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W P Segars,
Jason Bond,
Jack Frush,
Sylvia Hon,
Chris Eckersley,
Cameron H Williams,
Jianqiao Feng,
Daniel J Tward,
J T Ratnanather, M I Miller,
D Frush,
E Samei
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ABSTRACT: Purpose: The authors previously developed the 4D extended cardiac-torso (XCAT) phantom for multimodality imaging research. The XCAT consisted of highly detailed whole-body models for the standard male and female adult, including the cardiac and respiratory motions. In this work, the authors extend the XCAT beyond these reference anatomies by developing a series of anatomically variable 4D XCAT adult phantoms for imaging research, the first library of 4D computational phantoms.Methods: The initial anatomy of each phantom was based on chest-abdomen-pelvis computed tomography data from normal patients obtained from the Duke University database. The major organs and structures for each phantom were segmented from the corresponding data and defined using nonuniform rational B-spline surfaces. To complete the body, the authors manually added on the head, arms, and legs using the original XCAT adult male and female anatomies. The structures were scaled to best match the age and anatomy of the patient. A multichannel large deformation diffeomorphic metric mapping algorithm was then used to calculate the transform from the template XCAT phantom (male or female) to the target patient model. The transform was applied to the template XCAT to fill in any unsegmented structures within the target phantom and to implement the 4D cardiac and respiratory models in the new anatomy. Each new phantom was refined by checking for anatomical accuracy via inspection of the models.Results: Using these methods, the authors created a series of computerized phantoms with thousands of anatomical structures and modeling cardiac and respiratory motions. The database consists of 58 (35 male and 23 female) anatomically variable phantoms in total. Like the original XCAT, these phantoms can be combined with existing simulation packages to simulate realistic imaging data. Each new phantom contains parameterized models for the anatomy and the cardiac and respiratory motions and can, therefore, serve as a jumping point from which to create an unlimited number of 3D and 4D variations for imaging research.Conclusions: A population of phantoms that includes a range of anatomical variations representative of the public at large is needed to more closely mimic a clinical study or trial. The series of anatomically variable phantoms developed in this work provide a valuable resource for investigating 3D and 4D imaging devices and the effects of anatomy and motion in imaging. Combined with Monte Carlo simulation programs, the phantoms also provide a valuable tool to investigate patient-specific dose and image quality, and optimization for adults undergoing imaging procedures.
Medical Physics 04/2013; 40(4):043701. · 2.83 Impact Factor
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ABSTRACT: Shape differences are manifested in cortical structures due to
neuropsychiatric disorders. Such differences can be measured by labeled
cortical distance mapping (LCDM) which characterizes the morphometry of the
laminar cortical mantle of cortical structures. LCDM data consist of signed
distances of gray matter (GM) voxels with respect to GM/white matter (WM)
surface. Volumes and descriptive measures (such as means and variances) for
each subject and the pooled distances provide the morphometric differences
between diagnostic groups, but they do not reveal all the morphometric
information contained in LCDM distances. To extract more information from LCDM
data, censoring of the distances is introduced. For censoring of LCDM
distances, the range of LCDM distances is partitioned at a fixed increment
size; and at each censoring step, and distances not exceeding the censoring
distance are kept. Censored LCDM distances inherit the advantages of the pooled
distances. Furthermore, the analysis of censored distances provides information
about the location of morphometric differences which cannot be obtained from
the pooled distances. However, at each step, the censored distances aggregate,
which might confound the results. The influence of data aggregation is
investigated with an extensive Monte Carlo simulation analysis and it is
demonstrated that this influence is negligible. As an illustrative example, GM
of ventral medial prefrontal cortices (VMPFCs) of subjects with major
depressive disorder (MDD), subjects at high risk (HR) of MDD, and healthy
control (Ctrl) subjects are used. A significant reduction in laminar thickness
of the VMPFC and perhaps shrinkage in MDD and HR subjects is observed when
compared to Ctrl subjects. The methodology is also applicable to LCDM-based
morphometric measures of other cortical structures affected by disease.
01/2013;
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ABSTRACT: We present a new diffeomorphic surface mapping algorithm under the framework of large deformation diffeomorphic metric mapping (LDDMM). Unlike existing LDDMM approaches, this new algorithm reduces the complexity of the estimation of diffeomorphic transformations by incorporating a shape prior in which a nonlinear diffeomorphic shape space is represented by a linear space of initial momenta of diffeomorphic geodesic flows from a fixed template. In addition, for the first time, the diffeomorphic mapping is formulated within a decision-theoretic scheme based on Bayesian modeling in which an empirical shape prior is characterized by a low dimensional Gaussian distribution on initial momentum. This is achieved using principal component analysis (PCA) to construct the eigenspace of the initial momentum. A likelihood function is formulated as the conditional probability of observing surfaces given any particular value of the initial momentum, which is modeled as a random field of vector-valued measures characterizing the geometry of surfaces. We define the diffeomorphic mapping as a problem that maximizes a posterior distribution of the initial momentum given observable surfaces over the eigenspace of the initial momentum. We demonstrate the stability of the initial momentum eigenspace when altering training samples using a bootstrapping method. We then validate the mapping accuracy and show robustness to outliers whose shape variation is not incorporated into the shape prior.
