Value Proposition: The BRITE framework operates on tagged MRI data to significantly improve the accuracy and efficiency of evaluating motion and strain in tissues. This advancement enables clinicians to make more informed decisions regarding patient care, particularly in diagnosing cardiovascular conditions and analyzing brain mechanics, ultimately leading to better health outcomes and more effective treatment strategies.
Technology Description: BRITE employs advanced algorithms to analyze tagged MRI sequences, allowing for simultaneous measurement of strain and motion without needing additional scan acquisitions. The methodology integrates innovations in machine learning to address challenges such as tag fading and tissue motion, providing a comprehensive approach for obtaining robust diagnostic information that is not only clinically accurate but also accessible and cost-effective in practice.
Unmet Need: BRITE provides accurate and reliable imaging solutions for diagnosing cardiovascular diseases, which affect nearly 121.5 million American adults and account for over 800,000 deaths annually, as well as mild traumatic brain injuries, causing approximately 1.4 million emergency room visits in the U.S. each year. Existing methods often lack the precision necessary for effective treatment and monitoring in these areas, highlighting the significance of BRITE's capabilities in potentially improving patient outcomes on a large scale.
Stage of Development: BRITE is currently at the demonstration stage, with successful proof-of-concept validation on tagged MRI data from gel phantoms already completed. The next steps involve validating the technology on human datasets, optimizing integration into clinical workflows, developing user-friendly interfaces, and pursuing necessary regulatory approvals.
Publication(s): Bian, Z. et al. (2026). Brightness-Invariant Tracking Estimation in Tagged MRI. In: Oguz, I., Zhang, S., Metaxas, D.N. (eds) Information Processing in Medical Imaging. IPMI 2025. Lecture Notes in Computer Science, vol 15830. Springer, Cham. https://doi.org/10.1007/978-3-031-96625-5_25
