  We present a novel conditional Generative Adversarial Network (cGAN)
architecture that is capable of generating 3D Computed Tomography scans in
voxels from noisy and/or pixelated approximations and with the potential to
generate full synthetic 3D scan volumes. We believe conditional cGAN to be a
tractable approach to generate 3D CT volumes, even though the problem of
generating full resolution deep fakes is presently impractical due to GPU
memory limitations. We present results for autoencoder, denoising, and
depixelating tasks which are trained and tested on two novel COVID19 CT
datasets. Our evaluation metrics, Peak Signal to Noise ratio (PSNR) range from
12.53 - 46.46 dB, and the Structural Similarity index ( SSIM) range from 0.89
to 1.
