Abstract #0964
Time-segmented and contrast-neutral motion correction using an end-to-end deep learning approach
Refaat E Gabr1, Masoud Edalati2, Lingzhi E Hu2, Adam Chandler2, Weiguo Zhang2, and Ponnada A Narayana3
1Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston, Houston, TX, United States, 2United Imaging Healthcare, Houston, TX, United States, 3University of Texas Health Science Center at Houston, Houston, TX, United States
Synopsis
We developed an end-to-end deep learning-based motion correction technique that utilized the time-segmented nature of MRI data acquisition. Results of computer simulations and in vivo studies show the network to be highly effective in correcting motion artifacts.
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