Direct parameter estimation of white matter model from DKI maps using recurrent neural network
Yujian Diao1,2,3 and Ileana Ozana Jelescu2,4
1Laboratory of Functional and Metabolic Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, 2CIBM Center for Biomedical Imaging, Lausanne, Switzerland, 3Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, 4Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland
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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