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.
Highlights
Image registration is a frequently used technique in medical image processing
The speedup will depend on the number of threads T that are used for parallelization
In this paper we present a number of CPU and graphics processing unit (GPU) optimizations for the image registration package elastix
Summary
Image registration is a frequently used technique in medical image processing. It refers to the process of automatically aligning imaging data, where a moving (target) image IM is deformed to mimick a fixed (reference) image IF. Registration is the problem of finding a coordinate transformation T that makes IM(T) spatially aligned with IF. The quality of alignment is defined by a cost function C. The optimal coordinate transformation is estimated by minimizing the cost function with respect to T, usually by means of an iterative optimization method embedded in a hierarchical (multiresolution) scheme. Areas of application include the alignment of data sets from different modalities (Mattes et al, 2003) to fuse information, comparison of follow-up with baseline scans (Staring et al, 2007) to follow disease development, alignment of different MR sequences for extraction of quantitative MR parameters such as in diffusion tensor imaging or MR relaxometry (Alexander et al, 2001; Bron et al, 2013), alignment of pre- and post-contrast images (Rueckert et al, 1999) to aid breast cancer detection and diagnosis, and updating treatment plans for radiotherapy and surgery (Pennec et al, 2003)
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