New sensor could flag MR scan motion in real time, saving time between repeat exams

por Gus Iversen, Editor in Chief | June 05, 2024
MRI
This self-powered sensor (circled in yellow) attached to a headrest within an MRI machine could help make imaging collection more efficient by detecting patient movement in real time.
A novel self-powered sensor could improve the efficiency of MR scans by detecting patient movement in real time, ending the exam on the spot instead of completing the blurred scan.

MR scans require patients to remain completely still for several minutes, and any movement can compromise the resulting images, meaning another scan needs to be performed. This can be particularly challenging for pediatric or claustrophobic patients.

While traditional motion tracking technologies face challenges in the MR environment due to the interference of magnetic materials with the imaging technology, the new sensor utilizes a triboelectric nanogenerator (TENG), which generates power through static electricity produced by friction between polymers. This approach avoids the need for metal or magnetic components that could interfere with MR scans.
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The sensor is constructed with two layers of plastic film coated with graphite-based conductive ink, surrounding a central silicone layer. When these layers are pressed together, they generate an electrostatic charge that creates a current, which is transmitted through a wire.

Integrated into an MR table, the TENG-based sensor was tested under the headrest area. It successfully detected movements such as a person turning their head or raising it off the table. Upon detecting movement, the sensor transmitted a signal to a computer, triggering an audible alert, displaying a pop-up window on the technician's screen, and ceasing the MR scan.

The researchers believe that this technology could make MR scans more efficient and less frustrating for both patients and technicians by reducing the need for repeated procedures and improving image quality in a single scan.

The study, published in ACS Sensors, did not report any specific funding sources for this research.
The sensor was developed by researchers Li Tao, Zhiyi Wu, and colleagues.

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