Keywords: Cartilage, Cartilage, MRF
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.
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