Keywords: Liver, DSC & DCE PerfusionRespiratory motion can impair the accurate estimation of physiological parameters in DCE-MR of the liver. A Deep learning network is proposed to quantitatively investigate the impact of respiratory motion on the estimation of physiological parameter maps. The proposed network provides quantitative parameters for DCE-MR and uncertainty estimates for these parameters. Here we could show that the estimated epistemic uncertainty of k_trans is sensitive to motion. This could provide important information about how well motion correction worked and how reliable the obtained quantitative DCE parameters are.
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