We are thrilled to receive another 5-year R01 grant from The National Institutes of Health and National Institute of Arthritis and Musculoskeletal and Skin Disease (NIAMS).
This new funding will support our lab to develop cutting-edge Artificial Intelligence and Machine Learning methods to improve MRI and image-based non-invasive human tissue quantification, particularly through ultra-high-resolution multi-dimensional quantitative imaging. We aim to explore, implement and optimize a fundamentally new approach toward enabling highly efficient imaging acquisition, reconstruction, analysis, and interpretation.
Again, we want to thank the grant sponsors and all scientific reviewers for the constructive and invaluable comments and suggestions, without which this grant could not have been funded.
This proposal will develop a rapid multi-relaxation imaging technique that can provide simultaneous three- dimensional T1, T2, and T1ρ relaxation mapping for quantifying tissue composition and ultra-structure using magnetic resonance imaging. The imaging technique incorporates a novel multi-relaxation mapping sequence using 3D golden-angle rapid acquisition and a novel physics-informed deep learning reconstruction and will be evaluated in a knee disease model. Our novel proposal would provide a new rapid imaging approach to non- invasively monitor disease-related and treatment-related changes in tissue composition and ultra-structure through multi-relaxation assessment and will have broad clinical applications for various diseases.
NIH RePORT