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Abstract #4014

Deep Neural Network for Single-Point Dixon Imaging with Flexible Echo Time

Jong Bum Son1, Marion Elizabeth Scoggins2, Basak Erguvan Dogan3, Ken-Pin Hwang1, and Jingfei Ma1

1Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, 2Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, 3Department of Diagnostic Radiology, The University of Texas Southwestern Medical Center, Dallas, TX, United States

Dixon imaging generally requires multiple input images acquired at varying echo-times for robust phase-correction and water and fat separation. However, multi-echo Dixon imaging suffers from relatively long scan-time and is more susceptible to motion related artefacts and inflexible in choosing scan-parameters. Recently, it was reported that deep neural networks can help separate water and fat from two-point, or multi-point Dixon images. In this work, we present a deep learning based method that can achieve water and fat separation from a single image acquired at a flexible echo-time and therefore can help alleviate the limitations of multi-point Dixon imaging.

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