IEEE Transactions on Medical Imaging 03/2012; · 3.64 Impact Factor
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ABSTRACT: We present a new diffeomorphic surface mapping algorithm under the framework of large deformation diffeomorphic metric mapping (LDDMM). Unlike existing LDDMM approaches, this new algorithm reduces the complexity of the estimation of diffeomorphic transformations by incorporating a shape prior in which a nonlinear diffeomorphic shape space is represented by a linear space of initial momenta of diffeomorphic geodesic flows from a fixed template. In addition, for the first time, the diffeomorphic mapping is formulated within a decision-theoretic scheme based on Bayesian modeling in which an empirical shape prior is characterized by a low dimensional Gaussian distribution on initial momentum. This is achieved using principal component analysis (PCA) to construct the eigenspace of the initial momentum. A likelihood function is formulated as the conditional probability of observing surfaces given any particular value of the initial momentum, which is modeled as a random field of vector-valued measures characterizing the geometry of surfaces. We define the diffeomorphic mapping as a problem that maximizes a posterior distribution of the initial momentum given observable surfaces over the eigenspace of the initial momentum. We demonstrate the stability of the initial momentum eigenspace when altering training samples using a bootstrapping method. We then validate the mapping accuracy and show robustness to outliers whose shape variation is not incorporated into the shape prior.
IEEE transactions on medical imaging. 09/2011; 31(2):302-11.
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ABSTRACT: In this article, we use longitudinal morphometry (shape and size) measures of hippocampus in subjects with mild dementia of Alzheimer type (DAT) and nondemented controls in logistic discrimination. The morphometric measures we use are volume and metric distance measures at baseline and follow-up (two years apart from baseline). Morphometric differences with respect to a template hippocampus were measured by the metric distance obtained from the large deformation diffeomorphic metric mapping (LDDMM) algorithm. LDDMM assigns metric distances on the space of anatomical images, thereby allowing for the direct comparison and quantization of morphometric changes. We also apply principal component analysis (PCA) on volume and metric distance measures to obtain principal components that capture some salient aspect of morphometry. We construct classifiers based on logistic regression to distinguish diseased and healthy hippocampi (hence potentially diagnose the mild form of DAT). We consider logistic classifiers based on volume and metric distance change over time (from baseline to follow-up), on the raw volumes and metric distances, and on principal components from various types of PCA analysis. We provide a detailed comparison of the performance of these classifiers and guidelines for their practical use. Moreover, combining the information conveyed by volume and metric distance measures by PCA can provide a better biomarker for detection of dementia compared to volume, metric distance, or both.
Current Alzheimer research 08/2011; 9(8):972-81. · 4.97 Impact Factor
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ABSTRACT: This work describes a protocol for creating a population of pediatric, patient specific, computational dosimetry phantoms. Pediatric CT data are mapped to a detailed adult template using multi channel (MC) - large deformation diffeomorphic metric mapping (LDDMM) applied to whole body images. Challenges are studied and overcome using 2D LDDMM applied to simulated phantoms. The protocol involves automatic placement of landmarks, landmark based affine and LDDMM registration, followed by 4 applications of MC-LDDMM, with successively decreasing smoothness constraints. Deformed adult organ surfaces typically agree with pediatric organ surfaces within 1-2 voxels (~2.3 mm).
Bioengineering Conference (NEBEC), 2011 IEEE 37th Annual Northeast; 05/2011
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ABSTRACT: Neuropsychiatric disorders have been demonstrated to manifest shape differences in cortical structures. Labeled Cortical Distance Mapping (LCDM) is a powerful tool in quantifying such morphometric differences and characterizes the morphometry of the laminar cortical mantle of cortical structures. Specifically, LCDM data are distances of labeled gray matter (GM) voxels with respect to the gray/white matter cortical surface. Volumes and descriptive measures (such as means and variances for each subject) based on LCDM distances provide descriptive summary information on some of the shape characteristics. However, additional morphometrics are contained in the data and their analysis may provide additional clues to underlying differences in cortical characteristics. To use more of this information, we pool (merge) LCDM distances from subjects in the same group. These pooled distances can help detect morphometric differences between groups, but do not provide information about the locations of such differences in the tissue in question. In this article, we check for the influence of the assumption violations on the analysis of pooled LCDM distances. We demonstrate that the classical parametric tests are robust to the non-normality and within sample dependence of LCDM distances and nonparametric tests are robust to within sample dependence of LCDM distances. We specify the types of alternatives for which the tests are more sensitive. We also show that the pooled LCDM distances provide powerful results for group differences in distribution of LCDM distances. As an illustrative example, we use GM in the ventral medial prefrontal cortex (VMPFC) in subjects with major depressive disorder (MDD), subjects at high risk (HR) of MDD, and healthy subjects. Significant morphometric differences were found in VMPFC due to MDD or being at HR. In particular, the analysis indicated that distances in left and right VMPFCs tend to decrease due to MDD or being at HR, possibly as a result of thinning. The methodology can also be applied to other cortical structures.
