3D T1ρ mapping is a promising technique for quantitative assessment of biochemical changes in knee cartilage. However, synovial fluid, if not suppressed, may compromise T1ρ quantification, particularly in clinical conditions like osteoarthritis where cartilage is usually irregular and synovial fluid is increased. A long-T2-selective inversion approach can be used to suppress the synovial fluid signal at the cost of increased scan time by 50%. This study demonstrated that deep learning can be used to effectively eliminate synovial fluid from T1ρ data acquired without active fluid suppression, potentially leading to improved T1ρ quantification of knee cartilage accuracy without adding scan time.
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