Quantitative dynamic contrast-enhanced (DCE) MRI has the potential for early detection, accurate staging, and therapy monitoring of cancers. However, clinical abdominal DCE-MRI has limited temporal resolution and can only provide qualitative or semi-quantitative assessments of tissue vascularity. In this study, we investigated the feasibility of retrospective quantification of multi-phasic abdominal DCE-MRI by improving the temporal resolution via deep learning. Simulated multi-phasic DCE data was generated using 2-sec temporal-resolution Multitasking DCE images. Results show that DCE kinetic parameters retrospectively estimated by deep learning agree with the ground truth, and are capable of differentiating abnormal tissues.