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Christos Davatzikos

Publications and source records attributed to Christos Davatzikos.

At least 37 records · Page 2Linked to original sources
PubMed2005

Statistical representation and simulation of high-dimensional deformations: application to synthesizing brain deformations.

This paper proposes an approach to effectively representing the statistics of high-dimensional deformations, when relatively few training samples are available, and conventional methods, like PCA, fail due to insufficient training. Based on previous work on scale-space decomposition of deformation fields, herein we represent the space of "valid deformations" as the intersection of three subspaces: one that satisfies constraints on deformations themselves, one that satisfies constraints on Jacobian determinants of deformations, and one that represents smooth deformations via a Markov Random Field (MRF). The first two are extensions of PCA-based statistical shape models. They are based on a wavelet packet basis decomposition that allows for more accurate estimation of the covariance structure of deformation or Jacobian fields, and they are used jointly due to their complementary strengths and limitations. The third is a nested MRF regularization aiming at eliminating potential discontinuities introduced by assumptions in the statistical models. A randomly sampled deformation field is projected onto the space of valid deformations via iterative projections on each of these subspaces until convergence, i.e. all three constraints are met. A deformation field simulator uses this process to generate random samples of deformation fields that are not only realistic but also representative of the full range of anatomical variability. These simulated deformations can be used for validation of deformable registration methods. Other potential uses of this approach include representation of shape priors in statistical shape models as well as various estimation and hypothesis testing paradigms in the general fields of computational anatomy and pattern recognition.

Algorithms↗
PubMed2004

Optimized prostate biopsy via a statistical atlas of cancer spatial distribution.

A methodology is presented for constructing a statistical atlas of spatial distribution of prostate cancer from a large patient cohort, and it is used for optimizing needle biopsy. An adaptive-focus deformable model is used for the spatial normalization and registration of 100 prostate histological samples, which were provided by the Center for Prostate Disease Research of the US Department of Defense, resulting in a statistical atlas of spatial distribution of prostate cancer. Based on this atlas, a statistical predictive model was developed to optimize the needle biopsy sites, by maximizing the probability of detecting cancer. Experimental results using cross-validation show that the proposed method can detect cancer with a 99% success rate using seven needles, in these samples.

Algorithms↗
PubMed2004

Morphological classification of brains via high-dimensional shape transformations and machine learning methods.

A high-dimensional shape transformation posed in a mass-preserving framework is used as a morphological signature of a brain image. Population differences with complex spatial patterns are then determined by applying a nonlinear support vector machine (SVM) pattern classification method to the morphological signatures. Significant reduction of the dimensionality of the morphological signatures is achieved via wavelet decomposition and feature reduction methods. Applying the method to MR images with simulated atrophy shows that the method can correctly detect subtle and spatially complex atrophy, even when the simulated atrophy represents only a 5% variation from the original image. Applying this method to actual MR images shows that brains can be correctly determined to be male or female with a successful classification rate of 97%, using the leave-one-out method. This proposed method also shows a high classification rate for old adults' age classification, even under difficult test scenarios. The main characteristic of the proposed methodology is that, by applying multivariate pattern classification methods, it can detect subtle and spatially complex patterns of morphological group differences which are often not detectable by voxel-based morphometric methods, because these methods analyze morphological measurements voxel-by-voxel and do not consider the entirety of the data simultaneously.

Aged↗
PubMed2004

Measuring temporal morphological changes robustly in brain MR images via 4-dimensional template warping.

Robustly measuring subtle longitudinal brain changes is a major challenge in computational neuroanatomy. This paper describes a method for measuring temporal morphological brain changes, by means of a 4-dimensional image warping mechanism. Longitudinal stability is achieved by considering all temporal MR images of an individual simultaneously, rather than by individually warping a template to an individual, or by warping the images of one time-point to those of another time-point. Following earlier work in 3D, a local morphological signature is attached to each voxel of a sequence of images, and it includes a set of image attributes reflecting morphological characteristics of the spatiotemporal structure around the respective voxel at different scales. This attribute vector forms the basis for searching in the 4-dimensional space for a counterpart that has similar morphological signature, thereby leading to automated detection of anatomical correspondence. Ambiguities in this process are reduced by constructing attribute vectors that are highly distinctive of respective voxels, and by using a hierarchical matching procedure in which reliable and easily distinguishable voxels are used to guide the 4D deformation process. The resultant deformations are smooth both in the spatial and temporal dimensions, and are shown to significantly improve warping accuracy over a series of independent 3D warpings, in longitudinal measurements.

