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.
Highlights
Neuroimage registration is useful for many neuroimage applications such as statistical quantification of human brain morphometry and computer-aided diagnosis
For FLIRT acceleration, the method was proposed in[17] is adopted, which just focused on the accelerating scheme on the linear registration tool FLIRT with correlation ratio (CR) as the similarity measure. This paper extends it with more experiments about neuroimage registration and further analysis about the accelerated algorithm
A randomly selected image was chosen as the reference image, and the other images were considered as moving images and registered to the reference image
Summary
Neuroimage registration is useful for many neuroimage applications such as statistical quantification of human brain morphometry and computer-aided diagnosis. Shams [5] accelerated 3D medical images registration by computing mutual information (MI) as the similarity metric on GPU. Since correlation ratio (CR) is a more widely used similarity metric in many applications [14], Li presented an accelerated FLIRT for volume image registration with CR as the similarity metric on GPUs [17].
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