  Spiral acquisitions are preferred in real-time MRI because of their
efficiency, which has made it possible to capture vocal tract dynamics during
natural speech. A fundamental limitation of spirals is blurring and signal loss
due to off-resonance, which degrades image quality at air-tissue boundaries.
Here, we present a new CNN-based off-resonance correction method that
incorporates an attention-gate mechanism. This leverages spatial and channel
relationships of filtered outputs and improves the expressiveness of the
networks. We demonstrate improved performance with the attention-gate, on 1.5
Tesla spiral speech RT-MRI, compared to existing off-resonance correction
methods.
