Toward Contact-Free Motion Sensing Technique in Low-Field MRI Using Beat Pilot Tone

Low-field magnetic resonance imaging (MRI) suffers from fundamentally low signal-to-noise ratio (SNR), which is typically mitigated by acquiring multiple sets of imaging data and averaging them. However, this approach lengthens the scan time and can make images highly susceptible to motion artifacts. In this article, a novel contact-free radio frequency (RF) motion sensor beat pilot tone (BPT), which has high temporal resolution and is easy to be integrated into both MR system and imaging workflow, was first introduced in a 0.25 T permanent C-shaped MRI system. We improved the portability of BPT by integrating it into a 10 cm × 5 cm × 2 cm device and retained its adjustability in power and frequency. We compared BPT with another contact-free motion sensing technology, pilot tone (PT), for detecting the same motion. The results indicate that the motion modulation achieved by BPT is over six times that of PT, demonstrating that BPT has higher detection sensitivity. A phantom experiment was performed to suggest BPT has no adverse impact on imaging. We conducted series of in vivo experiments and demonstrated that BPT has the ability to detect different motion patterns, such as shaking and nodding head motion, respiratory motion, and cardiac motion. With BPT signal guidance, we successfully reconstructed motion-resolved images of a head scan under in-plane motion and abdominal imaging under free-breathing. SNR of images is higher than previous low-field images, and the motion artifacts are reduced, which shows the potential for wide application of BPT in low-field MRI.

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Publication Details

Journal
IEEE Transactions on Instrumentation and Measurement
Published
2025-01-01
DOI
https://doi.org/10.1109/tim.2025.3529065
Citations
1
Primary Topic
Advanced MRI Techniques and Applications
Type
article
Field-Weighted Citation Impact
1.36

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article

Toward Contact-Free Motion Sensing Technique in Low-Field MRI Using Beat Pilot Tone

Suen Chen, Sijie Zhong, Yueqi Qiu, Suma Anand et al.
1 citations
IEEE Transactions on Instrumentation and Measurement
Advanced MRI Techniques and Applications
1.36
article

Toward Contact-Free Motion Sensing Technique in Low-Field MRI Using Beat Pilot Tone

Suen Chen, Sijie Zhong, Yueqi Qiu, Suma Anand, Michael Lustig, Zhiyong Zhang, Hao Chen, Haoyu Sun
article en
1 citations

Abstract

Low-field magnetic resonance imaging (MRI) suffers from fundamentally low signal-to-noise ratio (SNR), which is typically mitigated by acquiring multiple sets of imaging data and averaging them. However, this approach lengthens the scan time and can make images highly susceptible to motion artifacts. In this article, a novel contact-free radio frequency (RF) motion sensor beat pilot tone (BPT), which has high temporal resolution and is easy to be integrated into both MR system and imaging workflow, was first introduced in a 0.25 T permanent C-shaped MRI system. We improved the portability of BPT by integrating it into a 10 cm × 5 cm × 2 cm device and retained its adjustability in power and frequency. We compared BPT with another contact-free motion sensing technology, pilot tone (PT), for detecting the same motion. The results indicate that the motion modulation achieved by BPT is over six times that of PT, demonstrating that BPT has higher detection sensitivity. A phantom experiment was performed to suggest BPT has no adverse impact on imaging. We conducted series of in vivo experiments and demonstrated that BPT has the ability to detect different motion patterns, such as shaking and nodding head motion, respiratory motion, and cardiac motion. With BPT signal guidance, we successfully reconstructed motion-resolved images of a head scan under in-plane motion and abdominal imaging under free-breathing. SNR of images is higher than previous low-field images, and the motion artifacts are reduced, which shows the potential for wide application of BPT in low-field MRI.

IEEE Transactions on Instrumentation and MeasurementVol. 74
Shanghai Jiao Tong University (CN), University of California, Berkeley (US)
Shanghai Science and Technology Development Foundation, National Natural Science Foundation of China
Openalex Percentile: Top 25%
Advanced MRI Techniques and Applications
1.36
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