Journal of Mathematical Imaging and Vision 05/2011; 40(1):20-35. · 1.39 Impact Factor
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ABSTRACT: In this study, we used multi-detector computed tomographic (MDCT) images of human left ventricles at end-diastole and end-systole to perform quantitative analysis and comparison of heart motion in patients with anterior wall myocardial infarction and ischemic cardiomyopathy (ICM) versus those with global non-ischemic cardiomyopathy (NICM). MDCT ventricular images of 25 patients (13 with ICM) with ejection fraction (EF) <35% were analyzed. We used a novel technique (parallel transport) to translate within subject motion-related end-diastole to end-systole deformation to a common coordinate system without incorporating across-subject variation. We then performed a hypothesis testing on the ventricular motion variation in the global template coordinate. Statistical analysis indicated that there were meaningful ventricular motion differences between ICM and NICM groups. Additionally, patients with ICM demonstrated less wall thickening at ES in the anterior wall where the pathology was located.
Biomedical Imaging: From Nano to Macro, 2009. ISBI '09. IEEE International Symposium on; 08/2009
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ABSTRACT: We create a series of detailed computerized phantoms to estimate patient organ and effective dose in pediatric CT and investigate techniques for efficiently creating patient-specific phantoms based on imaging data. The initial anatomy of each phantom was previously developed based on manual segmentation of pediatric CT data. Each phantom was extended to include a more detailed anatomy based on morphing an existing adult phantom in our laboratory to match the framework (based on segmentation) defined for the target pediatric model. By morphing a template anatomy to match the patient data in the LDDMM framework, it was possible to create a patient specific phantom with many anatomical structures, some not visible in the CT data. The adult models contain thousands of defined structures that were transformed to define them in each pediatric anatomy. The accuracy of this method, under different conditions, was tested using a known voxelized phantom as the target. Errors were measured in terms of a distance map between the predicted organ surfaces and the known ones. We also compared calculated dose measurements to see the effect of different magnitudes of errors in morphing. Despite some variations in organ geometry, dose measurements from morphing predictions were found to agree with those calculated from the voxelized phantom thus demonstrating the feasibility of our methods.
SPIE; 02/2009
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01/2009;
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ABSTRACT: Anatomical shape differences in cortical structures in the brain can be associated with various neuropsychiatric and neuro-developmental diseases or disorders. Labeled Cortical Distance Map (LCDM), can be a powerful tool to quantize such morphometric differences. In this article, we investigate various issues regarding the analysis of LCDM distances in relation to morphometry. The length of the LCDM distance vector provides the number of voxels (approximately a multiple of volume (in mm^3)); median, mode, range, and variance of LCDM distances are all suggestive of size, thickness, and shape differences. However these measures provide a crude summary based on LCDM distances which may convey much more information about the tissue in question. To utilize more of this information, we pool (merge) the LCDM distances from subjects in the same group or condition. The statistical methodology we employ require normality and within and between sample independence. We demonstrate that the violation of these assumptions have mild influence on the tests. We specify the types of alternatives the parametric and nonparametric tests are more sensitive for. We also show that the pooled LCDM distances provide powerful results for group differences in distribution, left-right morphometric asymmetry of the tissues, and variation of LCDM distances. As an illustrative example, we use gray matter (GM) tissue of ventral medial prefrontal cortices (VMPFCs) from subjects with major depressive disorder, subjects at high risk, and control subjects. We find significant evidence that VMPFCs of subjects with depressive disorders are different in shape compared to those of normal subjects.
06/2008;
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ABSTRACT: This paper describes the application of a novel segmentation method in high resolution MRI subvolumes containing hippocampus in five subjects and occipital lobe in five subjects. The alternating kernel mixture (AKM) algorithm is used to segment the MRI subvolumes into cerebrospinal fluid, gray matter, and white matter. The segmentation is validated by comparison with manual segmentation. The misclassification errors are 0.10-0.17 (n=10). When compared with Bayesian segmentation method, AKM yields smaller errors. By generating multiple mixtures for each tissue compartment, AKM mimics the increasing variance in the manual segmentation in partial volumes between the highly folded tissues. AKM's superior performance makes it useful for automated segmentation of sub-cortical and cortical structures in neuro-imaging studies.
