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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