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Front Neuroinform. 2014 Jan 16;7:50. doi: 10.3389/fninf.2013.00050. eCollection 2013.

Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease.

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Division of Image Processing (LKEB), Department of Radiology, Leiden University Medical Center Leiden, Netherlands.
Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology Erasmus MC, Rotterdam, Netherlands.
Division of Image Processing (LKEB), Department of Radiology, Leiden University Medical Center Leiden, Netherlands ; Intelligent Systems Group, Faculty of EEMCS, Delft University of Technology Delft, Netherlands.
Department of Radiology Erasmus MC, Rotterdam, Netherlands.


Nonrigid image registration is an important, but time-consuming task in medical image analysis. In typical neuroimaging studies, multiple image registrations are performed, i.e., for atlas-based segmentation or template construction. Faster image registration routines would therefore be beneficial. In this paper we explore acceleration of the image registration package elastix by a combination of several techniques: (i) parallelization on the CPU, to speed up the cost function derivative calculation; (ii) parallelization on the GPU building on and extending the OpenCL framework from ITKv4, to speed up the Gaussian pyramid computation and the image resampling step; (iii) exploitation of certain properties of the B-spline transformation model; (iv) further software optimizations. The accelerated registration tool is employed in a study on diagnostic classification of Alzheimer's disease and cognitively normal controls based on T1-weighted MRI. We selected 299 participants from the publicly available Alzheimer's Disease Neuroimaging Initiative database. Classification is performed with a support vector machine based on gray matter volumes as a marker for atrophy. We evaluated two types of strategies (voxel-wise and region-wise) that heavily rely on nonrigid image registration. Parallelization and optimization resulted in an acceleration factor of 4-5x on an 8-core machine. Using OpenCL a speedup factor of 2 was realized for computation of the Gaussian pyramids, and 15-60 for the resampling step, for larger images. The voxel-wise and the region-wise classification methods had an area under the receiver operator characteristic curve of 88 and 90%, respectively, both for standard and accelerated registration. We conclude that the image registration package elastix was substantially accelerated, with nearly identical results to the non-optimized version. The new functionality will become available in the next release of elastix as open source under the BSD license.


Alzheimer's disease; OpenCL; acceleration; elastix; image registration; parallelization

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