A survey of image registration techniques
ACM Computing Surveys (CSUR)
Fast volumetric deformation on general purpose hardware
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS workshop on Graphics hardware
Non-rigid Registration with Use of Hardware-Based 3D Bézier Functions
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
Integrated Registration and Visualization of Medical Image Data
CGI '98 Proceedings of the Computer Graphics International 1998
Brook for GPUs: stream computing on graphics hardware
ACM SIGGRAPH 2004 Papers
Fast Deformable Registration on the GPU: A CUDA Implementation of Demons
ICCSA '08 Proceedings of the 2008 International Conference on Computational Sciences and Its Applications
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Image Processing
A survey of medical image registration on graphics hardware
Computer Methods and Programs in Biomedicine
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Visualization of multimodal images in medicine and other application areas requires correct and efficient registration. Optimally, the alignment operation is made an integral part of the rendering process. Voxel based approaches using mutual information ensure high quality similarity measurement. As a limiting factor, high computational load is caused since for every iteration of the optimization procedure one volume is transformed into the coordinate system of the other, a 2D histogram is generated and mutual information is computed. The expensive trilinear interpolation operations are well supported by 3D texture mapping hardware. However, existing strategies compute the histogram and mutual information on the CPU and thus require a cost intensive data transfer. Overcoming this considerable bottleneck, we introduce a new approach that efficiently supports all computations on modern graphics cards. This makes expensive data transfers from GPU to main memory dispensable. Due to its modularity, the presented approach can be integrated into general frameworks. As a major result, the speed improvement over existing GPU-CPU strategies amounts to a factor of 4 and over pure conventional CPU techniques to more than a factor of 10. Overall, the suggested strategy contributes considerably to integration of multimodal registration directly into interactive volume visualization.