Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
J Magn Reson Imaging. 2023 Mar;57(3):690-705. doi: 10.1002/jmri.28472. Epub 2022 Nov 3.
Complex engineered systems are often equipped with suites of sensors and ancillary devices that monitor their performance and maintenance needs. MRI scanners are no different in this regard. Some of the ancillary devices available to support MRI equipment, the ones of particular interest here, have the distinction of actually participating in the image acquisition process itself. Most commonly, such devices are used to monitor physiological motion or variations in the scanner's imaging fields, allowing the imaging and/or reconstruction process to adapt as imaging conditions change. "Classic" examples include electrocardiography (ECG) leads and respiratory bellows to monitor cardiac and respiratory motion, which have been standard equipment in scan rooms since the early days of MRI. Since then, many additional sensors and devices have been proposed to support MRI acquisitions. The main physical properties that they measure may be primarily "mechanical" (eg acceleration, speed, and torque), "acoustic" (sound and ultrasound), "optical" (light and infrared), or "electromagnetic" in nature. A review of these ancillary devices, as currently available in clinical and research settings, is presented here. In our opinion, these devices are not in competition with each other: as long as they provide useful and unique information, do not interfere with each other and are not prohibitively cumbersome to use, they might find their proper place in future suites of sensors. In time, MRI acquisitions will likely include a plurality of complementary signals. A little like the microbiome that provides genetic diversity to organisms, these devices can provide signal diversity to MRI acquisitions and enrich measurements. Machine-learning (ML) algorithms are well suited at combining diverse input signals toward coherent outputs, and they could make use of all such information toward improved MRI capabilities. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.
复杂的工程系统通常配备有一系列传感器和辅助设备,用于监测其性能和维护需求。磁共振成像 (MRI) 扫描仪在这方面也不例外。有一些辅助设备可用于支持 MRI 设备,这里特别感兴趣的是那些实际上参与图像采集过程本身的设备。这些设备通常用于监测生理运动或扫描仪成像场的变化,使成像和/或重建过程能够适应成像条件的变化。“经典”的例子包括心电图 (ECG) 导联和呼吸波纹管,用于监测心脏和呼吸运动,自 MRI 早期以来,它们一直是扫描室的标准设备。从那时起,已经提出了许多其他传感器和设备来支持 MRI 采集。它们测量的主要物理特性可能主要是“机械”(例如加速度、速度和扭矩)、“声学”(声音和超声波)、“光学”(光和红外线)或“电磁”性质。这里介绍了这些辅助设备在临床和研究环境中的当前可用性。我们认为,这些设备之间不是竞争关系:只要它们提供有用且独特的信息,不会相互干扰,并且使用起来不是过于繁琐,它们就有可能在未来的传感器套件中找到自己的位置。随着时间的推移,MRI 采集可能会包含多种互补信号。有点像微生物组为生物体提供遗传多样性一样,这些设备可以为 MRI 采集提供信号多样性,并丰富测量结果。机器学习 (ML) 算法非常适合将多种输入信号组合为一致的输出,并且可以利用所有这些信息来提高 MRI 的能力。证据水平:2 技术功效:阶段 1。