For successful abdominal radiotherapy it is crucial to have a clear tumor definition and an accurate characterization of the motion. While dynamic contrast-enhanced (DCE) MRI aids tumor visualization, it is often hampered by motion artifacts. 4D-MRI characterizes this motion, but often lacks the contrast to clearly visualize the tumor. This dual requirement is challenging due to time constraints. Here, we propose combining both into a single acquisition by reconstructing the data in various ways in order to achieve both high spatio-temporal resolution DCE-MRI and accurate 4D-MRI motion estimates. A 5 min T1-weigthed DCE acquisition was collected in five renal-cell carcinoma patients and simulated in a digital phantom. Data were acquired continuously using a 3D golden angle radial stack-of-stars acquisition. This enabled three types of reconstruction; (1) a high spatio-temporal resolution DCE time series, (2) a 5D reconstruction and (3) a contrast-enhanced 4D-MRI for motion characterization. Motion extracted from the 4D- and 5D-MRI was compared with a separately acquired 4D-MRI and additional 2D cine MR imaging. Simulations on XCAT showed that 5D reconstructions severely underestimated motion ([Formula: see text]), whereas contrast-enhanced 4D-MRI only underestimated motion by [Formula: see text]. This was confirmed in the in vivo data where motion of the contrast-enhanced 4D-MRI was approximately [Formula: see text] smaller than the motion in the 2D cine MRI (5.8 mm versus 6.5 mm), but equal to a separately acquired 4D-MRI (5.8 mm versus 5.9 mm). 5D reconstructions underestimated the motion by more than [Formula: see text], but minimized respiratory-induced blurring in the contrast enhanced images. DCE time-series demonstrated clear tumor visualization and contrast enhancement throughout the entire field-of-view. DCE- and 4D-MRI can be integrated into a single acquisition that enables different reconstructions with complementary information for abdominal radiotherapy planning and, in an MRI-guided treatment, precise motion information, input for motion models, and rapid feedback on the contrast enhancement.