Generative AI in Medical Imaging and Its Application in Low Dose Computed Tomography (CT) Image Denoising

Deep learning techniques have made its way to the medical field. Medical images are essential tools for visualizing internal body structures up to cellular levels. X-ray computed tomography (CT) is a widely used non-invasive medical modality for patient diagnosis. Harmful effects of cumulative amounts of radiation exposure to patients undergoing CT scan have been recorded which includes hair loss, cancer and other illnesses. The “As Low as Reasonably Achievable” (ALARA) principle was developed with the purpose of minimizing the radiation dose to patients. This chapter discusses the implementation of artificial intelligence to devices for the reconstruction of CT images affected by the reduction of the radiation. The corrupted CT images have noticeable noise and artifacts that causes inaccuracies of medical diagnosis. One of the robust deep learning models for LDCT restoration is the Generative Adversarial Networks (GAN). This study shows a simple GAN architecture that aims to minimize edge over-smoothing, image texture enhancement and preservation of structural details of the medical images. Further, a benchmark testing was done to show the performance of the network compared with other state of the art models (SOTA). In addition, ablation experiments for the modules used in the network and loss functions used for the training procedure are also presented.