Performance of recurrent neural networks with Monte Carlo dropout for predicting pharmacokinetic parameters from dynamic contrast‐enhanced magnetic resonance imaging data

MCD is effective in quantifying uncertainty in PK parameter prediction from DCE-MRI data and improves their performance, particularly at low PSNR; however, at the expense of increased computational cost. This study helps deepen our understanding of RNNs with MCD and select suitable hyperparameters for creating an RNN architecture for DCE-MRI studies.