Computed tomography (CT) is one of the most widely used radiography exams worldwide for different diagnostic applications. However, CT scans involve ioniz- ing radiational exposure, which raises health concerns. Counter-intuitively, low- ering the adequate CT dose level introduces noise and reduces the image quality, which may impact clinical diagnosis. This study analyzed the feasibility of using a conditional generative adversarial network (cGAN) called pix2pix to learn the mapping from low dose to high dose CT images under different conditions. This study included 270 three-dimensional (3D) CT scan images (85,050 slices) from 90 unique patients imaged virtually using virtual imaging trials platform for model development and testing. Performance was reported as peak signal-to-noise ra- tio (PSNR) and structural similarity index measure
