A Generative Adversarial Network Approach for Noise and Artifacts Reduction in MRI Head and Neck Imaging

Abstract

As the volume of data available to healthcare and life sciences specialists proliferates, so do the opportunities for life-saving breakthroughs. But time is a key factor. High-Performance Computing (HPC) can help practitioners accurately analyze data and improve patient outcomes, from drug discovery to finding the best-tailored therapy options. In this paper, we present and discuss an Artificial Intelligent methodology based on a Generative Adversarial Network to improve the perceived visual quality of MRI images related to the head and neck region. The experimental results demonstrate that once trained and validated, our model performs better with respect to the state of art methods and testing it on unseen real corrupted data improved the quality of the images in most cases.