Fitting the IVIM bi-exponential model is challenging especially at low SNRs and time consuming. In this work we propose a supervised artificial neural network approach to obtain reliable parameters estimation as demonstrated in both simulated data and real acquisition. The proposed approach is promising and can outperform, in specific conditions, other state-of-the-art fitting methods.
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