Machine Learning Assisted Prediction of Cartilage Proteoglycan Content Using MR Fingerprinting

Motivation: Early signs of cartilage degeneration include changes in proteoglycan content, which cannot be diagnosed using standard clinical imaging tools. Goal(s): Prediction of cartilage proteoglycan content from quantitative MR fingerprinting data at 3T. Approach: Gaussian process regression (GPR) models were trained to predict optical density of safranin-O stained cartilage sections, representing proteoglycan content, from MRF data on a voxel-by-voxel basis. Results: The trained GPR models reached very high accuracy (mean correlation of 0.81 with a respective NRMSE of 11.7%) and had clearly enhanced performance when compared to linear models. Impact: Non-invasive prediction of proteoglycan content in cartilage using MR fingerprinting at clinical field strength is feasible, holding promise for direct clinical imaging of cartilage composition in the future.