Estimating the capillary input function using deep learning approach for Dynamic Contrast-Enhanced MRI assessment of blood brain barrier

This study proposes a deep learning approach of estimating the capillary level of input function for kinetic model analysis on dynamic contrast enhanced (DCE)-MRI data. Our deep-learning network was trained with the numerically synthesized data generated with a wide range of contrast kinetic dynamics with different arterial input function (AIF). We hypothesize that the voxel level capillary input functions would be more accurate input functions for pharmacokinetic analysis. This hypothesis was tested with the DCE-MRI data of healthy subjects.