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

ABSTRACT 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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    • "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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    ABSTRACT: We address the registration problem of genus-one surfaces (such as vertebrae bones) with prescribed landmark constraints. The high-genus topology of the surfaces makes it challenging to obtain a unique and bijective surface mapping that matches landmarks consistently. This work proposes to tackle this registration problem using a special class of quasi-conformal maps called Teichmüller maps (T-Maps). A landmark constrained T-Map is the unique mapping between genus-1 surfaces that minimizes the maximal conformality distortion while matching the prescribed feature landmarks. Existence and uniqueness of the landmark constrained T-Map are theoretically guaranteed. This work presents an iterative algorithm to compute the T-Map. The main idea is to represent the set of diffeomorphism using the Beltrami coefficients (BC). The BC is iteratively adjusted to an optimal one, which corresponds to our desired T-Map that matches the prescribed landmarks and satisfies the periodic boundary condition on the universal covering space. Numerical experiments demonstrate the effectiveness of our proposed algorithm. The method has also been applied to register vertebrae bones with prescribed landmark points and curves, which gives accurate surface registrations. Copyright © 2015 Elsevier B.V. All rights reserved.
    Medical Image Analysis 04/2015; DOI:10.1016/ · 3.68 Impact Factor
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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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    ABSTRACT: Identification of error in non-rigid registration is a critical problem in the medical image processing community. We recently proposed an algorithm that we call "Assessing Quality Using Image Registration Circuits" (AQUIRC) to identify non-rigid registration errors and have tested its performance using simulated cases. In this article, we extend our previous work to assess AQUIRC's ability to detect local non-rigid registration errors and validate it quantitatively at specific clinical landmarks, namely the Anterior Commissure (AC) and the Posterior Commissure (PC). To test our approach on a representative range of error we utilize 5 different registration methods and use 100 target images and 9 atlas images. Our results show that AQUIRC's measure of registration quality correlates with the true target registration error (TRE) at these selected landmarks with an R² = 0.542. To compare our method to a more conventional approach, we compute Local Normalized Correlation Coefficient (LNCC) and show that AQUIRC performs similarly. However, a multi-linear regression performed with both AQUIRC's measure and LNCC shows a higher correlation with TRE than correlations obtained with either measure alone, thus showing the complementarity of these quality measures. We conclude the article by showing that the AQUIRC algorithm can be used to reduce registration errors for all five algorithms.
    IEEE Transactions on Medical Imaging 07/2014; 34(1). DOI:10.1109/TMI.2014.2344911 · 3.80 Impact Factor
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    • "Our primary goal with this paper, and set of results , is to demonstrate that the proposed discrete optimisation framework is highly flexible and is capable of aligning cortical surfaces using a variety of different feature sets, in ways that improve functional colocalisation . To this end: the Univariate alignment section shows that the discrete method can perform folding-based alignment with similar accuracy and areal distortion as two state-of-the-art continuous methods (FreeSurfer (Fischl et al., 1999a) and Spherical Demons (Yeo et al., 2010)); and the Resting state fMRI driven alignment section shows how high-quality resting-state functional MRI alignment is sufficiently generalisable to also improve the alignment of task activa- tions. "
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    ABSTRACT: Surface-based cortical registration methods that are driven by geometrical features, such as folding, provide sub-optimal alignment of many functional areas due to variable correlation between cortical folding patterns and function. This has led to the proposal of new registration methods using features derived from functional and diffusion imaging. However, as yet there is no consensus over the best set of features for optimal alignment of brain function. In this paper we demonstrate the utility of a new Multimodal Surface Matching (MSM) algorithm capable of driving alignment using a wide variety of descriptors of brain architecture, function and connectivity. The versatility of the framework originates from adapting the discrete Markov Random Field (MRF) registration method to surface alignment. This has the benefit of being unconstrained by choice of a similarity measure and relatively insensitive to local minima. The method offers significant flexibility in the choice of feature set, and we demonstrate the advantages of this by performing registrations using univariate descriptors of surface curvature and myelination, multivariate feature sets derived from resting fMRI, and multimodal descriptors of surface curvature and myelination. We compare the results with two state of the art surface registration methods that use geometric features: FreeSurfer and Spherical Demons. In the future, the MSM technique will allow explorations into the best combinations of features and alignment strategies for inter-subject alignment of cortical functional areas for a wide range of neuroimaging data sets.
    NeuroImage 06/2014; 100. DOI:10.1016/j.neuroimage.2014.05.069 · 6.36 Impact Factor
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