Computed tomography (CT) and magnetic resonance imaging (MRI) provide cranial and cortical information that allows clinicians to diagnose and treat infant skull deformities. However, radiation from CT can cause cancer. MRI using a strong magnetic field, which is not harmful. However, it requires the patient to remain very still, so in the case of young children, they are put under sedation for the protocol, which can be dangerous. Therefore, a generative adversarial network (GAN) that converts MRI to CT, and vice versa, was developed. Early testing of the GAN produced promising synthetic images and had a mean absolute error of 16.33, which outperforms existing literature.