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Abstract #0673

Calibrationless Deblurring of Spiral RT-MRI of Speech Production Using Convolutional Neural Networks

Yongwan Lim1, Yannick Bliesener1, Shrikanth Narayanan1, and Krishna Nayak1

1University of Southern California, Los Angeles, CA, United States

Spiral acquisitions are preferred in speech real-time MRI because of their high efficiency, making it possible to capture vocal tract dynamics during natural speech production. A fundamental limitation is signal loss and/or blurring due to off-resonance, which degrades image quality most significantly at air-tissue boundaries. Here, we present a machine learning method that corrects for off-resonance artifact in spiral images of upper airway without acquiring a field map. Residual neural networks are trained on images simulated with known spiral trajectories and readout duration. The off-resonance blurring is effectively resolved at the articulator boundaries for long readout (~8ms) images at 1.5T.

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