An accelerative method for multimodality medical image registration based on CUDA

Image registration algorithm based on normal mutual information (NMI) has been adopted in multimodality medical image registration research for a few years. Basing on the whole information of the image pixels, this method has no incertitude of the problems about the similar parts, different range of values, segmentation, boundary, and so on. The results have always been more satisfied by doctors than other registration methods. However, medical images usually contain a huge amount of data and the calculation of mutual information takes a lot of statistical analyses. Therefore the operation time is too long to apply in clinical. According to this problem, researchers have intended to improve the algorithm on two aspects, namely software and hardware. Compute Unified Device Architecture (CUDA) is a new technology of parallel computing with broad-ranging application on image processing, reconstruction, analysis, and much more. Above all, we proposed a new improvement CUDA based method to accelerate NMI registration algorithm. Experiments show it cannot only ensure to provide a correct registration images but also an efficiency improvement as well. And the total speedup ratio nearly reaches 10.