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The Journal of Visualized Experiments (JoVE) is a peer reviewed, PubMed-indexed video journal. Our mission is to increase the productivity of scientific research.

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Articles by Xenophon Papademetris in JoVE

 JoVE Clinical and Translational Medicine

Em tempo real Biofeedback fMRI Segmentação o córtex orbitofrontal de Ansiedade Contaminação


JoVE 3535 1/20/2012

1, 1, 2, 1, 1, 1, 2,3,4, 1, 1

1Department of Diagnostic Radiology, Yale University School of Medicine, 2Department of Psychiatry, Yale University School of Medicine, 3Yale Child Study Center, Yale University School of Medicine, 4Interdepartmental Neuroscience Program, Yale University School of Medicine

Aqui apresentamos um método para a formação de pessoas para controlar uma área do cérebro envolvida na ansiedade de contaminação e para sondar a relação entre a ansiedade de contaminação e os padrões de conectividade do cérebro.

Other articles by Xenophon Papademetris on PubMed

Integrated Intensity and Point-Feature Nonrigid Registration

Medical Image Computing and Computer-assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Sep, 2001  |  Pubmed ID: 20473359

In this work, we present a method for the integration of feature and intensity information for non rigid registration. Our method is based on a free-form deformation model, and uses a normalized mutual information intensity similarity metric to match intensities and the robust point matching framework to estimate feature (point) correspondences. The intensity and feature components of the registration are posed in a single energy functional with associated weights. We compare our method to both point-based and intensity-based registrations. In particular, we evaluate registration accuracy as measured by point landmark distances and image intensity similarity on a set of seventeen normal subjects. These results suggest that the integration of intensity and point-based registration is highly effective in yielding more accurate registrations.

Estimation of 3-D Left Ventricular Deformation from Medical Images Using Biomechanical Models

IEEE Transactions on Medical Imaging. Jul, 2002  |  Pubmed ID: 12374316

The quantitative estimation of regional cardiac deformation from three-dimensional (3-D) image sequences has important clinical implications for the assessment of viability in the heart wall. We present here a generic methodology for estimating soft tissue deformation which integrates image-derived information with biomechanical models, and apply it to the problem of cardiac deformation estimation. The method is image modality independent. The images are segmented interactively and then initial correspondence is established using a shape-tracking approach. A dense motion field is then estimated using a transversely isotropic, linear-elastic model, which accounts for the muscle fiber directions in the left ventricle. The dense motion field is in turn used to calculate the deformation of the heart wall in terms of strain in cardiac specific directions. The strains obtained using this approach in open-chest dogs before and after coronary occlusion, exhibit a high correlation with strains produced in the same animals using implanted markers. Further, they show good agreement with previously published results in the literature. This proposed method provides quantitative regional 3-D estimates of heart deformation.

Geometric Strategies for Neuroanatomic Analysis from MRI

NeuroImage. 2004  |  Pubmed ID: 15501099

In this paper, we describe ongoing work in the Image Processing and Analysis Group (IPAG) at Yale University specifically aimed at the analysis of structural information as represented within magnetic resonance images (MRI) of the human brain. Specifically, we will describe our applied mathematical approaches to the segmentation of cortical and subcortical structure, the analysis of white matter fiber tracks using diffusion tensor imaging (DTI), and the intersubject registration of neuroanatomical (aMRI) data sets. Many of our methods rally around the use of geometric constraints, statistical (MAP) estimation, and the use of level set evolution strategies. The analysis of gray matter structure and connecting white matter paths combined with the ability to bring all information into a common space via intersubject registration should provide us with a rich set of data to investigate structure and variation in the human brain in neuropsychiatric disorders, as well as provide a basis for current work in the development of integrated brain function-structure analysis.

Functional Brain Image Analysis Using Joint Function-Structure Priors

Medical Image Computing and Computer-assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Jan, 2004  |  Pubmed ID: 20543899

We propose a new method for context-driven analysis of functional magnetic resonance images (fMRI) that incorporates spatial relationships between functional parameter clusters and anatomical structure directly for the first time. We design a parametric scheme that relates functional and structural spatially-compact regions in a single unified manner. Our method is motivated by the fact that the fMRI and anatomical MRI (aMRI) have consistent relations that provide configurations and context that aid in fMRI analysis. We develop a statistical decision-making strategy to estimate new fMRI parameter images (based on a General Linear Model-GLM) and spatially-clustered zones within these images. The analysis is based on the time-series data and contextual information related to appropriate spatial grouping of parameters in the functional data and the relationship of this grouping to relevant gray matter structure from the anatomical data. We introduce a representation for the joint prior of the functional and structural information, and define a joint probability distribution over the variations of functional clusters and the related structure contained in a set of training images. We estimate the Maximum A Posteriori (MAP) functional parameters, formulating the function-structure model in terms of level set functions. Results from 3D fMRI and aMRI show that this context-driven analysis potentially extracts more meaningful information than the standard GLM approach.

The "obese Insulin-sensitive" Adolescent: Importance of Adiponectin and Lipid Partitioning

The Journal of Clinical Endocrinology and Metabolism. Jun, 2005  |  Pubmed ID: 15797955

There is a wide interindividual variation in peripheral insulin sensitivity at any given body mass index or percent body fat among obese adolescents with normal glucose tolerance. The goals of this study were to determine whether variability in insulin sensitivity is associated with differences in patterns of lipid partitioning or substrate use under fasting and hyperinsulinemic conditions. We compared 14 obese insulin-resistant adolescents with 14 obese insulin-sensitive controls, pair matched for age, gender, pubertal stage and body composition. Insulin sensitivity was assessed by the hyperinsulinemic-euglycemic clamp, intramyocellular lipid content by (1)H-nuclear magnetic resonance and visceral fat by magnetic resonance imaging. Obese insulin-sensitive subjects had lower intramyocellular (1.64 +/- 0.68 vs.2.26 +/- 0.62% of water peak, P = 0.017) and visceral lipid deposition (45 +/- 23 vs. 77 +/- 52 cm(2), P = 0.04) and a higher level of adiponectin, compared with their obese-resistant counterparts (8.8 +/- 3.6 vs. 6.5 +/- 1.8 mug/dl, P = 0.015). Glycerol fluxes were similar between the two obese groups yet occurred in the face of different concentrations of insulin. Intramyocellular lipid and visceral fat were negatively related to insulin sensitivity. Obese insulin-sensitive adolescents are characterized by lower lipid deposition in the intramyocellular and visceral compartments and greater levels of adiponectin, despite similar degree of adiposity.

Preliminary Evidence for Persistent Abnormalities in Amygdala Volumes in Adolescents and Young Adults with Bipolar Disorder

Bipolar Disorders. Dec, 2005  |  Pubmed ID: 16403182

Abnormalities in volumes of the amygdala have been reported previously in adolescents and adults with bipolar disorder (BD). Several studies have reported reduced volumes in adolescents with BD; however, both decreases and increases in volumes have been reported in adults with BD. Understanding of potential developmental contributions to these disturbances in morphology of the amygdala has been limited by the absence of longitudinal data in persons with BD. Here we use a within-subject longitudinal design to investigate whether amygdala volume abnormalities persist in adolescents and young adults with BD over a time interval of approximately 2 years.

Characterizing Vascular Connectivity from MicroCT Images

Medical Image Computing and Computer-assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2005  |  Pubmed ID: 16686021

X-ray microCT (computed tomography) has become a valuable tool in the analysis of vascular architecture in small animals. Because of its high resolution, a detailed assessment of blood vessel physiology and pathology is possible. Vascular measurement from noninvasive imaging is important for the study and quantification of vessel disease and can aid in diagnosis, as well as measure disease progression and response to therapy. The analysis of tracked vessel trajectories enables the derivation of vessel connectivity information, lengths between vessel junctions as well as level of ramification, contributing to a quantitative analysis of vessel architecture. In this paper, we introduce a new vessel tracking methodology based on wave propagation in oriented domains. Vessel orientation and vessel likelihood are estimated based on an eigenanalysis of gray-level Hessian matrices computed at multiple scales. An anisotropic wavefront then propagates through this vector field with a speed modulated by the maximum vesselness response at each location. Putative vessel trajectories can be found by tracing the characteristics of the propagation solution between different points. We present preliminary results from both synthetic and mouse microCT image data.

