Head magnetic resonance imaging (MRI) is susceptible to motion artifacts. Images with gross motion artifacts cannot be easily corrected, which makes the monitoring of patient movement is essential to improve the acquired image quality. Several methods have been proposed to detect patient movement during MRI scanning, such as navigator, optical tracking system and the image quality assessment. However, they have some disadvantages such as insufficient real-time performance and requiring additional MR data acquisition. Herein, we propose an MR-compatible millimeter wave (mmWave) radar system to monitor some typical types of movements during head MRI scanning. The radar sensor is installed in a 3T MR system. When MRI system scanning, the radar installed will vibrate. A novel algorithm is proposed to generate the spectrogram without vibration interference. Due to limited movement, we explore how the range information rather than micro-Doppler information can assist the activity classification. After data processing, convolutional neural network (CNN) is applied to extract the features in multiple range bins. The extracted features will be inputted to a shallow neural network for recognizing the moving parts. The result shows four different activities are classified with an overall accuracy of (96.81±0.69)%. Finally, experimental results with acquired head MR images prove our system’s sufficient capability of recognizing nodding head yet inadequate capability of detecting head shaking movement.
Abdominal MRI is susceptible to respiratory motion artifacts. The existing clinical solution is using breathing belt to track the movement of the abdomen and trigger MRI acquisition during the end-expiration phase. Attaching respiratory belt to patients often slows down clinical workflow and affects patient comfort especially for those with surgical wounds and respiratory disorders. Herein we, for the first, propose a novel MRI compatible frequency modulated continuous wave (FMCW) radar to track respiratory motion within MRI bore in a non-contact fashion. The electromagnetic wave from FMCW radar can penetrate clothing and MRI RF coils to achieve continuous monitoring of patient’s vital signs. The system consists of a front-end FMCW radar sensor and a FPGA based power management/communication board that interface with a clinical MRI scanner. This design fully integrates the FMCW radar signal with MRI control console to enable real time respiratory triggered MRI acquisition. Consistent respiratory waveform was validated by comparing FMCW signal with traditional breathing belt measurement. Superior image quality from clinical MRI pulse sequence was achieved using the developed system in healthy volunteers.
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