Machine Learning for Heterogeneous Brain Magnetic Resonance Imaging: Bridging the Gap to Generalizable Models

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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 multi-site 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. In the early 2010s, MR scanners with a magnetic field strength of 1.5 T were largely used. These machines have been replaced with 3 T MR scanners in the last years, not only for research but also for routine exams. However, tools developed for studies using 1.5 T images show poor generalization capability, performing poorly in 3 T MR images. Developing new methods to handle this diverse MR imaging data is crucial for achieving accurate models and broadening their usage. My 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 multi-site studies. In my first NSERC Discovery Grant, I will develop data harmonization and domain adaptation strategies of ML models that allow generalization from one dataset to another, avoiding domain-specific decision-making, using specific classification and segmentation tasks on healthy control participants and patients on brain MR imaging as proof of concepts. ML models with consistent results across different imaging types, different groups of subjects (varying age ranges, sex, and pathology), and acquisition parameters are more reliable and allow broader usage. 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.