Parallel computation of mutual information on the GPU with application to real-time registration of 3D medical images

Abstract

Due to processing constraints, automatic image-based registration of medical images has been largely used as a pre-operative tool. We propose a novel method named sort and count for efficient parallelization of mutual information (MI) computation designed for massively multi-processing architectures. Combined with a parallel transformation implementation and an improved optimization algorithm, our method achieves real-time (less than 1. s) rigid registration of 3D medical images using a commodity graphics processing unit (GPU). This represents a more than 50-fold improvement over a standard implementation on a CPU. Real-time registration opens new possibilities for development of improved and interactive intraoperative tools that can be used for enhanced visualization and navigation during an intervention.