Ultra-Fast High-Resolution Multi-Parametric MRI for Characterizing Cartilage Extracellular Matrix

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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. Quantitative magnetic resonance (MR) imaging has been used to quantify cartilage composition and microstructure changes due to extracellular matrix degeneration in OA research studies. While many quantitative MR techniques have been explored, existing methods face serious limitations, including lack of specificity to assess individual macromolecular components, sensitive to magic angle effect, susceptible to partial volume effect due to thick image slice. More importantly, quantitative MR techniques typically require a much longer scan time than standard imaging due to the need for repeated scans of the same imaging object at varying imaging parameters. Spatial resolution and imaging volume coverage must be compromised to make a clinically feasible scan in OA research studies. This proposal aims to develop a new imaging technique that can provide robust, sensitive, and specific imaging biomarkers for simultaneously assessing cartilage proteoglycan and collagen components, and meanwhile can be acquired at the submillimeter spatial resolution, thin image slice, and full knee coverage within a 10-min scan time. Among all the quantitative MR techniques, multi-component T2 relaxation imaging has been found to provide sensitive and specific information for cartilage proteoglycan content; cross-relaxation imaging has been found to provide complementary information regarding the collagen fiber network of cartilage. The proposal will develop a simultaneous multi-component T2 relaxation and cross-relaxation imaging technique that can provide sensitive and specific imaging biomarkers to assess proteoglycan and collagen content and their ultra-structures in a unified imaging framework (Aim 1). This imaging protocol will be optimized using rigorous statistical methods and 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 tissue 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 this newly proposed technique on detecting OA incidence and predicting OA progression (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 research team. Successful completion of the proposal will provide the OA research community with a new set of MR biomarkers to non-invasively monitor disease-related and treatment-related changes in cartilage composition and ultra-structure in human subjects.