Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network

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Apr 02, 2021
Jayalakshmi Mangalagiri, David Chapman, Aryya Gangopadhyay, Yaacov Yesha, Joshua Galita, Sumeet Menon, Yelena Yesha, Babak Saboury, Michael Morris, Phuong Nguyen
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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.

* It is a short paper accepted in CSCI 2020 conference and is accepted to publication in the IEEE CPS proceedings  
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