Contrast-Enhanced CT (CECT) imaging is used in the diagnosis of renal cancer and planning of surgery. Often, some CECT phase images are either completely missing or are corrupted with external noise making them useless. We propose a probabilistic deep generative model for imputing missing phase images in a sequence of CECT image. Our proposed model recovers the missing phase images with quantified uncertainty estimates enabling medical decision-makers make better-informed decisions. Furthermore, we propose a novel style-based adversarial loss to learn very fine-scale features unique to CECT imaging resulting in better recovery. We demonstrate the efficacy of this algorithm using a patient dataset collected in an IRB-approved retrospective study.