Articulated Rigid Registration for Serial Lower-limb Mouse Imaging

Medical Image Computing and Computer-assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2005  |  Pubmed ID: 16686048

This paper describes a new piecewise rotational transformation model for capturing the articulation of joints such as the hip and the knee. While a simple piecewise rigid model can be applied, such models suffer from discontinuities at the motion boundary leading to both folding and stretching. Our model avoids both of these problems by constructing a provably continuous transformation along the motion interface. We embed this transformation model within the robust point matching framework and demonstrate its successful application to both synthetic data, and to serial x-ray CT mouse images. In the later case, our model captures the articulation of six joints, namely the left/right hip, the left/right knee and the left/right ankle. In the future such a model could be used to initialize non-rigid registrations of images from different subjects, as well as, be embedded in intensity-based and integrated registration algorithms. It could also be applied to human data in cases where articulated motion is an issue (e.g. image guided prostate radiotherapy, lower extremity CT angiography).

Brain Tissue Segmentation Based on Corrected Gray-scale Analysis

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. 2005  |  Pubmed ID: 17282881

Image signal-to-noise ratio (SNR) and signal intensity (SI) inhomogeneities are factors that strongly affect the accuracy and precision of brain tissue segmentations in magnetic resonance image (MRI). In this work, SNR and contrast of brain images are optimized by TR and inversion recovery time TI in multi-spectrum MRI data sets. SI inhomogeneities are measured in vivo using a recently developed method allowing improved correction. The three-Gaussain distribution model is used to fit histograms of the images to find the initialization parameters for an Expectation-Maximization (EM) segmentation algorithm. Compared with other methods, the field map method provides better correction of SI inhomogeneities and excellent segmentation results.

Regional Whole Body Fat Quantification in Mice

Information Processing in Medical Imaging : Proceedings of the ... Conference. 2005  |  Pubmed ID: 17354710

Obesity has risen to epidemic levels in the United States and around the world. Global indices of obesity such as the body mass index (BMI) have been known to be inaccurate predictors of risk of diabetes, and it is commonly recognized that the distribution of fat in the body is a key measure. In this work, we describe the early development of image analysis methods to quantify regional body fat distribution in groups of both male and female wildtype mice using magnetic resonance images. In particular, we present a new formulation which extends the expectation-maximization formalism commonly applied in brain segmentation to multi-exponential data and applies it to the problem of regional whole body fat quantification. Previous segmentation approaches for multispectral data typically perform the classification on fitted parameters, such as the density and the relaxation times. In contrast, our method directly computes a likelihood term from the raw data and hence explicitly accounts for errors in the fitting process, while still using the fitted parameters to model the variation in the appearance of each tissue class. Early validation results, using magnetic resonance spectroscopic imaging as a gold standard, are encouraging. We also present results demonstrating differences in fat distribution between male and female mice.

Nonrigid 3D Brain Registration Using Intensity/feature Information

Medical Image Computing and Computer-assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2006  |  Pubmed ID: 17354980

The brain deforms non-rigidly during neurosurgery, preventing preoperatively acquired images from accurately depicting the intraoperative brain. If the deformed brain surface can be detected, biomechanical models can be applied to calculate the resulting volumetric deformation. The reliability of this volumetric calculation is dependent on the accuracy of the surface detection. This work presents a surface tracking algorithm which relies on Bayesian analysis to track cortical surface movement. The inputs to the model are 3D preoperative brain images and intraoperative stereo camera images. The addition of a camera calibration optimization term creates a more robust model, capable of tracking the cortical surface in the presence of camera calibration error.

Alanine Aminotransferase Levels and Fatty Liver in Childhood Obesity: Associations with Insulin Resistance, Adiponectin, and Visceral Fat

The Journal of Clinical Endocrinology and Metabolism. Nov, 2006  |  Pubmed ID: 16912127

Concurrent with the rise in obesity, nonalcoholic fatty liver disease is recognized as the leading cause of serum aminotransferase elevations in obese youth. Nevertheless, the complete metabolic phenotype associated with abnormalities in biomarkers of liver injury and intrahepatic fat accumulation remains to be established.

Rapid Calculations of Susceptibility-induced Magnetostatic Field Perturbations for in Vivo Magnetic Resonance

Physics in Medicine and Biology. Dec, 2006  |  Pubmed ID: 17148824

Static magnetic field perturbations generated by variations of magnetic susceptibility within samples reduce the quality and integrity of magnetic resonance measurements. These perturbations are difficult to predict in vivo where wide variations of internal magnetic susceptibility distributions are common. Recent developments have provided rapid computational means of estimating static field inhomogeneity within the small susceptibility limits of materials typically studied using magnetic resonance. Such a predictive mechanism could be a valuable tool for sequence simulation, field shimming and post-acquisition image correction. Here, we explore this calculation protocol and demonstrate its predictive power in estimating in vivo inhomogeneity within the human brain. Furthermore, we quantitatively explore the predictive limits of the computation. For in vivo comparison, a method of magnetic susceptibility registration using MRI and CT data is presented and utilized to carry out subject-specific inhomogeneity estimation. Using this algorithm, direct comparisons in human brain and phantoms are made between field map acquisitions and calculated inhomogeneity. Distortion correction in echo-planar images due to static field inhomogeneity is also demonstrated using the computed field maps.

Brain Shape Characterization from Deformation

Proceedings / IEEE International Symposium on Biomedical Imaging: from Nano to Macro. IEEE International Symposium on Biomedical Imaging. 2006  |  Pubmed ID: 20216923

The characterization of shape in the brain is of great importance for understanding differences in structure and the relationship to function. Structural differences have been associated with, for example, age, sex, handedness, cognitive abilities and many neurologic and psychiatric conditions. Nonrigid registration methods enable the characterization of shape differences between images based on the transformation that relates them. Unlike methods which characterize shape in terms of geometric features computed from individual structures, transformation-based deformation description characterizes the entire space and therefore may better reflect the interrelationships between structures, as well as changes within and near structure. The transformation, as characterized by the local Jacobian, can yield an expressive description of local shape differences.

Dynamic Imaging of Lymphatic Vessels and Lymph Nodes Using a Bimodal Nanoparticulate Contrast Agent

Lymphatic Research and Biology. 2007  |  Pubmed ID: 18035933

Evaluation of lymphedema and lymph node metastasis in humans has relied primarily on invasive or radioactive modalities. While noninvasive technologies such as magnetic resonance imaging (MRI) offer the potential for true three-dimensional imaging of lymphatic structures, invasive modalities, such as optical fluorescence microscopy, provide higher resolution and clearer delineation of both lymph nodes and lymphatic vessels. Thus, contrast agents that image lymphatic vessels and lymph nodes by both fluorescence and MRI may further enhance our understanding of the structure and function of the lymphatic system. Recent applications of bimodal (fluorescence and MR) contrast agents in mice have not achieved clear visualization of lymphatic vessels and nodes. Here the authors describe the development of a nanoparticulate contrast agent that is taken up by lymphatic vessels to draining lymph nodes and detected by both modalities.

A Comprehensive System for Intraoperative 3D Brain Deformation Recovery

Medical Image Computing and Computer-assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2007  |  Pubmed ID: 18044612

During neurosurgery, brain deformation renders preoperative images unreliable for localizing pathologic structures. In order to visualize the current brain anatomy, it is necessary to nonrigidly warp these preoperative images to reflect the intraoperative brain. This can be accomplished using a biomechanical model driven by sparse intraoperative information. In this paper, a linear elastic model of the brain is developed which can infer volumetric brain deformation given the cortical surface displacement. This model was tested on both a realistic brain phantom and in vivo, proving its ability to account for large brain deformations. Also, an efficient semiautomatic strategy for preoperative cortical feature detection is outlined, since accurate segmentation of cortical features can aid intraoperative cortical surface tracking.

