Print Email Facebook Twitter Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease Title Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease Author Shamonin, D.P. Bron, E.E. Lelieveldt, B.P.F. Smits, M. Klein, S. Staring, M. Faculty Electrical Engineering, Mathematics and Computer Science Department Intelligent Systems Date 2014-01-16 Abstract 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. Subject Alzheimer's diseaseOpenCLaccelerationelastiximage registrationparallelization To reference this document use: http://resolver.tudelft.nl/uuid:ac9c71b2-907a-4dab-a733-847fcbab8f06 DOI https://doi.org/10.3389/fninf.2013.00050 Publisher Frontiers ISSN 1662-5196 Source Frontiers in Neuroinformatics, 7, 2014 Part of collection Institutional Repository Document type journal article Rights (c) 2014 The Author(s)Creative Commons BY Files PDF Lelieveldt_2014.pdf 1.54 MB Close viewer /islandora/object/uuid:ac9c71b2-907a-4dab-a733-847fcbab8f06/datastream/OBJ/view