Algorithms↗
PubMed2004

Deformable registration of cortical structures via hybrid volumetric and surface warping.

Registration of cortical structures across individuals is a very important step for quantitative analysis of the human brain cortex. This paper presents a method for deformable registration of cortical structures across individuals, using hybrid volumetric and surface warping. In the first step, a feature-based volumetric registration algorithm is used to warp a model cortical surface to the individual's space. This step greatly reduces the variation between the model and individual, thus providing a good initialization for the next step of surface warping. In the second step, a surface registration method, based on matching geometric attributes, warps the model surface to the individual. Point correspondences are also established at this step. The attribute vector, as the morphological signature of surface, was designed to be as distinctive as possible, so that each vertex on the model surface can find its correspondence on the individual surface. Experimental results on both synthesized and real brain data demonstrate the performance of the proposed method in the registration of cortical structures across individuals.

Artifacts↗
PubMed2004

Why voxel-based morphometric analysis should be used with great caution when characterizing group differences.

A variety of voxel-based morphometric analysis methods have been adopted by the neuroimaging community in the recent years. In this commentary we describe why voxel-based statistics, which are commonly used to construct statistical parametric maps, are very limited in characterizing morphological differences between groups, and why the effectiveness of voxel-based statistics is significantly biased toward group differences that are highly localized in space and of linear nature, whereas it is significantly reduced in cases with group differences of similar or even higher magnitude, when these differences are spatially complex and subtle. The complex and often subtle and nonlinear ways in which various factors, such as age, sex, genotype and disease, can affect brain morphology, suggest that alternative, unbiased methods based on statistical learning theory might be able to better quantify brain changes that are due to a variety of factors, especially when relationships between brain networks, rather than individual structures, and disease are examined.

Bias↗
PubMed2004

Automated morphometric study of brain variation in XXY males.

This paper studies brain morphometry variation associated with XXY males (Klinefelter's syndrome) by using an automated whole-brain volumetric analysis method. The application to 34 XXY males and 62 normal male controls reveals pronounced volume reduction in the brains of XXY males, relative to the brains of normal controls, localized at the insula, temporal gyri, amygdala, hippocampus, cingulate, and occipital gyri. Most of these statistically significant regions are in the gray matter structures, with the exception of one cluster of atrophy involved in white matter structure, i.e., right parietal lobe white matter. Compared to previous findings documented in the literature, our findings provide a better spatial localization of the affected regions. In addition to the reduction of local volume, overall enlargement of ventricles and overall volume reduction of both white matter and gray matter are also found in XXY males.

Adolescent↗
PubMed2004

A Bayesian morphometry algorithm.

Most methods for structure-function analysis of the brain in medical images are usually based on voxel-wise statistical tests performed on registered magnetic resonance (MR) images across subjects. A major drawback of such methods is the inability to accurately locate regions that manifest nonlinear associations with clinical variables. In this paper, we propose Bayesian morphological analysis methods, based on a Bayesian-network representation, for the analysis of MR brain images. First, we describe how Bayesian networks (BNs) can represent probabilistic associations among voxels and clinical (function) variables. Second, we present a model-selection framework, which generates a BN that captures structure-function relationships from MR brain images and function variables. We demonstrate our methods in the context of determining associations between regional brain atrophy (as demonstrated on MR images of the brain), and functional deficits. We employ two data sets for this evaluation: the first contains MR images of 11 subjects, where associations between regional atrophy and a functional deficit are almost linear; the second data set contains MR images of the ventricles of 84 subjects, where the structure-function association is nonlinear. Our methods successfully identify voxel-wise morphological changes that are associated with functional deficits in both data sets, whereas standard statistical analysis (i.e., t-test and paired t-test) fails in the nonlinear-association case.

Aged↗
PubMed2004

Estimating topology preserving and smooth displacement fields.