Segmentation of Myocardial Volumes from Real-time 3D Echocardiography Using an Incompressibility Constraint

Medical Image Computing and Computer-assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2007  |  Pubmed ID: 18051042

Real-time three-dimensional (RT3D) echocardiography is a new imaging modality that presents the unique opportunity to visualize the complex three-dimensional (3-D) shape and the motion of left ventricle (LV) in vivo. To take advantage of this opportunity, automatic segmentation of LV myocardium is essential. While there are a variety of efforts on the segmentation of LV endocardial (ENDO) boundaries, the segmentation of epicardial (EPI) boundaries is still problematic. In this paper, we present a new approach of coupled-surfaces propagation to address this problem. Our method is motivated by the idea that the volume of the myocardium is close to being constant during a cardiac cycle and takes this tight coupling as an important constraint. We employ two surfaces, each driven by the image-derived information that takes into account the ultrasound physics by modeling speckle using shifted Rayleigh distribution while maintaining the coupling. By evolving two surfaces simultaneously, the final representation of myocardium is thus achieved. Results from 328 sets of RT3D echocardiographic data are evaluated against the outlines of three observers. We show that the results from automatic segmentation are comparable to those from manual segmentation.

The Role of Skeletal Muscle Insulin Resistance in the Pathogenesis of the Metabolic Syndrome

Proceedings of the National Academy of Sciences of the United States of America. Jul, 2007  |  Pubmed ID: 17640906

We examined the hypothesis that insulin resistance in skeletal muscle promotes the development of atherogenic dyslipidemia, associated with the metabolic syndrome, by altering the distribution pattern of postprandial energy storage. Following ingestion of two high carbohydrate mixed meals, net muscle glycogen synthesis was reduced by approximately 60% in young, lean, insulin-resistant subjects compared with a similar cohort of age-weight-body mass index-activity-matched, insulin-sensitive, control subjects. In contrast, hepatic de novo lipogenesis and hepatic triglyceride synthesis were both increased by >2-fold in the insulin-resistant subjects. These changes were associated with a 60% increase in plasma triglyceride concentrations and an approximately 20% reduction in plasma high-density lipoprotein concentrations but no differences in plasma concentrations of TNF-alpha, IL-6, adiponectin, resistin, retinol binding protein-4, or intraabdominal fat volume. These data demonstrate that insulin resistance in skeletal muscle, due to decreased muscle glycogen synthesis, can promote atherogenic dyslipidemia by changing the pattern of ingested carbohydrate away from skeletal muscle glycogen synthesis into hepatic de novo lipogenesis, resulting in an increase in plasma triglyceride concentrations and a reduction in plasma high-density lipoprotein concentrations. Furthermore, insulin resistance in these subjects was independent of changes in the plasma concentrations of TNF-alpha, IL-6, high-molecular-weight adiponectin, resistin, retinol binding protein-4, or intraabdominal obesity, suggesting that these factors do not play a primary role in causing insulin resistance in the early stages of the metabolic syndrome.

High Visceral and Low Abdominal Subcutaneous Fat Stores in the Obese Adolescent: a Determinant of an Adverse Metabolic Phenotype

Diabetes. Feb, 2008  |  Pubmed ID: 17977954

To explore whether an imbalance between the visceral and subcutaneous fat depots and a corresponding dysregulation of the adipokine milieu is associated with excessive accumulation of fat in the liver and muscle and ultimately with insulin resistance and the metabolic syndrome.

Bayesian Analysis of FMRI Data with ICA Based Spatial Prior

Medical Image Computing and Computer-assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2008  |  Pubmed ID: 18982612

Spatial modeling is essential for fMRI analysis due to relatively high noise in the data. Earlier approaches have been primarily concerned with the spatial coherence of the BOLD response in local neighborhoods. In addition to a smoothness constraint, we propose to incorporate prior knowledge of brain activation patterns learned from training samples. This spatially informed prior can significantly enhance the estimation process by inducing sensitivity to task related regions of the brain. As fMRI data exhibits intersubject variability in functional anatomy, we design the prior using Independent Component Analysis (ICA). Due to the non-Gaussian assumption, ICA does not regress to the mean activation pattern and thus avoids suppressing intersubject differences. Results from a real fMRI experiment indicate that our approach provides statistically significant improvement in estimating activation compared to the standard general linear model (GLM) based methods.

Bidirectional Segmentation of Three-dimensional Cardiac MR Images Using a Subject-specific Dynamical Model

Medical Image Computing and Computer-assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2008  |  Pubmed ID: 18982636

Statistical model-based segmentation of the left ventricles has received considerable attention these years. While many statistical models have been shown to improve segmentation results, most of them either belong to (1) static models (SM) that neglect the temporal coherence of a cardiac sequence, or (2) generic dynamical models (GDM) that neglect the individual differences of cardiac motion. In this paper, we propose a subject-specific dynamical model (SSDM) that can simultaneously handle inter-subject variability and temporal cardiac dynamics (intra-subject variability). We also design a dynamic prediction algorithm that can progressively predict the shape of a new cardiac sequence at a given frame based on the shapes observed in earlier frames. Furthermore, to reduce the accumulation of the segmentation errors throughout the entire sequence, we take into account the periodic nature of cardiac motion and perform bidirectional segmentation from a certain frame in a cardiac sequence. "Leave-one-out" validation on 32 sequences show that our algorithm can capture local shape variations and suppress the propagation of segmentation errors.

Novel Interaction Techniques for Neurosurgical Planning and Stereotactic Navigation

IEEE Transactions on Visualization and Computer Graphics. Nov-Dec, 2008  |  Pubmed ID: 18989014

Neurosurgical planning and image guided neurosurgery require the visualization of multimodal data obtained from various functional and structural image modalities, such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), functional MRI, Single photon emission computed tomography (SPECT) and so on. In the case of epilepsy neurosurgery for example, these images are used to identify brain regions to guide intracranial electrode implantation and resection. Generally, such data is visualized using 2D slices and in some cases using a 3D volume rendering along with the functional imaging results. Visualizing the activation region effectively by still preserving sufficient surrounding brain regions for context is exceedingly important to neurologists and surgeons. We present novel interaction techniques for visualization of multimodal data to facilitate improved exploration and planning for neurosurgery. We extended the line widget from VTK to allow surgeons to control the shape of the region of the brain that they can visually crop away during exploration and surgery. We allow simple spherical, cubical, ellipsoidal and cylindrical (probe aligned cuts) for exploration purposes. In addition we integrate the cropping tool with the image-guided navigation system used for epilepsy neurosurgery. We are currently investigating the use of these new tools in surgical planning and based on further feedback from our neurosurgeons we will integrate them into the setup used for image-guided neurosurgery.

Effective Visualization of Complex Vascular Structures Using a Non-parametric Vessel Detection Method

IEEE Transactions on Visualization and Computer Graphics. Nov-Dec, 2008  |  Pubmed ID: 18989016

The effective visualization of vascular structures is critical for diagnosis, surgical planning as well as treatment evaluation. In recent work, we have developed an algorithm for vessel detection that examines the intensity profile around each voxel in an angiographic image and determines the likelihood that any given voxel belongs to a vessel; we term this the "vesselness coefficient" of the voxel. Our results show that our algorithm works particularly well for visualizing branch points in vessels. Compared to standard Hessian based techniques, which are fine-tuned to identify long cylindrical structures, our technique identifies branches and connections with other vessels. Using our computed vesselness coefficient, we explore a set of techniques for visualizing vasculature. Visualizing vessels is particularly challenging because not only is their position in space important for clinicians but it is also important to be able to resolve their spatial relationship. We applied visualization techniques that provide shape cues as well as depth cues to allow the viewer to differentiate between vessels that are closer from those that are farther. We use our computed vesselness coefficient to effectively visualize vasculature in both clinical neurovascular x-ray computed tomography based angiography images, as well as images from three different animal studies. We conducted a formal user evaluation of our visualization techniques with the help of radiologists, surgeons, and other expert users. Results indicate that experts preferred distance color blending and tone shading for conveying depth over standard visualization techniques.

Effects of Working Memory Load on Oscillatory Power in Human Intracranial EEG

Cerebral Cortex (New York, N.Y. : 1991). Aug, 2008  |  Pubmed ID: 18056698

Studies of working memory load effects on human EEG power have indicated divergent effects in different frequency bands. Although gamma power typically increases with load, the load dependency of the lower frequency theta and alpha bands is uncertain. We obtained intracranial electroencephalography measurements from 1453 electrode sites in 14 epilepsy patients performing a Sternberg task, in order to characterize the anatomical distribution of load-related changes across the frequency spectrum. Gamma power increases occurred throughout the brain, but were most common in the occipital lobe. In the theta and alpha bands, both increases and decreases were observed, but with different anatomical distributions. Increases in theta and alpha power were most prevalent in frontal midline cortex. Decreases were most commonly observed in occipital cortex, colocalized with increases in the gamma range, but were also detected in lateral frontal and parietal regions. Spatial overlap with group functional magnetic resonance imaging results was minimal except in the precentral gyrus. These findings suggest that power in any given frequency band is not a unitary phenomenon; rather, reactivity in the same frequency band varies in different brain regions, and may relate to the engagement or inhibition of a given area in a cognitive task.

