Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration

Computer Science and Artificial Intelligence Laboratory, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
IEEE Trans Med Imaging 09/2009; 29:650-668. DOI: 10.1109/TMI.2009.2030797
Source: DBLP


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.

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Available from: Mert R Sabuncu, Jun 19, 2014
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    • "Surface registration is obtained by matching geometric quantities defined on each surfaces. Some commonly chosen geometric quantities are surface curvatures (Yeo et al., 2010), surface metric (Lord et al., 2007) , angular structure (Haker et al., 2000; Gu et al., 2004; Jin et al., 2008; Hurdal and Stephenson, 2009) and convexity (Fischl et al., 1999b). The third category of registration models is the hybrid surface registration algorithm, which combines both landmarkguided and geometry-guided registration together. "
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    • "A number of algorithms that enforce inverse consistency have been proposed. A representative set of such algorithms can be found in [13] [14] [15] [16] [17] [18] [19] [20] [21] [22]. The consistency of three transformations was used by Holden et al. [12], Freeborough [24], Woods et al. [11], and Christensen [10] to compare and evaluate several similarity measures and registration algorithms. "
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    • "Anatomical measures like cortical thickness, sulcal depth, and gyrification index, which can be directly derived from this mathematical representation, have been widely used in the study of both healthy and diseased brains (Thompson et al. 2007; Nordahl et al. 2007; Schmitt et al. 2007; Im et al. 2008; Goldman et al. 2009; Thambisetty et al. 2010). Cortical reconstruction is also the first step in many neuroimaging algorithms including but not limited to cortical labeling (Bruce Fischl et al. 2004; Desikan et al. 2006; Destrieux et al. 2010), surface based registration (B Fischl et al. 1999; Tosun et al. 2004; Lyttelton et al. 2007; Tosun and Prince 2008; Yeo et al. 2010), and surface based morphometry (Chung et al. 2003; Chung, Dalton, and Davidson 2008; Fornito et al. 2008). "
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