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PROJECT SUMMARY Human knee joints are composed of many different tissues including articular cartilage, calcified cartilage, menisci, ligaments, tendons and bone all of which are important for the health of the joint. Recent research suggests that osteoarthritis (OA) is not just a disease of hyaline cartilage, but rather of the entire joint. Several researchers have demonstrated that the T1ρ relaxation time is more sensitive to proteoglycan (PG) content of the cartilage, while T2 relaxation time is more sensitive to collagen orientation, integrity of network and hydration. The short T1ρ menisci, calcified cartilage, tendons, and ligaments are integral part of joint biomechanics and abnormalities in these tissues may impact joint degeneration and onset of OA. Therefore, quantitative evaluation of relaxation times in these tissues may provide sensitive biochemical markers for early OA diagnosis, and disease progression. However, conventional T1ρ-MRI sequences (e.g., GRE and FSE readouts) are limited value in detecting early macromolecular changes in semi-solid short T1ρ tissues or tissue components such as menisci, ligaments and tendons. Most of the knee joint structures, including menisci, ligaments and tendons have both dominant short (bound/restricted water associated with collagen and/or proteoglycans) and minor long (less restricted/free water associated with macromolecules) components. Therefore, there is a significant need for reliable, non-invasive, time efficient, ZTE-based biexponential 3D-T1ρ imaging sequence for quantitative assessment of semi-solid structures in the knee joint that could detect the early biochemical changes in extracellular matrix (ECM) with short and long relaxation components and their corresponding fractions. The overarching goal of this R21 proposal is to develop, optimize, and translate ZTE (PETRA)-based biexponential 3D-T1ρ imaging pulse sequence for improved quantitative assessment of morphological and biochemical characterization of semi-solid short T1ρ structures in the knee joint (menisci, ligaments and tendons) on a standard clinical 3T scanner employing multicoil compressed sensing (CS).
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Multiparametric Mapping of Knee Joint with Magnetic Resonance Fingerprinting
Multiparametric Mapping of Knee Joint with Magnetic Resonance Fingerprinting
Data-Driven Learning Framework for Fast Quantitative Knee Joint Mapping
Data-Driven Learning Framework for Fast Quantitative Knee Joint Mapping
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