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.
MR to CT image synthesis plays an important role in medical image analysis, and its applications included, but not limited to PET-MR attenuation correction and MR only radiation therapy planning. Recently, deep learning-based image synthesis techniques have achieved much success. However, most of the current methods require large scales of paired data from two different modalities, which greatly limits their usage as in some situation paired data is infeasible to obtain. Some efforts have been proposed to relax this constraint such as cycle-consistent adversarial networks (Cycle-GAN). However, the cycle consistency loss is an indirect structural similarity constraint of input and synthesized images, and it can lead to inferior synthesized results. To overcome this challenge, a novel correlation coefficient loss is proposed to directly enforce the structural similarity between MR and synthesized CT image, which can not only improve the representation capability of the network but also guarantee the structure consistency between MR and synthesized CT images. In addition, to overcome the problem of big variance in whole-body mapping, we use the multi-view adversarial learning scheme to combine the complementary information along different directions to provide more robust synthesized results. Experimental results demonstrate that our method can achieve better MR to CT synthesis results both qualitatively and quantitatively with unpaired MR and CT images compared with state-of-the-art methods.
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