Keywords: Joint, Bone
Motivation: The knee joint is prone to degenerative changes and injuries. Conventional knee MRI is important but often has low efficiency and reduced image quality. Deep learning-based super-resolution techniques show potential to address these issues, but clinical validation is needed to confirm practical benefits.
Goal(s): Investigate the clinical benefits of a deep learning-based composite super-resolution reconstruction approach for knee MRI.
Approach: 110 patients with each scanning conventional and composite super-resolution reconstruction knee MRI were included. Image quality was compared using objective and subjective assessments.
Results: The composite super-resolution reconstruction significantly improved image quality, efficiency, and diagnostic value compared to conventional knee MRI.
Impact: The practice of deep learning-based super-resolution reconstruction would be beneficial for knee MRI overall diagnostic quality and efficiency, further enhancing patient comfort and clinical workflow.
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