Automatic Registration of Serial Cerebral Angiography: A Comparative Review

Image registration can play a major role in medical imaging as it can be used to identify changes that have occurred over a period of time, thus mirroring treatment effectiveness, recovery, and detection of diseases onset. While medical image registration algorithms have been largely evaluated on MRI and CT, less attention has been given to Digital Subtraction Angiography (DSA). DSA of the brain is the method of choice for the diagnosis of numerous neurovascular conditions and is used during neurovascular surgeries. Numerous studies have relied on semi-automated registration that involve manual selection of matching features to compute the mapping between images. Nevertheless, there are currently a variety of automatic registration methods which have been developed, although the performance of these methods on DSA have not been fully explored. In this paper, we identify and review a variety of automatic registration methods, and evaluate algorithm performance in the context of serial image registration. We find that intensity-based methods are consistent in performance, while feature-based methods can perform better, but are also more variable in success. Ultimately a combined algorithm may be optimal for automatic registration, which can be applied to analyze vasculature information and improve unbiased treatment evaluation in clinical trials.