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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