More Accurate Talairach Coordinates for Neuroimaging Using Non-linear Registration

NeuroImage. Aug, 2008  |  Pubmed ID: 18572418

While the Talairach atlas remains the most commonly used system for reporting coordinates in neuroimaging studies, the absence of an actual 3-D image of the original brain used in its construction has severely limited the ability of researchers to automatically map locations from 3-D anatomical MRI images to the atlas. Previous work in this area attempted to circumvent this problem by constructing approximate linear and piecewise-linear mappings between standard brain templates (e.g. the MNI template) and Talairach space. These methods are limited in that they can only account for differences in overall brain size and orientation but cannot correct for the actual shape differences between the MNI template and the Talairach brain. In this paper we describe our work to digitize the Talairach atlas and generate a non-linear mapping between the Talairach atlas and the MNI template that attempts to compensate for the actual differences in shape between the two, resulting in more accurate coordinate transformations. We present examples in this paper and note that the method is available freely online as a Java applet.

Abnormal Corpus Callosum Integrity in Bipolar Disorder: a Diffusion Tensor Imaging Study

Biological Psychiatry. Oct, 2008  |  Pubmed ID: 18620337

Abnormalities in the anterior interhemispheric connections provided by the corpus callosum (CC) have long been implicated in bipolar disorder (BD). In this study, we used complementary diffusion tensor imaging methods to study the structural integrity of the CC and localization of potential abnormalities in BD.

Abnormal Anterior Cingulum Integrity in Bipolar Disorder Determined Through Diffusion Tensor Imaging

The British Journal of Psychiatry : the Journal of Mental Science. Aug, 2008  |  Pubmed ID: 18669996

Convergent evidence implicates white matter abnormalities in bipolar disorder. The cingulum is an important candidate structure for study in bipolar disorder as it provides substantial white matter connections within the corticolimbic neural system that subserves emotional regulation involved in the disorder.

A Non-parametric Vessel Detection Method for Complex Vascular Structures

Medical Image Analysis. Feb, 2009  |  Pubmed ID: 18678521

Modern medical imaging techniques enable the acquisition of in vivo high resolution images of the vascular system. Most common methods for the detection of vessels in these images, such as multiscale Hessian-based operators and matched filters, rely on the assumption that at each voxel there is a single cylinder. Such an assumption is clearly violated at the multitude of branching points that are easily observed in all, but the most focused vascular image studies. In this paper, we propose a novel method for detecting vessels in medical images that relaxes this single cylinder assumption. We directly exploit local neighborhood intensities and extract characteristics of the local intensity profile (in a spherical polar coordinate system) which we term as the polar neighborhood intensity profile. We present a new method to capture the common properties shared by polar neighborhood intensity profiles for all the types of vascular points belonging to the vascular system. The new method enables us to detect vessels even near complex extreme points, including branching points. Our method demonstrates improved performance over standard methods on both 2D synthetic images and 3D animal and clinical vascular images, particularly close to vessel branching regions.

Effects of the Brain-derived Neurotrophic Growth Factor Val66met Variation on Hippocampus Morphology in Bipolar Disorder

Neuropsychopharmacology : Official Publication of the American College of Neuropsychopharmacology. Mar, 2009  |  Pubmed ID: 18704093

Histological and behavioral research in bipolar disorder (BD) implicates structural abnormalities in the hippocampus. Brain-derived neurotrophic growth factor (BDNF) protein is associated with hippocampal development and plasticity, and in mood disorder pathophysiology. We tested the hypotheses that both the BDNF val66met polymorphism and BD diagnosis are associated with decreased hippocampus volume, and that individuals with BD who carry the met allele have the smallest hippocampus volumes compared to individuals without BD and val/val homozygotes. We further explored localization of morphological differences within hippocampus in BD associated with the met allele. Twenty individuals with BD and 18 healthy comparison (HC) subjects participated in high-resolution magnetic resonance imaging scans from which hippocampus volumes were defined and measured. We used linear mixed model analysis to study effects of diagnosis and BDNF genotype on hippocampus volumes. We then employed three-dimensional mapping to localize areas of change within the hippocampus associated with the BDNF met allele in BD. We found that hippocampus volumes were significantly smaller in BD compared to HC subjects, and presence of the BDNF met allele was associated with smaller hippocampus volume in both diagnostic groups. The BD subgroup who carried the BDNF met allele had the smallest hippocampus volumes, and three-dimensional mapping identified these decreases as most prominent in left anterior hippocampus. These results support effects of BD diagnosis and BDNF genotype on hippocampus structure and suggest a genetic subgroup within BD who may be most vulnerable to deficits in hippocampus and may most benefit from interventions that influence BDNF-mediated signaling.

From Medical Image Computing to Computer-aided Intervention: Development of a Research Interface for Image-guided Navigation

The International Journal of Medical Robotics + Computer Assisted Surgery : MRCAS. Jun, 2009  |  Pubmed ID: 19301361

This paper describes the development and application of a research interface to integrate research image analysis software with a commercial image-guided surgery navigation system. This interface enables bi-directional transfer of data such as images, visualizations and tool positions in real time.

Preliminary Evidence for Progressive Prefrontal Abnormalities in Adolescents and Young Adults with Bipolar Disorder

Journal of the International Neuropsychological Society : JINS. May, 2009  |  Pubmed ID: 19402934

Previous cross-sectional study of ventral prefrontal cortex (VPFC) implicated progressive volume abnormalities during adolescence in bipolar disorder (BD). In the present study, a within-subject, longitudinal design was implemented to examine brain volume changes during adolescence/young adulthood. We hypothesized that VPFC volume decreases over time would be greater in adolescents/young adults with BD than in healthy comparison adolescents/young adults. Eighteen adolescents/young adults (10 with BD I and 8 healthy comparison participants) underwent two high-resolution magnetic resonance imaging scans over approximately 2 years. Regional volume changes over time were measured. Adolescents/young adults with BD displayed significantly greater volume loss over time, compared to healthy comparison participants, in a region encompassing VPFC and rostral PFC and extending to rostral anterior cingulate cortex (p < .05). Additional areas where volume change differed between groups were observed. While data should be interpreted cautiously due to modest sample size, this study provides preliminary evidence to support the presence of accelerated loss in VPFC and rostral PFC volume in adolescents/young adults with BD.

Self-assembly of PH-responsive Fluorinated Dendrimer-based Particulates for Drug Delivery and Noninvasive Imaging

Biomaterials. Aug, 2009  |  Pubmed ID: 19443028

Dendrimers are nanoscale macromolecules with well-defined branching chemical structures. Control over the architecture and function of these structures has enabled many advances in materials science and biomedical applications. Though dendrimers are directly synthesized by iteration of simple repetitive steps, generation of the larger, more complex structures required for many biomedical applications by covalent synthetic methods has been challenging. Here we demonstrate a spontaneous self-assembly of poly(amidoamine) dendrimers into complex nanoscopic and microscopic particulates following partial fluorination of the constituent dendrimer subunits. These dense particulates exhibit a stimulus-induced response to low external pH that causes their disassembly over time, enabling controlled release of encapsulated agents. In addition, we show that these assemblies offer a sufficiently high density of fluorine spins to enable detection of their site-specific accumulation in vivo by (19)F magnetic resonance imaging ((19)F MRI). Fluorinated dendrimer-based particulates present new features and capabilities important for a wide variety of emerging biomedical applications.

Serial Noninvasive Targeted Imaging of Peripheral Angiogenesis: Validation and Application of a Semiautomated Quantitative Approach

Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine. Aug, 2009  |  Pubmed ID: 19617325

Previous studies by our group have demonstrated the feasibility of noninvasive imaging of alpha(v) integrin to assess temporal and spatial changes in peripheral and myocardial angiogenesis. In this study, we validate the reproducibility, accuracy, and applicability of a new semiautomated noninvasive approach for serial quantitative evaluation of targeted micro-SPECT/CT images of peripheral angiogenesis in wild-type and endothelial nitric oxide sythase (eNOS)-deficient (eNOS-/-) mice subjected to hindlimb ischemia.

