Computational anatomy: an emerging discipline

Grenander, Ulf; Miller, Michael I.

doi:10.1090/qam/1668732

Cited by 545 publications

(432 citation statements)

References 116 publications

(182 reference statements)

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“…Fox et al, 2001). All these efforts draw on methods from the rapidly growing field of computational anatomy (see e.g., Ashburner et al, 2003;Bankman, 1999: Bookstein, 2001: Chung, 2001: Csernansky et al, 1998Davatzikos, 1996Davatzikos, , 2001Drury and Van Essen, 1997;Evans et al, 1994;Fischl and Dale, 2000;Fitzpatrick and Sonka, 2000;Gee and Bajcsy, 1998;Gerig et al, 2001;Grenander and Miller, 1998;Leahy and Insana, 2001;Miller et al, 2002;Sereno et al, 1996;Thompson and Toga, 2003a,b;Thompson et al, 2000a,b, 2001a,b, cf. Toga, 1998Toga and Mazziotta, 2002; see other papers in this issue for recent developments and reviews).…”

Section: Computational Anatomymentioning

confidence: 99%

“…This distortion can be expressed using an operator, here denoted by L, which is typically the CauchyNavier differential operator L = mj 2 + (l + m)j(j T ! ), or powers of the Laplacian on space or space time (Christensen et al, 1996;Dupuis et al, 1998;Grenander and Miller, 1998;Toga, 1998;Toga and Thompson, 2003a,b). These operators tend to penalize high values of the Laplacian j 2 u(r) of the flow, as well as high values of the gradient of the divergence of the flow j(j T !…”

Section: Mathematics Of Matching: Covariant Pdesmentioning

confidence: 99%

“…If not, the specific triangulations of the surfaces will affect how the surfaces are matched. In the covariant PDE approach (Christensen et al, 1996;Dupuis et al, 1998;Grenander and Miller, 1998;Thompson et al, 2000a,b;Toga, 1998;Toga and Thompson, 2003a,b), correction terms (Christoffel symbols, C i jk ) make the necessary adjustments for fluctuations in the metric tensor of the mapping procedure. In the partial differential equations (Eq.…”

Section: Mathematics Of Matching: Covariant Pdesmentioning

confidence: 99%

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Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia

et al. 2004

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This paper describes algorithms that can identify patterns of brain structure and function associated with Alzheimer's disease, schizophrenia, normal aging, and abnormal brain development based on imaging data collected in large human populations. Extraordinary information can be discovered with these techniques: dynamic brain maps reveal how the brain grows in childhood, how it changes in disease, and how it responds to medication. Genetic brain maps can reveal genetic influences on brain structure, shedding light on the nature-nurture debate, and the mechanisms underlying inherited neurobehavioral disorders. Recently, we created time-lapse movies of brain structure for a variety of diseases. These identify complex, shifting patterns of brain structural deficits, revealing where, and at what rate, the path of brain deterioration in illness deviates from normal. Statistical criteria can then identify situations in which these changes are abnormally accelerated, or when medication or other interventions slow them. In this paper, we focus on describing our approaches to map structural changes in the cortex. These methods have already been used to reveal the profile of brain anomalies in studies of dementia, epilepsy, depression, childhoodand adult-onset schizophrenia, bipolar disorder, attention-deficit/ hyperactivity disorder, fetal alcohol syndrome, Tourette syndrome, Williams syndrome, and in methamphetamine abusers. Specifically, we describe an image analysis pipeline known as cortical pattern matching that helps compare and pool cortical data over time and across subjects. Statistics are then defined to identify brain structural differences between groups, including localized alterations in cortical thickness, gray matter density (GMD), and asymmetries in cortical organization. Subtle features, not seen in individual brain scans, often emerge when population-based brain data are averaged in this way. Illustrative examples are presented to show the profound effects of development and various diseases on the human cortex. Dynamically spreading waves of gray matter loss are tracked in dementia and schizophrenia, and these sequences are related to normally occurring changes in healthy subjects of various ages. D 2004 Published by Elsevier Inc.

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Section: Computational Anatomymentioning

confidence: 99%

Section: Mathematics Of Matching: Covariant Pdesmentioning

confidence: 99%

Section: Mathematics Of Matching: Covariant Pdesmentioning

confidence: 99%

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Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia

et al. 2004

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“…Spatial normalization involves the registration of images and the generation of a stereotaxic atlas that represents the statistical distribution of the group at each voxel (Friston et al, 1995;Mazziotta et al, 1995;Thompson and Toga, 1997;Grenander and Miller, 1998;Guimond et al, 2000). The registration of diffusion tensor images has generally been performed in a similar way to the registration of T1-weighted or SPGR MR images.…”

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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“…In cases where it is impractical to predict anatomical domains of damage a priori, researchers tend to use a voxel-based approach to characterize statistical differences between groups Rugg-Gunn et al, 2001). VBA involves spatial normalization of brain images to a stereotactic 3D space (Friston 1995;Mazziotta 1995;Thompson, 1997;Grenander, 1998). In order to produce a more normalized distribution of image data, a smoothing function is typically applied to the images (Ashburner and Friston, 2000;Good et al, 2001;Watkins et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

Diffusion tensor imaging segmentation of white matter structures using a Reproducible Objective Quantification Scheme (ROQS)

2007

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Reproducible Objective Quantification Scheme (ROQS) is a novel method for regional white matter measurements of diffusion tensor imaging (DTI) parameters that overcomes the limitations of previous approaches for analyzing large cohorts of subjects reliably. ROQS is a semi-automated technique that exploits the fiber orientation information from the diffusion tensor in conjunction with a binary masking and chain-linking algorithm to segment anatomically distinct white matter tracts for subsequent quantitative analysis of DTI parameters such as fractional anisotropy and apparent diffusion coefficient. When applied to 3 Tesla whole-brain DTI of normal adult volunteers, ROQS is shown to segment the corpus callosum much faster than manual region of interest (ROI) delineation, and with better reproducibility and accuracy.

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Computational anatomy: an emerging discipline

Cited by 545 publications

References 116 publications

Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia

Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia

Spatial normalization of diffusion tensor MRI using multiple channels

Diffusion tensor imaging segmentation of white matter structures using a Reproducible Objective Quantification Scheme (ROQS)

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