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Abstract #3225

Deep learning improves retrospective free-breathing 4D-ZTE thoracic imaging: Initial experience

Dorottya Papp1, Jose M. Castillo T.1, Piotr A. Wielopolski1, Pierluigi Ciet1, Gyula Kotek1, Jifke F Veenland1, and Juan Antonio Hernandez-Tamames2
1Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, Netherlands, 2Erasmus Medical Center, Rotterdam, Netherlands

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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