Department of Radiology, Johns Hopkins University, F.M. Kirby Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
Department of Medical Radiation Physics, Lund University, Lund, Sweden.
J Magn Reson. 2019 Sep;306:55-65. doi: 10.1016/j.jmr.2019.07.034. Epub 2019 Jul 9.
Over the past decades, the field of in vivo magnetic resonance (MR) has built up an impressive repertoire of data acquisition and analysis technologies for anatomical, functional, physiological, and molecular imaging, the description of which requires many book volumes. As such it is impossible for a few authors to have an authoritative overview of the field and for a brief article to be inclusive. We will therefore focus mainly on data acquisition and attempt to give some insight into the principles underlying current advanced methods in the field and the potential for further innovation. In our view, the foreseeable future is expected to show continued rapid progress, for instance in imaging of microscopic tissue properties in vivo, assessment of functional and anatomical connectivity, higher resolution physiologic and metabolic imaging, and even imaging of receptor binding. In addition, acquisition speed and information content will continue to increase due to the continuous development of approaches for parallel imaging (including simultaneous multi-slice imaging), compressed sensing, and MRI fingerprinting. Finally, artificial intelligence approaches are becoming more realistic and will have a tremendous effect on both acquisition and analysis strategies. Together, these developments will continue to provide opportunity for scientific discovery and, in combination with large data sets from other fields such as genomics, allow the ultimate realization of precision medicine in the clinic.
在过去的几十年中,活体磁共振(MR)领域积累了令人印象深刻的解剖学、功能、生理和分子成像的数据采集和分析技术,这些技术的描述需要多本书籍的篇幅。因此,少数作者不可能对该领域有权威性的概述,也不可能涵盖一篇简短的文章。因此,我们将主要关注数据采集,并尝试深入了解该领域当前先进方法的原理以及进一步创新的潜力。在我们看来,预计未来将继续快速发展,例如在活体微观组织特性成像、功能和解剖连接评估、更高分辨率的生理和代谢成像,甚至受体结合成像方面。此外,由于并行成像(包括同时多层成像)、压缩感知和 MRI 指纹识别等方法的不断发展,采集速度和信息量将继续增加。最后,人工智能方法变得越来越现实,将对采集和分析策略产生巨大影响。这些发展将共同为科学发现提供机会,并与基因组学等其他领域的大数据集相结合,在临床上实现精准医学的最终目标。