Cardiac flow is still not fully understood, and is currently an active research topic. Using phase-contrast magnetic resonance imaging (PC-MRI) blood flow can be measured. For the inspection of such flow, researchers often rely on methods that require additional scans produced by different imaging modalities to provide context. This requires labor-intensive registration and often manual segmentation before any exploration of the data is performed. This work provides a framework that allows for a quick exploration of cardiac flow without the need of additional imaging and time-consuming segmentation. To achieve this, only the 4D data from one PC-MRI scan is used. A context visualization is derived automatically from the data, and provides context for the flow. Instead of relying on segmentation to deliver an accurate context, the heart’s ventricles are approximated by half-ellipsoids that can be placed with minimal user interaction. Furthermore, seeding positions for flow visualization can be placed automatically in areas of interest defined by the user and based on derived flow features. The framework enables a user to do a fast initial exploration of cardiac flow, as is demonstrated by a use case and a user study involving cardiac blood flow researchers.