OpenIGTLink: an Open Network Protocol for Image-guided Therapy Environment

The International Journal of Medical Robotics + Computer Assisted Surgery : MRCAS. Dec, 2009  |  Pubmed ID: 19621334

With increasing research on system integration for image-guided therapy (IGT), there has been a strong demand for standardized communication among devices and software to share data such as target positions, images and device status.

Sexually Dimorphic Features of Vermis Morphology in Bipolar Disorder

Bipolar Disorders. Nov, 2009  |  Pubmed ID: 19839998

The cerebellar vermis is increasingly implicated in bipolar disorder (BD). In this study, we investigated vermis morphology in BD using a quantitative volumetric analysis.

Variation in Orbitofrontal Cortex Volume: Relation to Sex, Emotion Regulation and Affect

Social Cognitive and Affective Neuroscience. Dec, 2009  |  Pubmed ID: 20019072

Sex differences in brain structure have been examined extensively but are not completely understood, especially in relation to possible functional correlates. Our two aims in this study were to investigate sex differences in brain structure, and to investigate a possible relation between orbitofrontal cortex subregions and affective individual differences. We used tensor-based morphometry to estimate local brain volume from MPRAGE images in 117 healthy right-handed adults (58 female), age 18-40 years. We entered estimates of local brain volume as the dependent variable in a GLM, controlling for age, intelligence and whole-brain volume. Men had larger left planum temporale. Women had larger ventromedial prefrontal cortex (vmPFC), right lateral orbitofrontal (rlOFC), cerebellum, and bilateral basal ganglia and nearby white matter. vmPFC but not rlOFC volume covaried with self-reported emotion regulation strategies (reappraisal, suppression), expressivity of positive emotions (but not of negative), strength of emotional impulses, and cognitive but not somatic anxiety. vmPFC volume statistically mediated sex differences in emotion suppression. The results confirm prior reports of sex differences in orbitofrontal cortex structure, and are the first to show that normal variation in vmPFC volume is systematically related to emotion regulation and affective individual differences.

A Dynamical Shape Prior for LV Segmentation from RT3D Echocardiography

Medical Image Computing and Computer-assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2009  |  Pubmed ID: 20054422

Real-time three-dimensional (RT3D) echocardiography is the newest generation of three-dimensional (3-D) echocardiography. Segmentation of RT3D echocardiographic images is essential for determining many important diagnostic parameters. In cardiac imaging, since the heart is a moving organ, prior knowledge regarding its shape and motion patterns becomes an important component for the segmentation task. However, most previous cardiac models are either static models (SM), which neglect the temporal coherence of a cardiac sequence or generic dynamical models (GDM), which neglect the inter-subject variability of cardiac motion. In this paper, we present a subject-specific dynamical model (SSDM) which simultaneously handles inter-subject variability and cardiac dynamics (intra-subject variability). It can progressively predict the shape and motion patterns of a new sequence at the current frame based on the shapes observed in the past frames. The incorporation of this SSDM into the segmentation process is formulated in a recursive Bayesian framework. This results in a segmentation of each frame based on the intensity information of the current frame, as well as on the prediction from the previous frames. Quantitative results on 15 RT3D echocardiographic sequences show that automatic segmentation with SSDM is superior to that of either SM or GDM, and is comparable to manual segmentation.

AN IMPROVED UNBIASED METHOD FOR DIFFSPECT QUANTIFICATION IN EPILEPSY

Proceedings / IEEE International Symposium on Biomedical Imaging: from Nano to Macro. IEEE International Symposium on Biomedical Imaging. Jun, 2009  |  Pubmed ID: 20706559

Determining the region of seizure onset is of critical importance for treating medically intractable epilepsy. Comparisons between an ictal and interictal Single Photon Emission Computed Tomography (SPECT) images have been shown to be successful in localizing focal epilepsy. The Ictal-Interictal Subtraction Analysis by Statistical Parametric Mapping (ISAS) algorithm remains one the more successful algorithms for comparing these images. However ISAS is limited by its statistical design. This design introduces a scan order bias in the estimation of the normal variance of sequential SPECT images. We have corrected this bias by estimating the normal variance with a half-normal distribution. In this paper we present an updated algorithm (ISAS HN) based on the original ISAS algorithm with a corrected estimate of the normal variance and an open-source utility for ISAS HN.

A Non-rigid Registration Method for Serial MicroCT Mouse Hindlimb Images

Lecture Notes in Computer Science. Jan, 2009  |  Pubmed ID: 20411038

We present a new method for the non-rigid registration of serial mouse microCT images which undergo potentially large changes in the positions of the legs due to articulation. While non-rigid registration methods have been extensively used in the evaluation of individual organs, application in whole body imaging has been limited, primarily because the scale of possible displacements and deformations is large resulting in poor convergence of most methods. Our method is based on the extended demons algorithm that uses a level-set representation of the mouse skin and skeleton as an input, and composed of three steps reflecting the natural physical movements of bony structures. We applied our method to the registration of serial microCT mouse images demonstrating encouraging performances as compared to competitive techniques.

A Dynamical Shape Prior for LV Segmentation from RT3D Echocardiography

Medical Image Computing and Computer-assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2009  |  Pubmed ID: 20425989

Real-time three-dimensional (RT3D) echocardiography is the newest generation of three-dimensional (3-D) echocardiography. Segmentation of RT3D echocardiographic images is essential for determining many important diagnostic parameters. In cardiac imaging, since the heart is a moving organ, prior knowledge regarding its shape and motion patterns becomes an important component for the segmentation task. However, most previous cardiac models are either static models (SM), which neglect the temporal coherence of a cardiac sequence or generic dynamical models (GDM), which neglect the inter-subject variability of cardiac motion. In this paper, we present a subject-specific dynamical model (SSDM) which simultaneously handles inter-subject variability and cardiac dynamics (intra-subject variability). It can progressively predict the shape and motion patterns of a new sequence at the current frame based on the shapes observed in the past frames. The incorporation of this SSDM into the segmentation process is formulated in a recursive Bayesian framework. This results in a segmentation of each frame based on the intensity information of the current frame, as well as on the prediction from the previous frames. Quantitative results on 15 RT3D echocardiographic sequences show that automatic segmentation with SSDM is superior to that of either SM or GDM, and is comparable to manual segmentation.

A Non-rigid Registration Method for Serial MicroCT Mouse Hindlimb Images

Medical Image Computing and Computer-assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2009  |  Pubmed ID: 20426048

We present a new method for the non-rigid registration of serial mouse microCT images which undergo potentially large changes in the positions of the legs due to articulation. While non-rigid registration methods have been extensively used in the evaluation of individual organs, application in whole body imaging has been limited, primarily because the scale of possible displacements and deformations is large resulting in poor convergence of most methods. Our method is based on the extended demons algorithm that uses a level-set representation of the mouse skin and skeleton as an input, and composed of three steps reflecting the natural physical movements of bony structures. We applied our method to the registration of serial microCT mouse images demonstrating encouraging performances as compared to competitive techniques.

Volumetric Shape Model for Oriented Tubular Structure from DTI Data

Medical Image Computing and Computer-assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2009  |  Pubmed ID: 20426091

In this paper, we describe methods for constructing shape priors using orientation information to model white matter tracts from magnetic resonance diffusion tensor images (DTI). Shape Normalization is needed for the construction of a shape prior using statistical methods. Moving beyond shape normalization using boundary-only or orientation-only information, our method combines the idea of sweeping and inverse-skeletonization to parameterize 3D volumetric shape, which provides point correspondence and orientations over the whole volume in a continuous fashion. Tangents from this continuous model can be treated as a de-noised reconstruction of the original structural orientation inside a shape. We demonstrate the accuracy of this technique by reconstructing synthetic data and the 3D cingulum tract from brain DTI data and manually drawn 2D contours for each tract. Our output can also serve as the input for subsequent boundary finding or shape analysis.

