The use high-resolution magnetic resolution imaging (MRI) is beneficial for acquiring quantitative biomarkers corresponding to osteoarthritis (OA) severity and progression. However, the long scan times of high-resolution sequences, such as double-echo steady-state (DESS) that was included in the Osteoarthritis Initiative, precludes their widespread adoption. Deep-learning-based super-resolution has the potential to transform low-resolution MRI that can be acquired faster, into high-resolution images. Using qualitative cartilage image quality, and quantitative cartilage morphometry and osteophyte detection, we have shown that deep-learning-based super-resolution can enhance DESS slice-resolution threefold and offer the same utility as the original high-resolution acquisition for obtaining OA biomarkers.
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