Self-Supervised Deep-Learning Networks for Mono and Bi-exponential T1ρ Fitting in the Knee Joint

Motivation: The nonlinear least squares (NLS)-based estimation of mono- and bi-exponential T1ρ maps in the knee joint is highly time-consuming. Deep-learning (DL) methods are faster alternatives. Goal(s): However, DL requires substantial training data, which is usually obtained using NLS on acquired data. This paper introduces self-supervised DL models that leverage synthetic target data for training, eliminating the need for scanned or NLS data to be used as reference. Approach: We have tested five different DL models, each utilizing a distinct activation function, and compared them against NLS. Results: The proposed models are 25-200x faster than the NLS method, with errors close to NLS. Impact: This study compared five different self-supervised DL models for estimating mono- and bi-exponential T1ρ maps in the knee joint. These models are faster alternatives to NLS, potentially replacing it to produce reference maps.