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Abstract #3574

3D MRI Processing Using a Video Domain Transfer Deep Learning Neural Network

Jong Bum Son1, Mark D. Pagel2, and Jingfei Ma1
1Imaging Physics Department, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, 2Cancer Systems Imaging Department, The University of Texas MD Anderson Cancer Center, Houston, TX, United States

3D deep-learning neural networks can help ensure the slice-to-slice consistency. However, the performance of 3D networks may be degraded due to limited hardware. In this work, we developed a video domain transfer framework for 3D MRI processing to combine benefits of 2D and 3D networks with less graphical processing unit memory demands and slice-by-slice coherent outputs. Our approach consists of first translating “3D MRI images” to “a time-sequence of 2D multi-frame motion pictures,” then applying the video domain transfer to create temporally coherent multi-frame video outputs, and finally translating the output back to compose “spatially consistent volumetric MRI images.”

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