In this work we present an artificial neural network (ANN) approach for the evaluation of the combined IVIM-Kurtosis model and robust mapping of the diffusion parameters in the human brain. Measuring seven healthy subjects the parameter map quality could be improved compared to an ordinary least squares regression by significantly reducing outliers and decreasing the variance while preserving the tissue contrast. An ROI-based analysis additionally showed a better agreement of the mean parameter values with the literature along with a better distinction between white and grey matter for the ANN approach.
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