WMTI-Watson is a widely used biophysical model that estimates microstructure parameters from the diffusion and kurtosis tensors. Here we propose a deep learning (DL) approach based on the recurrent neural network (RNN) to increase the robustness and accelerate the parameter estimation. The RNN solver achieved high accuracy, had good generality and was extremely fast in computation. The proposed DL approach is highly promising to replace the conventional nonlinear least-squares optimization in parameter estimation of WMTI-Watson model and thus estimate WM parameters from any DKI maps.
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