A systematic automated post-processing approach for quantitative analysis of 3D T1ρ knee MRI

Motivation: To address the global healthcare challenge of knee osteoarthritis. Goal(s): Develop and validate an automated post-processing method for quantitative 3D T1ρ knee imaging analysis. The proposed post-processing pipeline accelerates the process while preserving a user-friendly and clinical-related output. Approach: We proposed a post-processing pipeline that combines parcellation, ROIs selection, T1rho fitting, and regionally averaged outputs. We evaluated our approach on 30 OA patients and 10 healthy controls. Results: The proposed post-processing approach achieved satisfactory performance on automatic ROI selection compared to the manually labelled ROIs and provided quantitative T1ρ analysis with clinical promise. Impact: Our proposed pipeline enables automated post-processing for T1ρ imaging with deep learning, pushing this promising technique to the clinics to provide sensitive and quantitative knee OA diagnostics.