Performance analysis of accelerated image registration using GPGPU
Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units
Computer Methods and Programs in Biomedicine
Accelerating 3D nonrigid registration using the cell broadband engine processor
IBM Journal of Research and Development
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Accelerating POCS interpolation of 3D irregular seismic data with Graphics Processing Units
Computers & Geosciences
A Parallel GPU algorithm for mutual information based 3D nonrigid image registration
Euro-Par'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part II
A survey of medical image registration on graphics hardware
Computer Methods and Programs in Biomedicine
CUDA optimization strategies for compute- and memory-bound neuroimaging algorithms
Computer Methods and Programs in Biomedicine
True 4D image denoising on the GPU
Journal of Biomedical Imaging - Special issue on Parallel Computation in Medical Imaging Applications
GPU accelerated normalized mutual information and B-spline transformation
EG VCBM'08 Proceedings of the First Eurographics conference on Visual Computing for Biomedicine
A GPU approach for accelerating 3D deformable registration (DARTEL) on brain biomedical images
Proceedings of the 20th European MPI Users' Group Meeting
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In the medical imaging field, we need fast deformable registration methods especially in intra-operative settings characterized by their time-critical applications. Image registration studies which are based on Graphics Processing Units (GPUs) provide fast implementations. However, only a small number of these GPU-based studies concentrate on deformable registration. We implemented Demons, a widely used deformable image registration algorithm, on NVIDIA’s Quadro FX 5600 GPU with the Compute Unified Device Architecture (CUDA) programming environment. Using our code, we registered 3D CT lung images of patients. Our results show that we achieved the fastest runtime among the available GPU-based Demons implementations. Additionally, regardless of the given dataset size, we provided a factor of 55 speedup over an optimized CPU-based implementation. Hence, this study addresses the need for on-line deformable registration methods in intra-operative settings by providing the fastest and most scalable Demons implementation available to date. In addition, it provides an implementation of a deformable registration algorithm on a GPU, an understudied type of registration in the general-purpose computation on graphics processors (GPGPU) community.