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Abstract #1386

Accelerated volumetric vocal tract MRI using model based deep learning

Wahidul Alam1 and Sajan Goud Lingala1,2
1Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa city, IA, United States, 2Department of Radiology, University of Iowa, Iowa city, IA, United States


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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