The goal of this project is to develop a deep learning (using CycleGAN) algorithm that can 1) accurately simulate the image noise present in low-dose CT images and 2) remove low-dose image noise to simulate the quality of a high-dose image given an initial low-dose input. Once implemented, this will increase the safety and quality of low-dose brain CT scans by reducing the patient’s exposure to ionizing radiation.