Testing Predictions from Personality Neuroscience. Brain Structure and the Big Five

Psychological Science. Jun, 2010  |  Pubmed ID: 20435951

We used a new theory of the biological basis of the Big Five personality traits to generate hypotheses about the association of each trait with the volume of different brain regions. Controlling for age, sex, and whole-brain volume, results from structural magnetic resonance imaging of 116 healthy adults supported our hypotheses for four of the five traits: Extraversion, Neuroticism, Agreeableness, and Conscientiousness. Extraversion covaried with volume of medial orbitofrontal cortex, a brain region involved in processing reward information. Neuroticism covaried with volume of brain regions associated with threat, punishment, and negative affect. Agreeableness covaried with volume in regions that process information about the intentions and mental states of other individuals. Conscientiousness covaried with volume in lateral prefrontal cortex, a region involved in planning and the voluntary control of behavior. These findings support our biologically based, explanatory model of the Big Five and demonstrate the potential of personality neuroscience (i.e., the systematic study of individual differences in personality using neuroscience methods) as a discipline.

Integrated Segmentation and Nonrigid Registration for Application in Prostate Image-guided Radiotherapy

Medical Image Computing and Computer-assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2010  |  Pubmed ID: 20879214

Many current image-guided radiotherapy (IGRT) systems incorporate an in-room cone-beam CT (CBCT) with a radiotherapy linear accelerator for treatment day imaging. Segmentation of key anatomical structures (prostate and surrounding organs) in 3DCBCT images as well as registration between planning and treatment images are essential for determining many important treatment parameters. Due to the image quality of CBCT, previous work typically uses manual segmentation of the soft tissues and then registers the images based on the manual segmentation. In this paper, an integrated automatic segmentation/constrained nonrigid registration is presented, which can achieve these two aims simultaneously. This method is tested using 24 sets of real patient data. Quantitative results show that the automatic segmentation produces results that have an accuracy comparable to manual segmentation, while the registration part significantly outperforms both rigid and non-rigid registration. Clinical application also shows promising results.

Vascular Tree Reconstruction by Minimizing A Physiological Functional Cost

Conference on Computer Vision and Pattern Recognition Workshops IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops. Jun, 2010  |  Pubmed ID: 21755061

The reconstruction of complete vascular trees from medical images has many important applications. Although vessel detection has been extensively investigated, little work has been done on how connect the results to reconstruct the full trees. In this paper, we propose a novel theoretical framework for automatic vessel connection, where the automation is achieved by leveraging constraints from the physiological properties of the vascular trees. In particular, a physiological functional cost for the whole vascular tree is derived and an efficient algorithm is developed to minimize it. The method is generic and can be applied to different vessel detection/segmentation results, e.g. the classic rigid detection method as adopted in this paper. We demonstrate the effectiveness of this method on both 2D and 3D data.

A Coupled Deformable Model for Tracking Myocardial Borders from Real-time Echocardiography Using an Incompressibility Constraint

Medical Image Analysis. Jun, 2010  |  Pubmed ID: 20350833

Real-time three-dimensional (RT3D) echocardiography is a new image acquisition technique that allows instantaneous acquisition of volumetric images for quantitative assessment of cardiac morphology and function. To quantify many important diagnostic parameters, such as ventricular volume, ejection fraction, and cardiac output, an automatic algorithm to delineate the left ventricle (LV) from RT3D echocardiographic images is essential. While a number of efforts have been made towards segmentation of the LV endocardial (ENDO) boundaries, the segmentation of epicardial (EPI) boundaries remains problematic. In this paper, we present a coupled deformable model that addresses this problem. The idea behind our method is that the volume of the myocardium is close to being constant during a cardiac cycle and our model uses this coupling as an important constraint. We employ two surfaces, each driven by the image-derived information that takes into account ultrasound physics by modeling the speckle statistics using the Nakagami distribution while maintaining the coupling. By simultaneously evolving two surfaces, the final segmentation of the myocardium is thus achieved. Results from 80 sets of synthetic data and 286 sets of real canine data were evaluated against the ground truth and against outlines from three independent observers, respectively. We show that results obtained with our incompressibility constraint were more accurate than those obtained without constraint or with a wall thickness constraint, and were comparable to those from manual segmentation.

New Open-source Ictal SPECT Analysis Method Implemented in BioImage Suite

Epilepsia. Apr, 2010  |  Pubmed ID: 20074234

Ictal single photon emission computed tomography (SPECT) is a powerful tool for noninvasive seizure localization, but it has been underutilized because of practical challenges, including difficulty in implementing ictal-interictal SPECT difference analysis. We previously validated a freely available utility for this purpose, ictal-interictal subtraction analysis by statistical parametric mapping (SPM) (ISAS). To further simplify and improve the difference imaging technique, we now compare a new algorithm, ISAS BioImage Suite (see http://spect.yale.edu and http://bioimagesuite.org), to the original ISAS method in 13 patients with known seizure localization. We found that ISAS BioImage Suite was in agreement with the original algorithm in all cases for which ISAS correctly identified a single unambiguous region of seizure onset. We also tested for possible effects of scan-order bias in the control group used for the analysis and found no significant effect on the results. These findings establish a simple, validated and objective method for analyzing ictal-interictal SPECT difference images for use in the care of patients with epilepsy.

Image-guided Intraoperative Cortical Deformation Recovery Using Game Theory: Application to Neocortical Epilepsy Surgery

IEEE Transactions on Medical Imaging. Feb, 2010  |  Pubmed ID: 20129844

During neurosurgery, nonrigid brain deformation prevents preoperatively-acquired images from accurately depicting the intraoperative brain. Stereo vision systems can be used to track intraoperative cortical surface deformation and update preoperative brain images in conjunction with a biomechanical model. However, these stereo systems are often plagued with calibration error, which can corrupt the deformation estimation. In order to decouple the effects of camera calibration from the surface deformation estimation, a framework that can solve for disparate and often competing variables is needed. Game theory, which was developed to handle decision making in this type of competitive environment, has been applied to various fields from economics to biology. In this paper, game theory is applied to cortical surface tracking during neocortical epilepsy surgery and used to infer information about the physical processes of brain surface deformation and image acquisition. The method is successfully applied to eight in vivo cases, resulting in an 81% decrease in mean surface displacement error. This includes a case in which some of the initial camera calibration parameters had errors of 70%. Additionally, the advantages of using a game theoretic approach in neocortical epilepsy surgery are clearly demonstrated in its robustness to initial conditions.

Segmentation of the Left Ventricle from Cardiac MR Images Using a Subject-specific Dynamical Model

IEEE Transactions on Medical Imaging. Mar, 2010  |  Pubmed ID: 19789107

Statistical models have shown considerable promise as a basis for segmenting and interpreting cardiac images. While a variety of statistical models have been proposed to improve the segmentation results, most of them are either static models (SMs), which neglect the temporal dynamics of a cardiac sequence, or generic dynamical models (GDMs), which are homogeneous in time and neglect the intersubject variability in cardiac shape and deformation. In this paper, we develop a subject-specific dynamical model (SSDM) that simultaneously handles temporal dynamics (intrasubject variability) and intersubject variability. We also propose a dynamic prediction algorithm that can progressively identify the specific motion patterns of a new cardiac sequence based on the shapes observed in past frames. The incorporation of this SSDM into the segmentation framework is formulated in a recursive Bayesian framework. It starts with a manual segmentation of the first frame, and then segments each frame according to intensity information from the current frame as well as the prediction from past frames. In addition, to reduce error propagation in sequential segmentation, we take into account the periodic nature of cardiac motion and perform segmentation in both forward and backward directions. We perform "leave-one-out" test on 32 canine sequences and 22 human sequences, and compare the experimental results with those from SM, GDM, and active appearance motion model (AAMM). Quantitative analysis of the experimental results shows that SSDM outperforms SM, GDM, and AAMM by having better global and local consistencies with manual segmentation. Moreover, we compare the segmentation results from forward and forward-backward segmentation. Quantitative evaluation shows that forward-backward segmentation suppresses the propagation of segmentation errors.