We propose a method for enforcing topology preservation and smoothness onto a given displacement field. We first analyze the conditions for topology preservation on two- and three-dimensional displacement fields over a discrete rectangular grid. We then pose the problem of finding the closest topology preserving displacement field in terms of its complete set of gradients, which we later solve using a cyclic projections framework. Adaptive smoothing of a displacement field is then formulated as an extension of topology preservation, via constraints imposed on the Jacobian of the displacement field. The simulation results indicate that this technique is a fast and reliable method to estimate a topology preserving displacement field from a noisy observation that does not necessarily preserve topology. They also show that the proposed smoothing method can render morphometric analysis methods that are based on displacement field of shape transformations more robust to noise without removing important morphologic characteristics.

Algorithms↗
PubMed2004

Determining correspondence in 3-D MR brain images using attribute vectors as morphological signatures of voxels.

Finding point correspondence in anatomical images is a key step in shape analysis and deformable registration. This paper proposes an automatic correspondence detection algorithm for intramodality MR brain images of different subjects using wavelet-based attribute vectors (WAVs) defined on every image voxel. The attribute vector (AV) is extracted from the wavelet subimages and reflects the image structure in a large neighborhood around the respective voxel in a multiscale fashion. It plays the role of a morphological signature for each voxel, and our goal is, therefore, to make it distinctive of the respective voxel. Correspondence is then determined from similarities of AVs. By incorporating the prior knowledge of the spatial relationship among voxels, the ability of the proposed algorithm to find anatomical correspondence is further improved. Experiments with MR images of human brains show that the algorithm performs similarly to experts, even for complex cortical structures.

Aged↗
PubMed2003

Longitudinal magnetic resonance imaging studies of older adults: a shrinking brain.

Age-related loss of brain tissue has been inferred from cross-sectional neuroimaging studies, but direct measurements of gray and white matter changes from longitudinal studies are lacking. We quantified longitudinal magnetic resonance imaging (MRI) scans of 92 nondemented older adults (age 59-85 years at baseline) in the Baltimore Longitudinal Study of Aging to determine the rates and regional distribution of gray and white matter tissue loss in older adults. Using images from baseline, 2 year, and 4 year follow-up, we found significant age changes in gray (p < 0.001) and white (p < 0.001) volumes even in a subgroup of 24 very healthy elderly. Annual rates of tissue loss were 5.4 +/- 0.3, 2.4 +/- 0.4, and 3.1 +/- 0.4 cm3 per year for total brain, gray, and white volumes, respectively, and ventricles increased by 1.4 +/- 0.1 cm3 per year (3.7, 1.3, 2.4, and 1.2 cm3, respectively, in very healthy). Frontal and parietal, compared with temporal and occipital, lobar regions showed greater decline. Gray matter loss was most pronounced for orbital and inferior frontal, cingulate, insular, inferior parietal, and to a lesser extent mesial temporal regions, whereas white matter changes were widespread. In this first study of gray and white matter volume changes, we demonstrate significant longitudinal tissue loss for both gray and white matter even in very healthy older adults. These data provide essential information on the rate and regional pattern of age-associated changes against which pathology can be evaluated and suggest slower rates of brain atrophy in individuals who remain medically and cognitively healthy.

Age Factors↗
PubMed2003

Spatial normalization of diffusion tensor fields.

A method for the spatial normalization and reorientation of diffusion tensor (DT) fields is presented. Spatial normalization of tensor fields requires an appropriate reorientation of the tensor on each voxel, in addition to its relocation into the standardized space. This appropriate tensor reorientation is determined from the spatial normalization transformation and from an estimate of the underlying fiber direction. The latter is obtained by treating the principal eigenvectors of the tensor field around each voxel as random samples drawn from the probability distribution that represents the direction of the underlying fiber. This approach was applied to DT images from nine normal volunteers, and the results show a significant improvement in signal-to-noise ratio (SNR) after spatial normalization and averaging of tensor fields across individuals. The statistics of the spatially normalized tensor field, which represents the tensor characteristics of normal individuals, may be useful for quantitatively characterizing individual variations of white matter structures revealed by DT imaging (DTI) and deviations caused by pathology. Simulated experiments using this methodology are also described.

