Accelerated Medical Image Registration using the Graphics Processing Unit by Daniel Henrik Adler

Registration of three-dimensional images is an important task in biomedical science. However, computational costs of 3D registration algorithms have hindered their widespread use in many clinical and research workflows. We describe an automated medical image registration framework, including a novel implementation of the mutual information similarity metric, that executes entirely on the commodity graphics processing unit (GPU). Our methods take advantage of the graphics hardware’s high computational parallelism and memory bandwidth to perform a ne, intensity-based registration of multi-modal 3D medical images at near interactive rates. We also accelerate the Demons algorithm for deformable registration on the GPU. Registration results generated using our GPU-based methods are equivalent to those generated by conventional software-based methods, but with an order of magnitude reduction in computation time.