title: Spherical demons: fast surface registration.
abstract: We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation 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 diffeomorphisms, the resulting registration is diffeomorphic and fast - registration 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 popular 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.
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, MIT, USA. ythomas@csail.mit.edu
authors.forename: B T Thomas
authors.initials: BT
authors.lastname: Yeo
authors.forename: Mert
authors.initials: M
authors.lastname: Sabuncu
authors.forename: Tom
authors.initials: T
authors.lastname: Vercauteren
authors.forename: Nicholas
authors.initials: N
authors.lastname: Ayache
authors.forename: Bruce
authors.initials: B
authors.lastname: Fischl
authors.forename: Polina
authors.initials: P
authors.lastname: Golland
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique