3D MRI is a powerful tool to safely visualize the various vocal-tract configurations during voice production [1-3]. Current accelerated 3D vocal-tract MRI schemes based on spatial total variation transform (SPTV) regularization are susceptible to non-trivial artifacts (e.g., blurring at air-tissue boundaries, patchy representation of small structures such as the epiglottis, glottis). In this work, we apply a model based deep learning reconstruction scheme that can significantly accelerate 3D vocal-tract imaging. We demonstrate it to produce images with high-spatial fidelity, natural-looking like contrast, and significantly robust to artifacts seen with current SPTV based schemes.
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