We propose a method to synthesize 3D consistent brain MRI utilizing multiple 2D conditional SPatially-Adaptive (DE)normalization (SPADE) GANs to preserve the spatial information of patient-specific brain anatomy and a 3D CNN based network to improve the image consistency in all directions in a cost-efficient manner. Three individual 2D SPADE-GAN networks are trained across the axial, sagittal and coronal slice directions and their outputs are thereafter used further to train a CNN based 3D image restoration network to combine them into one 3D volume, using real MRI as a reference. The resulting predicted-synthesized 3D brain MRI is evaluated quantitatively and qualitatively.
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