Optimization of non-linear image registration in AFNI
| Autor: | R. Glenn Brook, Frank Skidmore, Yuliang Liu, Thomas Anthony, Jon Marstrander, Lonnie D. Crosby, Mitchel D. Horton, Chad Burdyshaw, Junqi Yin |
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| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Speedup Iterative method business.industry Computer science Image registration Pattern recognition Translation (geometry) Pearson product-moment correlation coefficient 03 medical and health sciences symbols.namesake 030104 developmental biology 0302 clinical medicine Benchmark (computing) symbols Computer vision Affine transformation Artificial intelligence Image warping business 030217 neurology & neurosurgery |
| Zdroj: | XSEDE |
| Popis: | The Analysis of Functional Neuroimaging (AFNI) suite [1], a set of C programs and auxiliary scripts for processing, analyzing, and displaying functional Magnetic Resonance Imaging (fMRI) data (a technique for mapping human brain activity), is a widely adopted open-source tool in the MRI data analysis community. For many types of analysis pipelines, a key step is to register a subject's image to a pre-defined template so different images can be compared within a normalized coordinate. Although a 12-point affine transformation that includes translation, rotation, scaling, and shear works fine for some standard cases, it is usually found to be insufficient for voxel-wise types of analyses. The need for some other approach is exacerbated if the subject has brain atrophy due to some kind of neurological conditions such as Parkinson's disease. The 3dQwarp code in AFNI is a non-linear image registration procedure that overcomes the drawbacks of a canonical affine transformation. However, the existing OpenMP parallelization in 3dQwarp does not scale well when warping at an ultra fine level, and the hard-coded number of iterations of the iterative algorithm also limits the accuracy. Based on profiling and benchmark analysis, we improve the parallel efficiency of 3dQwarp by the optimization of its OpenMP structure and obtain about a 2x speedup for a normalized workload. With the incorporation of convergence criteria, we are able to perform warping at a much finer resolution than before and achieve on average 20% improvement in accuracy with respect to Pearson correlation measure. |
| Databáze: | OpenAIRE |
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