Medical Image Synthesis using Computer Vision Techniques

Abstract

Magnetic Resonance Imaging (MRI) is a type of scan that produces comprehensive images of the inside of the body using a steady magnetic field and radio waves. On the other hand, Computed Tomography (CT) scans, is a combination of a series of X-ray images, which are a type of radiation called ionizing radiation. It can be harmful to the DNA in your cells and also increase the chances that they'll turn cancerous. MRI is a safer option compared to CT and does not involve any radiation exposure. In this paper, we propose the use of Generative Adversarial Networks (GANs) to translate MRI images into equivalent CT images. We compare it with past techniques of MRI to CT scan conversion and elaborate on why GANs produce more realistic CT images while modeling the nonlinear relationship from MRI to CT.