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Accelerating Multi-Scale Flows for LDDKBM Diffeomorphic Registration

Stefan Sommer
Department of Computer Science, University of Copenhagen, Universitetsparken 1, DK-2100 Copenhagen E, Denmark
GPUCV workshop at ICCV 2011, 2011

@article{sommer2011accelerating,

   title={Accelerating Multi-Scale Flows for LDDKBM Diffeomorphic Registration},

   author={Sommer, S.},

   booktitle={GPUCV workshop at ICCV 2011},

   year={2011}

}

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Registrations in medical imaging and computational anatomy can be obtained using the Large Deformation Diffeomorphic Kernel Bundle Mapping (LDDKBM) framework. This provides a registration algorithm with a solid mathematical foundation while incorporating regularization of deformation at multiple scales. Because the variational formulation of LDDKBM implies a heavy computational burden in the search for optimal registrations, exploiting every possibility for faster computation will improve the usability of the algorithm. We present a parallelization strategy using the multi-scale structure and show that the parallelized method constitutes an example of how the processing power of GPUs can massively reduce the running time: after moving the computation to the GPU, we achieve a two order of magnitude speedup over a singlethreaded CPU implementation. Not only does this significantly reduce the cost of using multiple scales, it also allows the algorithm to be used on much larger datasets.
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