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Abstract #1140

Evaluating the Use of Deep-Learning Super-Resolution for Obtaining Osteoarthritis Biomarkers

Akshay S Chaudhari1, Jeff P Wood2, Kathryn J Stevens1, Zhongnan Fang3, Jin Hyung Lee4, Garry E Gold1, and Brian A Hargreaves1

1Radiology, Stanford University, Palo Alto, CA, United States, 2Austin Radiological Association, Austin, TX, United States, 3LVIS Corporation, Palo Alto, CA, United States, 4Neurology, Stanford University, Palo Alto, CA, United States

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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