Efficient diffeomorphic metric image registration via stationary velocity

摘要

Diffeomorphic image registration is a fundamental tool for MRI analysis, and fast growing image data demand highly efficient registration methods. Stationary velocity based method has much faster speed than non-stationary one in producing diffeomorphism, but how to achieve validity for large deformation while keeping its efficiency remains to be solved. To this end, we have proposed a new simplified model for optimal stationary velocity by representing temporal integration form of transformation variation via a single point model, e.g. the middle time point, according to mean value theorem and smoothness of transformation. This model can maintain the same registration accuracy as integral model but reduce the time cost a lot. It also shows as a better strategy than first order approximation model for large deformation mapping. Comparative study has also been conducted between this method and non-stationary approach on both synthesized and real brain images, and this approach demonstrated comparable whole brain mapping accuracy with much faster speed. Results showed efficacy of this approach in reducing complexity of stationary velocity based diffeomorphic registration while achieving validity for large deformation. (C) 2018 Elsevier B.V. All rights reserved.