Deep Learning Solutions for CT Metal Artifact Reduction

The World Health Organization reported that cancer is the second leading cause of death globally and is responsible for 9.6 million deaths in 2018. Approximately 50% of all cancer patients receive radiation therapy (RT). Many of them have metal implants, which induce image artifacts in the treatment planning CT images and compromise or preclude treatment in an estimated 15% of all radiation therapy patients. Despite extensive CT metal artifact reduction (MAR) research it remains one of the long-standing challenges in the CT field, without a clinically satisfactory solution.

The overall goal of this project is to develop cutting-edge deep learning imaging methods and software solutions for commercial CT scanners to eliminate CT metal artifacts in general and improve RT in particular. We propose a three-pronged approach to systematically tackle this challenge in three specific aims: (1) adversarial learning techniques for estimation of sinogram missing data and metal traces; (2) constrained disentanglement (CODE) networks to remove CT image artifacts during image reconstruction, through post-processing, and in both data and image domains; and (3) systematic evaluation of our proposed CT MAR techniques and clinical translation into robust RT planning methods to maximize the RT treatment planning accuracy and thus improve patient outcomes. Our synergistic track records in CT MAR research, especially with deep imaging methods over the past three years, promises an unprecedented opportunity for a brand-new solution to CT MAR. For the first time we will integrate contemporary AI innovations in data preprocessing, image reconstruction, post-processing, observer studies and treatment planning synergistically in a unified data-driven framework, positioning this project uniquely to eliminate metal artifacts and their complications in radiation therapy.