Rapid Three-dimensional Simultaneous Knee Multi-Relaxation Mapping

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PROJECT SUMMARY Osteoarthritis (OA) is one of the most prevalent diseases affecting human joints, characterized by decreased proteoglycan content and disruption of the collagen fiber network in the cartilage extracellular matrix. Magnetic resonance (MR) imaging has been used to quantify cartilage composition and microstructure changes due to degeneration in OA. Among all MR techniques, MR relaxometry is the most popular and can provide non-invasive, high-resolution, three-dimensional imaging biomarkers, which would be highly valuable in quantifying human tissues. Cartilage spin-spin (T2) relaxation time has been found to be sensitive to the changes of collagen ultrastructure associated with early cartilage degeneration. Cartilage spin-lattice relaxation in the rotating frame (T1ρ) is sensitive to the concentration changes of macromolecules and is correlated with proteoglycan loss in OA. The role of spin-lattice relaxation (T1) time has also been reported to correlate with the mechanical property changes of cartilage and is sensitive to progressive damage of the tissue. While each relaxation parameter provides limited and complementary information of cartilage, the capability of imaging T1, T2 and T1ρ together would provide a set of comprehensive imaging biomarkers for synergistically accessing the macromolecular content and their ultrastructure of cartilage. However, due to long scan time, poor image acquisition efficiency, and complex image reconstruction and tissue modeling, simultaneous multi-relaxation mapping is very challenging thus remains underdeveloped in OA research studies. This proposal will provide rapid three- dimensional simultaneous multi-relaxation imaging for mapping T1, T2, and T1ρ of the knee through developing a novel imaging sequence and reconstruction method (Aim 1). This new technique will leverage efficient three- dimensional golden-angle image acquisition and will be accelerated through a novel deep learning method that leverages self-supervised learning and MR physics-informed tissue modeling. The derived MR imaging biomarkers will be correlated with cartilage histological, biochemical, and mechanical properties, which will create a basis for interpretation of the clinical study results (Aim 2). A pilot clinical study using the optimized and accelerated imaging technique will be performed on patients with varying degrees of knee OA, establishing the clinical evidence of the utility, efficiency, and overall clinical value of multi-relaxation mapping on detecting and staging OA (Aim 3). Our proposed new methods will root from developing novel rapid image acquisition, combined with advanced deep learning reconstruction and automatic processing, all of which are pioneered by our team. Successful completion of the proposal will offer a new rapid imaging technique to non-invasively monitor disease-related and treatment-related changes in tissue composition and ultra-structure through multi- relaxation assessment. It will have broad clinical applications for OA and other diseases.