We are thrilled to receive a new 3-year Trailblazer R21 Grant entitled “Deep Learning Reconstruction for Rapid Multi-Component Relaxometry” from The National Institutes of Health and the National Institute of Biomedical Imaging and Bioengineering (NIBIB).
This exciting project will develop novel Artificial Intelligence and Machine Learning methods to improve MRI speed, accuracy, and efficiency in non-invasively imaging human diseases and quantifying human tissue microstructures and properties.
We want to thank the grant sponsors and give our gratitude to all scientific reviewers for their constructive and invaluable comments and suggestions that made this project much improved and finally funded.
This project will develop a novel deep learning reconstruction technique for multi-component relaxometry. The proposed reconstruction technique can offer a unique opportunity to explore the acceleration of multi-component relaxation mapping by leveraging the latest deep learning techniques, resulting in an accurate, efficient, and reliable model that is widely generalizable to different relaxation types in many body regions. Successful completion of the project will provide a clinically applicable multi-component relaxometry technique that can help better study, understand and stage diseases such as osteoarthritis and multiple sclerosis from the tissue microstructural and biochemical level.
NIH RePORT