Algorithms↗
PubMed2003

Very high-resolution morphometry using mass-preserving deformations and HAMMER elastic registration.

This article presents a very high-resolution voxel-based morphometric method, by using a mass-preserving deformation mechanism and a fully automated spatial normalization approach, referred to as HAMMER. By using a hierarchical attribute-based deformation strategy, HAMMER partly overcomes limitations of several existing spatial normalization methods, and it achieves a level of accuracy that makes possible morphometric measurements of spatial specificity close to the voxel dimensions. The proposed method is validated by a series of experiments, with both simulated and real brain images.

Adult↗
PubMed2003

Computer-assisted imaging to assess brain structure in healthy and diseased brains.

Neuroanatomical structures may be profoundly or subtly affected by the interplay of genetic and environmental factors, age, and disease. Such effects are particularly true in healthy ageing individuals and in those who have neurodegenerative diseases. The ability to use imaging to identify structural brain changes associated with different neurodegenerative disease states would be useful for diagnosis and treatment. However, early in the progression of such diseases, neuroanatomical changes may be too mild, diffuse, or topologically complex to be detected by simple visual inspection or manually traced measurements of regions of interest. Computerised methods are being developed that can capture the extraordinary morphological variability of the human brain. These methods use mathematical models sensitive to subtle changes in the size, position, shape, and tissue characteristics of brain structures affected by neurodegenerative diseases. Neuroanatomical features can be compared within and between groups of individuals, taking into account age, sex, genetic background, and disease state, to assess the structural basis of normality and disease. In this review, we describe the strengths and limitations of algorithms of existing computer-assisted tools at the most advanced stage of development, together with available and foreseeable evidence of their usefulness at the clinical and research level.

Algorithms↗
PubMed2003

Segmentation of prostate boundaries from ultrasound images using statistical shape model.

This paper presents a statistical shape model for the automatic prostate segmentation in transrectal ultrasound images. A Gabor filter bank is first used to characterize the prostate boundaries in ultrasound images in both multiple scales and multiple orientations. The Gabor features are further reconstructed to be invariant to the rotation of the ultrasound probe and incorporated in the prostate model as image attributes for guiding the deformable segmentation. A hierarchical deformation strategy is then employed, in which the model adaptively focuses on the similarity of different Gabor features at different deformation stages using a multiresolution technique, i.e., coarse features first and fine features later. A number of successful experiments validate the algorithm.

Algorithms↗
PubMed2003

Hierarchical active shape models, using the wavelet transform.

Active shape models (ASMs) are often limited by the inability of relatively few eigenvectors to capture the full range of biological shape variability. This paper presents a method that overcomes this limitation, by using a hierarchical formulation of active shape models, using the wavelet transform. The statistical properties of the wavelet transform of a deformable contour are analyzed via principal component analysis, and used as priors in the contour's deformation. Some of these priors reflect relatively global shape characteristics of the object boundaries, whereas, some of them capture local and high-frequency shape characteristics and, thus, serve as local smoothness constraints. This formulation achieves two objectives. First, it is robust when only a limited number of training samples is available. Second, by using local statistics as smoothness constraints, it eliminates the need for adopting ad hoc physical models, such as elasticity or other smoothness models, which do not necessarily reflect true biological variability. Examples on magnetic resonance images of the corpus callosum and hand contours demonstrate that good and fully automated segmentations can be achieved, even with as few as five training samples.

Algorithms↗
PubMed2003

Topology preservation and regularity in estimated deformation fields.

A general formalism to impose topology preserving regularity on a given irregular deformation field is presented. The topology preservation conditions are derived with regard to the discrete approximations to the deformation field Jacobian in a two-dimensional image registration problem. The problem of enforcing topology preservation onto a given deformation field is formulated in terms of the deformation gradients, and solved using a cyclic projections approach. The generalization of the developed algorithm leads to a deformation field regularity control by limiting the per voxel volumetric change within a prescribed interval. Extension of the topology preservation conditions onto a three-dimensional registration problem is also presented, together with a comparative analysis of the proposed algorithm with respect to a Gaussian regularizer that enforces the same topology preservation conditions.

Algorithms↗