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Accelerating non-linear image registration with GPUs

Paul Ross
Edinburgh Parallel Computing Centre, The University of Edinburgh
The University of Edinburgh, 2011

@article{ross2011accelerating,

   title={Accelerating non-linear image registration with GPUs},

   author={Ross, P.},

   year={2011}

}

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The alignment or registration of two images or volumetric datasets is frequently a requirement in modern image-processing applications, particularly within the context of medical imaging. Modern graphics-processing units (GPUs) are designed to perform simple 3D graphics-pipeline tasks on a massively parallel scale; this processing power can be harnessed for general computation via libraries such as Nvidia’s CUDA or the cross-platform standard OpenCL. By exploiting the unique hardware features of GPUs, a signi?cant performance improvement for registration applications can be achieved. As a result, the performance of one of the major bottlenecks has been improved by up to a factor of approximately 1000; this factor is likely to increase with larger datasets.
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