Abstract #1267

Deep Learning-Based Automatic Estimation of Volume and Fat Fraction in Abductor Muscles and their Associations with T1ρ and T2 in Hip Osteoarthritis Patients

Radhika Tibrewala¹, Valentina Pedoia¹, Carla Kinnunen¹, Tijana Popovic¹, Richard Souza^1,2, and Sharmila Majumdar¹

¹Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States, ²Physical Therapy and Rehabilitation Science, University of California San Francisco, San Francisco, CA, United States

In Osteoarthritis, cartilage degeneration can be accompanied by muscle weakness. T_1ρ and T₂ relaxation times have been used to probe cartilage degeneration. This study aims to develop an automatic machine-learning based segmentation and quantification pipeline to estimate the volumes and fat fractions of the three hip abductor muscles and study their associations with T_1ρ and T₂ relaxation times. Our results showed fast, reliable segmentations the hip abductor muscles and voxel based correlations between T_1ρ and fat fraction and T₂ and volumes of the muscles.

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