NSF Award
1723420
Knee osteoarthritis (OA) affects 10% of older adults and is a major cause of work absence, early retirement and joint replacement. Knee OA is a disease characterized by deterioration of the cartilage in the knee. Using current technology, it is hard to predict how fast or how much deterioration will take place because cartilage loss is a slow and gradual process and can only be detected through medical images. This project will explore a novel 3D image model that can predict the accurate change of knee cartilage, to facilitate early detection and personal treatment for OA. If successful, the project could benefit 35 million people in the United States by reducing the high economic cost related to OA treatment, and improving the quality of life for these people. The PIs plan to disseminate the research to local medical communities and design a new course to involve undergraduate students into the research. The novel 3D image predictive model should have a wide variety of imaging applications.<br/> <br/>The goal of this project is to explore a novel 3D-information-fusion mechanism for medical imaging and a novel 3D image-to-image predictive model using deep neural networks as the core. The project will integrate cartilage information from MRI sequences. To handle size differences and perform image registration, a universal coordinate system will be defined to form a continuous and complete representation of the cartilage plane. Using the coordinate system, deep neural networks will be trained to learn the underlying correlation between the 3D cartilage maps. Unlike the traditional image-to-single-value prediction, the model will make image-to-image prediction; that is, from a current 3D cartilage map to a future 3D cartilage map, for different lengths of time (2, 4, 6, and 8 years respectively), leveraging a large imaging database. Finally, the team will construct the future 3D knee models from the cartilage maps to display the trajectory of cartilage change in a 3D view.
