Tissue perfusion measurement using C-arm angiography systems capable of CT-like imaging (C-arm CT) is a novel technique with potentially high benefit for catheter-guided treatment of stroke in the interventional suite. New rapid scanning protocols with increased C-arm rotation speed enable fast acquisitions of C-arm CT volumes and allow for sampling the contrast flow with improved temporal resolution. However, the peak contrast attenuation values of brain tissue lie typically in a range of 5-30 HU. Thus perfusion imaging is very sensitive to noise. In this work we compare different denoising algorithms based on the algebraic reconstruction technique (ART) and introduce a novel denoising technique, which requires only iterative filtering in volume space and is computationally much more attractive. Our evaluation using a realistic digital brain phantom shows that all methods improve the perfusion maps perceptibly compared to Feldkamp-type (FDK) reconstruction. The volume-based technique performs similarly to the ART-based methods: the Pearson correlation of reference and reconstructed blood flow maps increases from 0.61 for the FDK method to 0.81 for the best ART method and to 0.79 for the volume-based method. Furthermore results from a canine stroke model study are shown.