Keywords: Analysis/Processing, Quantitative Imaging, Quantitative Mapping, T1ρ mapping, Nonlinear Least Squares, Bi-exponential models, Deep Learning
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.
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