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Accelerating Neuroimage Registration through Parallel Computation of Similarity Metric

Author

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  • Yun-gang Luo
  • Ping Liu
  • Lin Shi
  • Yishan Luo
  • Lei Yi
  • Ang Li
  • Jing Qin
  • Pheng-Ann Heng
  • Defeng Wang

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.

Suggested Citation

  • Yun-gang Luo & Ping Liu & Lin Shi & Yishan Luo & Lei Yi & Ang Li & Jing Qin & Pheng-Ann Heng & Defeng Wang, 2015. "Accelerating Neuroimage Registration through Parallel Computation of Similarity Metric," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-14, September.
    • Handle: RePEc:plo:pone00:0136718
    DOI: 10.1371/journal.pone.0136718
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