRI is the reduced amount of k-space data needed to reconstruct images, which can be exploited to reduce scan time or to improve spatial resolution and volumetric coverage. Efficient data acquisition using CS is extremely important for compositional mapping of the musculoskeletal system in general and knee cartilage mapping techniques in particular. High-resolution quantitative information about tissue biochemical composition could be obtained in just a few minutes using CS MRI. However, in order to make this goal a reality, some issues still need to be addressed. In this article we review the current state of the art of CS methods for rapid compositional mapping of knee cartilage. Specifically, data acquisition strategies, image reconstruction algorithms, and data fitting models are discussed. Different CS studies for T and T mapping of knee cartilage are reviewed, with illustrative results. Future directions, opportunities, and challenges of rapid compositional mapping techniques are also discussed. Level of Evidence: 4 Technical Efficacy: Stage 6 J. Magn. Reson. Imaging 2018;47:1185-1198.
压缩感知（CS）在 MRI 中的应用已经超过了十年，研究人员仍在努力寻找将其转化为不同研究和临床应用的方法。CS 在 MRI 中的最大优势是重建图像所需的 k 空间数据量减少，这可以用于缩短扫描时间或提高空间分辨率和容积覆盖范围。在骨骼肌肉系统的成分映射中，特别是在膝关节软骨映射技术中，使用 CS 进行高效的数据采集是非常重要的。使用 CS MRI 可以在短短几分钟内获得关于组织生化成分的高分辨率定量信息。然而，为了实现这一目标，仍有一些问题需要解决。本文综述了用于快速膝关节软骨成分映射的 CS 方法的最新进展。具体讨论了数据采集策略、图像重建算法和数据拟合模型。回顾了不同用于膝关节软骨 T1 和 T2 映射的 CS 研究，并给出了说明性结果。还讨论了快速成分映射技术的未来方向、机遇和挑战。磁共振成像杂志 2018 年；47:1185-1198。
