Abstract This paper presents a framework of non-interactive algorithms for the mapping of blood flow information to vessels in 3D-RA images. With the presented method, mapping of flow information to 3D-RA images is done automatically without user interaction. So far, radiologists had to perform this task by extensive image comparisons and did not obtain visualizations of the results. In our approach, flow information is reconstructed by forward projection of vessel pieces in a 3D-RA image to a two-dimensional projection series capturing the propagation of a short additional contrast agent bolus. For accurate 2D–3D image registration, an efficient patient motion compensation technique is introduced. As an exemplary flow-related quantity, bolus arrival times are reconstructed for the vessel pieces by matching of intensity–time curves. A plausibility check framework was developed which handles projection ambiguities and corrects for noisy flow reconstruction results. It is based on a linear programming approach to model the feeding structure of the vessel. The flow reconstruction method was applied to 12 cases of cerebral stenoses, AVMs and aneurysms, and it proved to be feasible in the clinical environment. The propagation of the injected contrast agent was reconstructed and visualized in three-dimensional images. The flow reconstruction method was able to visualize different types of useful information. In cases of stenosis of the middle cerebral artery (MCA), flow reconstruction can reveal impeded blood flow depending on the severeness of the stenosis. With cases of AVMs, flow reconstruction can clarify the feeding structure. The presented methods handle the problems imposed by clinical demands such as non-interactive algorithms, patient motion compensation, short reconstruction times, and technical requirements such as correction of noisy bolus arrival times and handling of overlapping vessel pieces. Problems occurred mainly in the reconstruction and segmentation of 3D-RA images in cases of complex AVMs. The concentration of injected contrast agent was often not sufficient to provide highly contrasted vessels in 3D-RA images. Another segmentation-related problem is known as ‘kissing vessels’ . Kissing vessel artifacts introduce artificial vessel junctions and thereby distort the feeding structure of the vessel. This may finally cause implausible flow reconstruction results and inverse flow directions in vessel segments. We are currently planning to validate our reconstruction results using particle imaging velocimetry (PIV). PIV experiments with phantoms, for which the true flow parameters are known, will allow for the assessment of the accuracy of our contrast agent based method. In the context of computational fluid dynamics techniques, the potential of the presented flow reconstruction method is high. Flow reconstruction results based on the presented method could be used both as boundary conditions for simulations and as a reference for the validation of simulation results. Computational fluid dynamics provide useful information such as arterial wall shear stress and complex flow patterns in aneurysms.