External Hardware and Sensors, for ImprovedMRI

Abstract

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.

Preliminary Experience with Three Alternative Motion Sensors for 0.55 Tesla MR Imaging

“…The ultrasound experiments demonstrate that the OCM sensor can be used to detect motion in the bladder in addition to the measurement of respiratory and cardiac motion previously demonstrated [ 12 , 13 ]. Interestingly, the displacement curves obtained from different depths of the ultrasound data exhibit out-of-phase behavior.…”

“…Recently, two new sensors have emerged to address some of these limitations. One is a small ultrasound sensor, also called an Organ Configuration Motion (OCM) sensor [ 12 , 13 ], and the other is the Pilot-Tone, a radiofrequency transmitter [ 14 , 15 , 16 ]. Both have shown effectiveness in compensating for respiratory and cardiac motion, suggesting potential for tracking internal organ motion.…”

Subject-specific timing adaption in time-encoded arterial spin labeling imaging

“…If too much motion of the images was noticed during image registration, the described method could also be used to trigger a new acquisition of the last matrix row(s). Other prospective motion correction methods without [ 39 ] or with external devices [ 40 ] might also be applicable in the future.…”

Beat Pilot Tone (BPT): Simultaneous MRI and RF motion sensing at arbitrary frequencies

“…While it is possible to use only the MR data itself for correction, [6][7][8] external motion monitoring signals or images offer a rich source of information that may improve the quality and speed of the correction. [9][10][11][12][13][14][15][16] Existing motion sensing systems in MRI may have limitations in sensitivity, ease of general use, or patient comfort. Cameras 17,18 are accurate but require a line-of-sight path that is often blocked by the MRI coils and are limited in penetration depth.…”

“…Cameras 17,18 are accurate but require a line-of-sight path that is often blocked by the MRI coils and are limited in penetration depth. 9 NMR-and MRI-based navigators [4][5][6]12,13,16 require sequence modifications and are adopted in limited applications. 19 Radiofrequency (RF) sensing [20][21][22][23][24][25][26][27][28] can detect a variety of motion types with high sensitivity and without contact; however, existing solutions are lacking in either sensitivity or generality.…”