Stochastic DT-MRI Connectivity Mapping on the GPU
IEEE Transactions on Visualization and Computer Graphics
GPU Accelerated Non-rigid Registration for the Evaluation of Cardiac Function
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Efficient GPU-accelerated elastic image registration
BioMED '08 Proceedings of the Sixth IASTED International Conference on Biomedical Engineering
Accuracy of GPU-based B-spline evaluation
CGIM '08 Proceedings of the Tenth IASTED International Conference on Computer Graphics and Imaging
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
A survey of medical image registration on graphics hardware
Computer Methods and Programs in Biomedicine
GPU accelerated normalized mutual information and B-spline transformation
EG VCBM'08 Proceedings of the First Eurographics conference on Visual Computing for Biomedicine
Fast parallel unbiased diffeomorphic atlas construction on multi-graphics processing units
EG PGV'09 Proceedings of the 9th Eurographics conference on Parallel Graphics and Visualization
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The presented image registration method uses a regularized gradient flow to correlate the intensities in two images. Thereby, an energy functional is successively minimized by descending along its regularized gradient. The gradient flow formulation makes use of a robust multi-scale regularization, an efficient multi-grid solver and an effective time-step control. The data processing is arranged in streams and mapped onto the functionality of a stream processor. This arrangement automatically exploits the high data parallelism of the problem, and local data access helps to maximize throughput and hide memory latency. Although dedicated stream processors exist, we use a DX9 compatible graphics card as a stream architecture because of its ideal price-performance ratio. The new floating point number formats guarantee a sufficient accuracy of the algorithm and eliminate previously present concerns about the use of graphics hardware for medical computing. Therefore, the implementation achieves reliable results at very high performance, registering two 2572 images in approximately 3sec, such that it could be used as an interactive tool in medical image analysis.