In image stacking, we combine multiple x-ray angiography images with incomplete arterial filling into a single output image with more completely filled arteries. Among other applications, image stacking is useful in neuroangiography embolization and in CO 2 angiography. Using Monte Carlo simulations and tests on clinical image sequences, we compare three methods: (1) traditional extreme-intensity (EI) which consists of a max-dark or max-light operation on the sequence, (2) matched filtering (MF) with spatially varying parameters, and (3) a new algorithm, trimmed-extreme-intensity (TEI). In the simulations, we use Poisson noise and model the time-course of the arterial contrast signal with a gamma variate curve. The figure of merit for comparisons is the contrast-to-noise (CNR) ratio. We find that our spatially-dependent MF method works well with image which have a well-defined direction of flow as in the legs, but not with more complex flow patterns as in neuroangiography. On clinical images, TEI gives good results and is more robust than MF.