Neuroanatomical Changes in a Mouse Model of Early Life Neglect

Brain Structure & Function. Oct, 2011  |  Pubmed ID: 21984312

Using a novel mouse model of early life neglect and abuse (ENA) based on maternal separation with early weaning, George et al. (BMC Neurosci 11:123, 2010) demonstrated behavioral abnormalities in adult mice, and Bordner et al. (Front Psychiatry 2(18):1-18, 2011) described concomitant changes in mRNA and protein expression. Using the same model, here we report neuroanatomical changes that include smaller brain size and abnormal inter-hemispheric asymmetry, decreases in cortical thickness, abnormalities in subcortical structures, and white matter disorganization and atrophy most severely affecting the left hemisphere. Because of the similarities between the neuroanatomical changes observed in our mouse model and those described in human survivors of ENA, this novel animal model is potentially useful for studies of human ENA too costly or cumbersome to be carried out in primates. Moreover, our current knowledge of the mouse genome makes this model particularly suited for targeted anatomical, molecular, and pharmacological experimentation not yet possible in other species.

Integrated Parcellation and Normalization Using DTI Fasciculography

Medical Image Computing and Computer-assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2011  |  Pubmed ID: 21995010

Existing methods for fiber tracking, interactive bundling and editing from Diffusion Magnetic Resonance Images (DMRI) reconstruct white matter fascicles using groups of virtual pathways. Classical numerical fibers suffer from image noise and cumulative tracking errors. 3D visualization of bundles of fibers reveals structural connectivity of the brain; however, extensive human intervention, tracking variations and errors in fiber sampling make quantitative fascicle comparison difficult. To simplify the process and offer standardized white matter samples for analysis, we propose a new integrated fascicle parcellation and normalization method that combines a generic parametrized volumetric tract model with orientation information from diffusion images. The new technique offers a tract-derived spatial parameter for each voxel within the model. Cross-subject statistics of tract data can be compared easily based on these parameters. Our implementation demonstrated interactive speed and is available to the public in a packaged application.

Vessel Connectivity Using Murray's Hypothesis

Medical Image Computing and Computer-assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2011  |  Pubmed ID: 22003740

We describe a new method for vascular image analysis that incorporates a generic physiological principle to estimate vessel connectivity, which is a key issue in reconstructing complete vascular trees from image data. We follow Murray's hypothesis of the minimum work principle to formulate the problem as an optimization problem. This principle reflects a global property of any vascular network, in contrast to various local geometric properties adopted as constraints previously. We demonstrate the effectiveness of our method using a set of microCT mouse coronary images. It is shown that the performance of our method has a statistically significant improvement over the widely adopted minimum spanning tree methods that rely on local geometric constraints.

Lateral Ventricle Volume and Psychotic Features in Adolescents and Adults with Bipolar Disorder

Psychiatry Research. Dec, 2011  |  Pubmed ID: 22041535

This magnetic resonance imaging study demonstrates increased lateral ventricle volume (LVV) in adolescents and adults with bipolar disorder (BD) with psychotic symptoms, but not without psychosis, compared to healthy adolescents and adults. This suggests LVV is a morphologic feature associated with psychosis in BD, present by adolescence.

A Whole-brain Voxel Based Measure of Intrinsic Connectivity Contrast Reveals Local Changes in Tissue Connectivity with Anesthetic Without a Priori Assumptions on Thresholds or Regions of Interest

NeuroImage. Oct, 2011  |  Pubmed ID: 21763437

The analysis of spontaneous fluctuations of functional magnetic resonance imaging (fMRI) signals has recently gained attention as a powerful tool for investigating brain circuits in a non-invasive manner. Correlation-based connectivity analysis investigates the correlations of spontaneous fluctuations of the fMRI signal either between a single seed region of interest (ROI) and the rest of the brain or between multiple ROIs. To do this, a priori knowledge is required for defining the ROI(s) and without such knowledge functional connectivity fMRI cannot be used as an exploratory tool for investigating the functional organization of the brain and its modulation under different conditions. In this work we examine two indices that provide voxel based maps reflecting the intrinsic connectivity contrast (ICC) of individual tissue elements without the need for defining ROIs and hence require no a priori information or assumptions. These voxel based ICC measures can also be used to delineate regions of interest for further functional or network analyses. The indices were applied to the study of sevoflurane anesthesia-induced alterations in intrinsic connectivity. In concordance with previous studies, the results show that sevoflurane affects different brain circuits in a heterogeneous manner. In addition ICC analyses revealed changes in regions not previously identified using conventional ROI connectivity analyses, probably because of an inappropriate choice of the ROI in the earlier studies. This work highlights the importance of such voxel based connectivity methodology.

Dissociation Between the Activity of the Right Middle Frontal Gyrus and the Middle Temporal Gyrus in Processing Semantic Priming

PloS One. 2011  |  Pubmed ID: 21829619

The aim of this event-related functional magnetic resonance imaging (fMRI) study was to test whether the right middle frontal gyrus (MFG) and middle temporal gyrus (MTG) would show differential sensitivity to the effect of prime-target association strength on repetition priming. In the experimental condition (RP), the target occurred after repetitive presentation of the prime within an oddball design. In the control condition (CTR), the target followed a single presentation of the prime with equal probability of the target as in RP. To manipulate semantic overlap between the prime and the target both conditions (RP and CTR) employed either the onomatopoeia "oink" as the prime and the referent "pig" as the target (OP) or vice-versa (PO) since semantic overlap was previously shown to be greater in OP. The results showed that the left MTG was sensitive to release of adaptation while both the right MTG and MFG were sensitive to sequence regularity extraction and its verification. However, dissociated activity between OP and PO was revealed in RP only in the right MFG. Specifically, target "pig" (OP) and the physically equivalent target in CTR elicited comparable deactivations whereas target "oink" (PO) elicited less inhibited response in RP than in CTR. This interaction in the right MFG was explained by integrating these effects into a competition model between perceptual and conceptual effects in priming processing.

Development and Application of a Multimodal Contrast Agent for SPECT/CT Hybrid Imaging

Bioconjugate Chemistry. Sep, 2011  |  Pubmed ID: 21851119

Hybrid or multimodality imaging is often applied in order to take advantage of the unique and complementary strengths of individual imaging modalities. This hybrid noninvasive imaging approach can provide critical information about anatomical structure in combination with physiological function or targeted molecular signals. While recent advances in software image fusion techniques and hybrid imaging systems have enabled efficient multimodal imaging, accessing the full potential of this technique requires development of a new toolbox of multimodal contrast agents that enhance the imaging process. Toward that goal, we report the development of a hybrid probe for both single photon emission computed tomography (SPECT) and X-ray computed tomography (CT) imaging that facilitates high-sensitivity SPECT and high spatial resolution CT imaging. In this work, we report the synthesis and evaluation of a novel intravascular, multimodal dendrimer-based contrast agent for use in preclinical SPECT/CT hybrid imaging systems. This multimodal agent offers a long intravascular residence time (t(1/2) = 43 min) and sufficient contrast-to-noise for effective serial intravascular and blood pool imaging with both SPECT and CT. The colocalization of the dendritic nuclear and X-ray contrasts offers the potential to facilitate image analysis and quantification by enabling correction for SPECT attenuation and partial volume errors at specified times with the higher resolution anatomic information provided by the circulating CT contrast. This may allow absolute quantification of intramyocardial blood volume and blood flow and may enable the ability to visualize active molecular targeting following clearance from the blood.

Diffusion Tensor Imaging in Autism Spectrum Disorders: Preliminary Evidence of Abnormal Neural Connectivity

The Australian and New Zealand Journal of Psychiatry. Feb, 2011  |  Pubmed ID: 21128874

This study indirectly tested the hypothesis that individuals with autism spectrum disorders (ASDs) have impaired neural connections between the amygdala, fusiform face area, and superior temporal sulcus, key processing nodes of the 'social brain'. This would be evidenced by abnormalities in the major fibre tracts known to connect these structures, including the inferior longitudinal fasciculus and inferior fronto-occipital fasciculus.

Intraoperative Real-time Querying of White Matter Tracts During Frameless Stereotactic Neuronavigation

Neurosurgery. Feb, 2011  |  Pubmed ID: 21135719

Brain surgery faces important challenges when trying to achieve maximum tumor resection while avoiding postoperative neurological deficits.

