Abstract
Neuroimage registration is crucial for brain morphometric analysis and treatment efficacy evaluation. However, existing advanced registration algorithms such as FLIRT and ANTs are not efficient enough for clinical use. In this paper, a GPU implementation of FLIRT with the correlation ratio (CR) as the similarity metric and a GPU accelerated correlation coefficient (CC) calculation for the symmetric diffeomorphic registration of ANTs have been developed. The comparison with their corresponding original tools shows that our accelerated algorithms can greatly outperform the original algorithm in terms of computational efficiency. This paper demonstrates the great potential of applying these registration tools in clinical applications.
Publication types
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Research Support, Non-U.S. Gov't
MeSH terms
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Algorithms
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Brain*
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Humans
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Image Processing, Computer-Assisted / methods*
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Imaging, Three-Dimensional / methods*
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Software
Grants and funding
The work described in this paper was supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No.: CUHK 475711, CUHK 416712, CUHK 473012, CUHK 14113214), a grant from Ministry of Science and Technology of the People's Republic of China (Project No.: 2013DFG12900), and grants from the National Natural Science Foundation of China (Project No. 61233012, and 81201157), and grants from The Science, Technology and Innovation Commission of Shenzhen Municipality (No. CXZZ20140606164105361, JCYJZ20130401112102666), a grant of Shenzhen Basic Research Project (No. CYJ20130402113127511), a grant from Natural Science Foundation of Guangdong (No. S2013010014973) and by project BME-p2-13/BME-CUHK of the Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong.