Spatial leakage effects are particularly confounding for seed-based investigations of brain networks using source-level electroencephalography (EEG) or magnetoencephalography (MEG). Various methods designed to avoid this issue have been introduced but are limited to particular assumptions about its temporal characteristics. Here, we investigate the usefulness of a model-based geometric correction scheme (GCS) to suppress spatial leakage emanating from the seed location. We analyze its properties theoretically and then assess potential advantages and limitations with simulated and experimental MEG data (resting state and auditory-motor task). To do so, we apply Minimum Norm Estimation (MNE) for source reconstruction and use variation of error parameters, statistical gauging of spatial leakage correction and comparison with signal orthogonalization. Results show that the GCS has a local (i.e., near the seed) effect only, in line with the geometry of MNE spatial leakage, and is able to map spatially all types of brain interactions, including linear correlations eliminated after signal orthogonalization. Furthermore, it is robust against the introduction of forward model errors. On the other hand, the GCS can be affected by local overcorrection effects and seed mislocation. These issues arise with signal orthogonalization too, although significantly less extensively, so the two approaches complement each other. The GCS thus appears to be a valuable addition to the spatial leakage correction toolkits for seed-based FC analyses in source-projected MEG/EEG data.