Unified Framework for Development, Deployment and Robust Testing of Neuroimaging Algorithms

Neuroinformatics. Mar, 2011  |  Pubmed ID: 21249532

Developing both graphical and command-line user interfaces for neuroimaging algorithms requires considerable effort. Neuroimaging algorithms can meet their potential only if they can be easily and frequently used by their intended users. Deployment of a large suite of such algorithms on multiple platforms requires consistency of user interface controls, consistent results across various platforms and thorough testing. We present the design and implementation of a novel object-oriented framework that allows for rapid development of complex image analysis algorithms with many reusable components and the ability to easily add graphical user interface controls. Our framework also allows for simplified yet robust nightly testing of the algorithms to ensure stability and cross platform interoperability. All of the functionality is encapsulated into a software object requiring no separate source code for user interfaces, testing or deployment. This formulation makes our framework ideal for developing novel, stable and easy-to-use algorithms for medical image analysis and computer assisted interventions. The framework has been both deployed at Yale and released for public use in the open source multi-platform image analysis software--BioImage Suite (bioimagesuite.org).

Targeted Imaging of the Spatial and Temporal Variation of Matrix Metalloproteinase Activity in a Porcine Model of Postinfarct Remodeling: Relationship to Myocardial Dysfunction

Circulation. Cardiovascular Imaging. Jul, 2011  |  Pubmed ID: 21505092

Matrix metalloproteinases (MMPs) are known to modulate left ventricular (LV) remodeling after a myocardial infarction (MI). However, the temporal and spatial variation of MMP activation and their relationship to mechanical dysfunction after MI remain undefined.

An Integrated Approach to Segmentation and Nonrigid Registration for Application in Image-guided Pelvic Radiotherapy

Medical Image Analysis. Oct, 2011  |  Pubmed ID: 21646038

External beam radiotherapy (EBRT) has become the preferred options for nonsurgical treatment of prostate cancer and cervix cancer. In order to deliver higher doses to cancerous regions within these pelvic structures (i.e. prostate or cervix) while maintaining or lowering the doses to surrounding non-cancerous regions, it is critical to account for setup variation, organ motion, anatomical changes due to treatment and intra-fraction motion. In previous work, manual segmentation of the soft tissues is performed and then images are registered based on the manual segmentation. In this paper, we present an integrated automatic approach to multiple organ segmentation and nonrigid constrained registration, which can achieve these two aims simultaneously. The segmentation and registration steps are both formulated using a Bayesian framework, and they constrain each other using an iterative conditional model strategy. We also propose a new strategy to assess cumulative actual dose for this novel integrated algorithm, in order to both determine whether the intended treatment is being delivered and, potentially, whether or not a plan should be adjusted for future treatment fractions. Quantitative results show that the automatic segmentation produced results that have an accuracy comparable to manual segmentation, while the registration part significantly outperforms both rigid and nonrigid registration. Clinical application and evaluation of dose delivery show the superiority of proposed method to the procedure currently used in clinical practice, i.e. manual segmentation followed by rigid registration.

A Non-rigid Registration Method for Serial Lower Extremity Hybrid SPECT/CT Imaging

Medical Image Analysis. Feb, 2011  |  Pubmed ID: 20869902

Small animal X-ray computed tomographic (microCT) imaging of the lower extremities permits evaluation of arterial growth in models of hindlimb ischemia, and when applied serially can provide quantitative information about disease progression and aid in the evaluation of therapeutic interventions. The quantification of changes in tissue perfusion and concentration of molecular markers concurrently obtained using nuclear imaging requires the ability to non-rigidly register the microCT images over time, a task made more challenging by the potentially large changes in the positions of the legs due to articulation. While non-rigid registration methods have been extensively used in the evaluation of individual organs, application in whole body imaging has been limited, primarily because the scale of possible displacements and deformations is large resulting in poor convergence of most methods. In this paper we present a new method based on the extended demons algorithm that uses a level-set representation of the body contour and skeletal structure as an input. The proposed serial registration method reflects the natural physical moving combination of mouse anatomy in which the movement of bones is the framework for body movements, and the movement of skin constrains the detailed movements of the specific segmented body regions. We applied our method to both the registration of serial microCT mouse images and the quantification of microSPECT component of the serially hybrid microCT-SPECT images demonstrating improved performance as compared to existing registration techniques.

Fasciculography: Robust Prior-free Real-time Normalized Volumetric Neural Tract Parcellation

IEEE Transactions on Medical Imaging. Feb, 2012  |  Pubmed ID: 21914568

Fiber tracking in diffusion tensor magnetic resonance images (DTIs) reveals 3-D structural connectivity of the brain conveniently and thus is a viable tool for investigating neural differences. Unfortunately, local noise, image artifacts and numerical tracking errors during integration-based techniques are cumulative. Prematurely terminated fibers and under-sampled fiber bundles result in incomplete reconstruction of white matter fiber tracts and hence incorrect anatomical measurements. Quantitative cross-subject tract analysis, which is critical for abnormality detection, is complicated by inefficient and inaccurate tract reconstruction and normalization from fiber bundles. Because of the above problems, we propose a parcellation method that aims for lower sensitivity to initialization and local orientation error by directly segmenting full white matter tracts (Fasciculography), rather than reconstructing individual curves, from diffusion tensor fields. A fast, robust volumetric, and intrinsically normalized solution is achieved by noise-filtering using a generic parametrized tract model to prevent premature tract termination. At the same time, orientation information reduces the search space, significantly speeding up the tract parcellation process with less human intervention. Detailed comparisons against streamline tracking, shortest-path tracking, and nonrigid registration using synthetic and real DTIs confirmed the superior properties of Fasciculography. Since a normalized tract can be delineated interactively in a just few seconds using the proposed method, accurate high volume tract comparisons become feasible.

CT-PET Weighted Image Fusion for Separately Scanned Whole Body Rat

Medical Physics. Jan, 2012  |  Pubmed ID: 22225323

Purpose: The limited resolution and lack of spatial information in positron emission tomography (PET) images require the complementary anatomic information from the computed tomography (CT) and/or magnetic resonance imaging (MRI). Therefore, multimodality image fusion techniques such as PET/CT are critical in mapping the functional images to structural images and thus facilitate the interpretation of PET studies. In our experimental situation, the CT and PET images are acquired in separate scanners at different times and the inherent differences in the imaging protocols produce significant nonrigid changes between the two acquisitions in addition to dissimilar image characteristics. The registration conditions are also poor because CT images have artifacts due to the limitation of current scanning settings, while PET images are very blurry (in transmission-PET) and have vague anatomical structure boundaries (in emission-PET).Methods: The authors present a new method for whole body small animal multimodal registration. In particular, the authors register whole body rat CT image and PET images using a weighted demons algorithm. The authors use both the transmission-PET and the emission-PET images in the registration process emphasizing particular regions of the moving transmission-PET image using the emission-PET image. After a rigid transformation and a histogram matching between the CT and the transmission-PET images, the authors deformably register the transmission-PET image to the CT image with weights based on the intensity-normalized emission-PET image. For the deformable registration process, the authors develop a weighted demons registration method that can give preferences to particular regions of the input image using a weight image.Results: The authors validate the results with nine rat image sets using the M-Hausdorff distance (M-HD) similarity measure with different outlier-suppression parameters (OSP). In comparison with standard methods such as the regular demons and the normalized mutual information (NMI)-based nonrigid free-form deformation (FFD) registration, the proposed weighted demons registration method shows average M-HD errors: 3.99 ± 1.37 (OSP = 10), 5.04 ± 1.59 (OSP = 20) and 5.92 ± 1.61 (OSP = ∞) with statistical significance (p < 0.0003) respectively, while NMI-based nonrigid FFD has average M-HD errors: 5.74 ± 1.73 (OSP = 10), 7.40 ± 7.84 (OSP = 20) and 9.83 ± 4.13 (OSP = ∞), and the regular demons has average M-HD errors: 6.79 ± 0.83 (OSP = 10), 9.19 ± 2.39 (OSP = 20) and 11.63 ± 3.99 (OSP = ∞), respectively. In addition to M-HD comparisons, the visual comparisons on the faint-edged region between the CT and the aligned PET images also show the encouraging improvements over the other methods.Conclusions: In the whole body multimodal registration between CT and PET images, the utilization of both the transmission-PET and the emission-PET images in the registration process by emphasizing particular regions of the transmission-PET image using an emission-PET image is effective. This method holds promise for other image fusion applications where multiple (more than two) input images should be registered into a single informative image.

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