Keywords: Machine Learning/Artificial Intelligence, Diffusion/other diffusion imaging techniques, Uncertainty estimationWe transformed the state-of-the art IVIM-NET for IVIM parameter fitting into a Bayesian Neural Network (BNN). BNNs can estimate uncertainty for quantitative MRI parameters, which is relevant for clinical decision making. We found that training on data with the highest SNR outperformed IVIM-BNNET models trained on matching SNR regarding parameter errors and uncertainties. A region with artificially increased noise could be identified from IVIM-BNNET's uncertainty output. Compared with traditional fitting, IVIM-BNNET achieved comparable accuracy, while being 21 times faster and providing less correlated parameter estimates. Monte-Carlo dropout rate 0.4 provided the best trade-off between low errors and low uncertainty.
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