Frontiers in the Convergence of Bioscience and Information Technologies, 2007. FBIT 2007; 11/2007
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ABSTRACT: By analyzing interpoint comparisons, we obtain significant results describing the relationship in "hippocampus shape-space" of clinically depressed, high risk, and control populations. In particular, our analysis demonstrates that the high risk population is closer in shape-space to the control population than to the clinically depressed population.
Frontiers in the Convergence of Bioscience and Information Technologies, 2007. FBIT 2007; 11/2007
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ABSTRACT: Shape differences in cortical structures in the brain can be associated with various neuropsychiatric and neuro-developmental diseases or disorders. Labeled Cortical Distance Map (LCDM) can be a powerful tool to quantize such differences in shapes derived from magnetic resonance images (MRI). This article investigates some aspects of LCDM distances in relation to morphometry. Simple morphometric measures based on LCDM indicate some aspect of the shape or size of the tissue in question. The length of the LCDM distance vector provides the number of voxels and thus volume of the tissue. The median, mode, range, and variance of LCDM distances and volume of the tissue are all suggestive of size, thickness, and shape differences. Statistical tests are employed to detect left-right asymmetry, group differences, and stochastic ordering (cdf differences) of these LCDM-based variables. We perform LCDM analysis of gray matter in ventral medial prefrontal cortices (IMPFCs) obtained from a neuro-imaging study of major depressive disorder (MDD), high risk, and control twin subjects. We find significant evidence that IMPFCs with MDD exhibit significant morphometric left-right asymmetry compared to those in high risk and control subjects. The method is also valid for analysis of morphometric measures of other organs or tissues and distances similar to LCDM distances.
Image and Signal Processing and Analysis, 2007. ISPA 2007. 5th International Symposium on; 10/2007
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ABSTRACT: In large-deformation diffeomorphic metric mapping (LDDMM), the diffeomorphic matching of images are modeled as evolution in time, or a flow, of an associated smooth velocity vector field v controlling the evolution. The initial momentum parameterizes the whole geodesic and encodes the shape and form of the target image. Thus, methods such as principal component analysis (PCA) of the initial momentum leads to analysis of anatomical shape and form in target images without being restricted to small-deformation assumption in the analysis of linear displacements. We apply this approach to a study of dementia of the Alzheimer type (DAT). The left hippocampus in the DAT group shows significant shape abnormality while the right hippocampus shows similar pattern of abnormality. Further, PCA of the initial momentum leads to correct classification of 12 out of 18 DAT subjects and 22 out of 26 control subjects
IEEE Transactions on Medical Imaging 05/2007; · 3.64 Impact Factor
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ABSTRACT: This paper proposes a method to match diffusion tensor magnetic resonance images (DT-MRI) through the large deformation diffeomorphic metric mapping of tensor fields on the image volume, resulting in optimizing for geodesics on the space of diffeomorphisms connecting two diffusion tensor images. A coarse to fine multi-resolution and multikernel- width scheme is detailed, to reduce both ambiguities and computation load. This is illustrated by numerical experiments on DT-MRI brain and images.
Computer Vision and Pattern Recognition Workshop, 2006. CVPRW '06. Conference on; 07/2006
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ABSTRACT: This paper presents an approach for automatically delineating the borders of human primary visual cortex and finding ridges of maximal response due to static phase-encoding stimuli on fMRI t-statistical maps via dynamic programming. The sensitivity of such an approach to the choice of initial starting and ending points and the identification of the ridge path over a wide response region are addressed. Moreover, retinotopic maps for left and right visual cortex are shown in a population of two normal subjects
Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the; 02/2006
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ABSTRACT: This paper proposes a method to match diffusion tensor magnetic resonance images (DT-MRI) through the large deformation diffeomorphic metric mapping of vector fields, focusing on the fiber orientations, considered as unit vector fields on the image volume. We study a suitable action of diffeomorphisms on such vector fields, and provide an extension of the large deformation diffeomorphic metric-mapping framework to this type of dataset, resulting in optimizing for geodesies on the space of diffeomorphisms connecting two images. Two different distance function of vector fields are considered. Existence of the minimizers under smoothness assumptions on the compared vector fields is proved, and coarse to fine hierarchical strategies are detailed, to reduce both ambiguities and computation load. This is illustrated by numerical experiments on DT-MRI heart and brain images.
Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on; 11/2005
