Project 3: Machine Learning for Heterogeneous Brain MRI: Bridging the Gap to Generalizable Models

Magnetic resonance (MR) imaging has become a key technology for brain imaging, resulting in massive databases, rapidly increasing the need for big data analytics, robust pooling, and harmonization, especially for data acquired across diverse cohorts. A barrier to the success of these techniques is the inherent variation between image acquisition protocols and different equipment, resulting in a lack of reproducible results. It has been shown that even when care is taken to standardize acquisitions, changes in hardware, software, or protocol design can lead to differences in quantitative results and loss of consistency. As a result, the quantitative utility of MR in multisite or long-term studies is dramatically impacted.

Machine learning (ML) has been extensively investigated for MR imaging analysis with multiple goals, such as quantitative analysis of structures or abnormalities and progress evaluation over time. Yet only a limited number of applications are now in use outside the research environment. A key reason for that is the poor generalizability of the models to data from different sources or acquisition domains. Developing new methods to handle this diverse MR imaging data is crucial for achieving accurate models and broadening their usage. Our long-term research program goal is to tackle the current limitations of the broader use of ML for medical imaging, focusing on the challenges of conducting large and multisite studies, using data harmonization and domain adaptation strategies of ML models that allow generalization from one dataset to another, avoiding domain-specific decision-making.

I anticipate a significant improvement in the generalization capability of ML tools developed for brain MR imaging applications. My findings will significantly impact the research area by allowing the usage of such models in larger, heterogeneous datasets and best practices when translating learning from one application to another using the proposed data harmonization and domain adaptation strategies. While my short-term goal is to work with MR imaging, the proposed strategies would be substantial for translation to other applications for other medical images and other computer vision applications.