A Parallel GPU Algorithm for Mutual Information Based 3D Nonrigid Image Registration

Many applications in biomedical image analysis require alignment or fusion of images acquired with different devices or at different times. Image registration geometrically aligns images allowing their fusion. Nonrigid techniques are usually required when the images contain anatomical structures of soft tissue. Nonrigid registration algorithms are very time consuming and can take hours for aligning a pair of 3D medical images on commodity workstation PCs. In this paper, we present parallel design and implementation of 3D non-rigid image registration for the Graphics Processing Units (GPUs). Existing GPU-based registration implementations are mainly limited to intra-modality registration problems. Our algorithm uses mutual information as the similarity metric and can process images of different modalities. The proposed design takes advantage of highly parallel and multi-threaded architecture of GPU containing large number of processing cores. The paper presents optimization techniques to effectively utilize high memory bandwidth provided by GPU using on-chip shared memory and co-operative memory update by multiple threads. Our results with optimized GPU implementation showed an average performance of 2.46 microseconds per voxel and achieved factor of 28 speedup over a CPU-based serial implementation. This improves the usability of nonrigid registration for some real world clinical applications and enables new ones, especially within intra-operative scenarios, where strict timing constraints apply.