Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration

• Published in: IEEE transactions on medical imaging Vol. 29; no. 3; pp. 650 - 668
• Main Authors: Yeo, B.T.T., Sabuncu, M.R., Vercauteren, T., Ayache, N., Fischl, B., Golland, P.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2010; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Artificial intelligence; Bioengineering; Biomedical imaging; Cerebral Cortex - anatomy & histology; Computer Science; Cortical registration; demons; diffeomorphism; Engineering profession; Engineering Sciences; Humans; Image Processing, Computer-Assisted; Image segmentation; Imaging; In vivo; Interpolation; Iterative algorithms; Least-Squares Analysis; Life Sciences; Magnetic Resonance Imaging - methods; Medical Imaging; Modeling and Simulation; Models, Biological; Position (location); Reproducibility of Results; Segmentation; Signal and Image processing; Smoothing methods; spherical registration; Spline; surface registration; Surgical implants; Time Factors; vector field interpolation; Warpage; Warping
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2009.2030797

We present the Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizors for the modified Demons objective function can be efficiently approximated on the sphere using iterative smoothing. Based on one parameter subgroups of diffeomorphisms, the resulting registration is diffeomorphic and fast. The Spherical Demons algorithm can also be modified to register a given spherical image to a probabilistic atlas. We demonstrate two variants of the algorithm corresponding to warping the atlas or warping the subject. Registration of a cortical surface mesh to an atlas mesh, both with more than 160 k nodes requires less than 5 min when warping the atlas and less than 3 min when warping the subject on a Xeon 3.2 GHz single processor machine. This is comparable to the fastest nondiffeomorphic landmark-free surface registration algorithms. Furthermore, the accuracy of our method compares favorably to the popular FreeSurfer registration algorithm. We validate the technique in two different applications that use registration to transfer segmentation labels onto a new image (1) parcellation of in vivo cortical surfaces and (2) Brodmann area localization in ex vivo cortical surfaces.