Accelerating Neuroimage Registration through Parallel Computation of Similarity Metric Accelerating Neuroimage Registration with Parallel Computating

Abstract

'Jing Qin' 'Pheng-Ann Heng' 'Defeng Wang' 'Dzung Pham'] 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.