Spherical demons: Fast surface registration

Abstract. We present the fast Spherical Demons algorithm for register-ing two spherical images. By exploiting spherical vector spline interpo-lation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently implemented on the sphere using convolution. Based on the one parameter subgroups of diffeomor-phisms, the resulting registration is diffeomorphic and fast – registra-tion of two cortical mesh models with more than 100k nodes takes less than 5 minutes, comparable to the fastest surface registration algorithms. Moreover, the accuracy of our method compares favorably to the popu-lar FreeSurfer registration algorithm. We validate the technique in two different settings: (1) parcellation in a set of in-vivo cortical surfaces and (2) Brodmann area localization in ex-vivo cortical surfaces.