Nonrigid Registration of 3D Scalar, Vector and Tensor Medical Data

Ruiz-Alzola, Juan; Westin, Carl‐Fredrik; Warfield, Simon K.; Nabavi, Arya; Kikinis, Ron

doi:10.1007/978-3-540-40899-4_55

Cited by 28 publications

(21 citation statements)

References 8 publications

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“…Our group has proposed diffusion tensor registration methods using tensor similarity (Ruiz-Alzola et al, 2000, 2002 and multiple channel information (Guimond et al, 2002). Ruiz-Alzola and colleagues (Ruiz-Alzola et al, 2000, 2002 extended the general concept of intensity-based similarity in registration to the tensor case and also proposed an interpolation method by means of the Kriging estimator. Their work is based on template matching by locally optimized similarity function.…”

Section: Introductionmentioning

confidence: 99%

Spatial normalization of diffusion tensor MRI using multiple channels

et al. 2003

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Diffusion Tensor MRI (DT-MRI) can provide important in vivo information for the detection of brain abnormalities in diseases characterized by compromised neural connectivity. To quantify diffusion tensor abnormalities based on voxel-based statistical analysis, spatial normalization is required to minimize the anatomical variability between studied brain structures. In this article, we used a multiple input channel registration algorithm based on a demons algorithm and evaluated the spatial normalization of diffusion tensor image in terms of the input information used for registration. Registration was performed on 16 DT-MRI data sets using different combinations of the channels, including a channel of T2-weighted intensity, a channel of the fractional anisotropy, a channel of the difference of the first and second eigenvalues, two channels of the fractional anisotropy and the trace of tensor, three channels of the eigenvalues of the tensor, and the six channel tensor components. To evaluate the registration of tensor data, we defined two similarity measures, i.e., the endpoint divergence and the mean square error, which we applied to the fiber bundles of target images and registered images at the same seed points in white matter segmentation. We also evaluated the tensor registration by examining the voxel-by-voxel alignment of tensors in a sample of 15 normalized DTMRIs. In all evaluations, nonlinear warping using six independent tensor components as input channels showed the best performance in effectively normalizing the tract morphology and tensor orientation. We also present a nonlinear method for creating a group diffusion tensor atlas using the average tensor field and the average deformation field, which we believe is a better approach than a strict linear one for representing both tensor distribution and morphological distribution of the population.

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Section: Introductionmentioning

confidence: 99%

Spatial normalization of diffusion tensor MRI using multiple channels

et al. 2003

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“…h Our space includes a set of 3-D curves and a number of cluster centers. From each center, μ k , we construct an Euclidean distance map: (2) and the nearest-neighbor transform, ℒ k : (3) where d(x, μ kj ) is the Euclidean distance from the point x in the space to the jth point on the kth center. Each element of ℒ k will thus contain the linear index of the nearest point of the center μ k .…”

Section: Similarity Measurementioning

confidence: 99%

“…Such methods are sensitive to the accuracy of specifying the ROIs and are prone to user errors. Others have performed a voxel-based analysis of a registered DTI dataset, which requires non-linear warping of the tensor field [2], which in turn needs re-orientation of the tensors [3,4].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Clustering and Quantitative Analysis of White Matter Fiber Tracts

Maddah

Wells

Warfield

et al. 2007

Lecture Notes in Computer Science

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A novel framework for joint clustering and point-by-point mapping of white matter fiber pathways is presented. Accurate clustering of the trajectories into fiber bundles requires point correspondence along the fiber pathways determined. This knowledge is also crucial for any tractoriented quantitative analysis. We employ an expectation-maximization (EM) algorithm to cluster the trajectories in a Gamma mixture model context. The result of clustering is the probabilistic assignment of the fiber trajectories to each cluster, an estimate of the cluster parameters, and point correspondences. Point-by-point correspondence of the trajectories within a bundle is obtained by constructing a distance map and a label map from each cluster center at every iteration of the EM algorithm. This offers a time-efficient alternative to pairwise curve matching of all trajectories with respect to each cluster center. Probabilistic assignment of the trajectories to clusters is controlled by imposing a minimum threshold on the membership probabilities, to remove outliers in a principled way. The presented results confirm the efficiency and effectiveness of the proposed framework for quantitative analysis of diffusion tensor MRI.

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“…Furthermore, using many degrees of freedom in the spatial transformation of tensor maps could diminish the real differences in tensor morphology across groups. DTI datasets, however, can be normalized accurately using any one of several excellent existing methods (Ashburner and Friston, 1999;Alexander et al, 2001;Ruiz-Alzola et al, 2000Xu et al, 2003) (Netsch and Muiswinkel, 2003;Park et al, 2003;Westin and Knutsson, 2003;Wang et al, 2004) (Bansal et al, 2005b), thereby avoiding this inappropriate elimination of real group differences. We should note that although the accurate spatial normalization of DTI datasets is essential for the valid comparison of tensor morphologies across groups, our method for the statistical comparison of tensor morphologies is independent of the method used to normalize DT images.…”

Section: Introductionmentioning

confidence: 99%

Voxel-wise comparisons of the morphology of diffusion tensors across groups of experimental subjects

Bansal

Staib

Plessen

et al. 2007

Psychiatry Research: Neuroimaging

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Water molecules in the brain diffuse preferentially along the fiber tracts within white matter, which form the anatomical connections across spatially distant brain regions. A diffusion tensor (DT) is a probabilistic ellipsoid composed of 3 orthogonal vectors, each having a direction and an associated scalar magnitude, that represent the probability of water molecules diffusing in each of those directions. The 3D morphologies of DTs can be compared across groups of subjects to reveal disruptions in structural organization and neuroanatomical connectivity of the brains of persons with various neuropsychiatric illnesses. Comparisons of tensor morphology across groups have typically been performed on scalar measures of diffusivity, such as Fractional Anisotropy (FA), rather than directly on the complex 3D morphologies of DTs. Scalar measures, however, are related in nonlinear ways to the eigenvalues and eigenvectors that create the 3D morphologies of DTs. We present a mathematical framework that permits the direct comparison across groups of mean eigenvalues and eigenvectors of individual DTs. We show that group-mean eigenvalues and eigenvectors are multivariate Gaussian distributed, and we use the Delta method to compute their approximate covariance matrices. Our results show that the theoretically computed Mean Tensor (MT) eigenvectors and eigenvalues match well with their respective true values. Furthermore, a comparison of synthetically generated groups of DTs highlights the limitations of using FA to detect group differences. Finally, analyses of in vivo DT data using our method reveal significant between-group differences in diffusivity along fiber tracts within white matter, whereas analyses based on FA values failed to detect some of these differences.

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Nonrigid Registration of 3D Scalar, Vector and Tensor Medical Data

Cited by 28 publications

References 8 publications

Spatial normalization of diffusion tensor MRI using multiple channels

Spatial normalization of diffusion tensor MRI using multiple channels

Probabilistic Clustering and Quantitative Analysis of White Matter Fiber Tracts

Voxel-wise comparisons of the morphology of diffusion tensors across groups of experimental subjects

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