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Attention-gated convolutional neural networks for off-resonance correction of spiral real-time MRI

2021-02-14 23:43:50
Yongwan Lim, Shrikanth S. Narayanan, Krishna S. Nayak
arXiv_CV
         
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2102.07271

PDF

https://arxiv.org/pdf/2102.07271.pdf

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