Although fully convolutional neural networks (FCNNs) have been widely used for MR imaging, they have not been extended for improving free-breathing lung imaging yet. Our aim was to improve the image quality of retrospective respiratory gated version of a Zero Echo Time (ZTE) MRI sequence (4D-ZTE) in free-breathing using a FCNN so enabling free-breathing acquisition in those patients who cannot perform breath-hold imaging. Our model obtained a MSE of 0.08% on the validation set. When tested on unseen data (4D-ZTE) the predicted images from our model had improved visual image quality and artifacts were reduced in free-breathing 4D-ZTE.
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