Abstract

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 1.5 T.

Date

2019

Authors

Yongwan Lim, Shrikanth Narayanan, Krishna S Nayak

Journal

Proc of ISMRM 27th Scientific Session, Montreal, Canada

Pages

673