Publications

RATIONALE AND OBJECTIVES: There are limited data on, and controversies regarding gender differences in the airway dimensions of smokers. Multi-detector CT (MDCT) images were analyzed to examine whether gender could explain differences in airway dimensions of anatomically matched airways in smokers. MATERIALS AND METHODS: We used VIDA imaging software to analyze MDCT scans from 2047 smokers (M:F, 1021:1026) from the COPDGene® cohort. The airway dimensions were analyzed from segmental to subsubsegmental bronchi. We compared the differences of luminal area, inner diameter, wall thickness, wall area percentage (WA%) for each airway between men and women, and multiple linear regression including covariates (age, gender, body sizes, and other relevant confounding factors) was used to determine the predictors of each airway dimensions. RESULTS: Lumen area, internal diameter and wall thickness were smaller for women than men in all measured airway (18.4 vs 22.5 mm(2) for segmental bronchial lumen area, 10.4 vs 12.5 mm(2) for subsegmental bronchi, 6.5 vs 7.7 mm(2) for subsubsegmental bronchi, respectively p 0.001). However, women had greater WA% in subsegmental and subsubsegmental bronchi. In multivariate regression, gender remained one of the most significant predictors of WA%, lumen area, inner diameter and wall thickness. CONCLUSION: Women smokers have higher WA%, but lower luminal area, internal diameter and airway thickness in anatomically matched airways as measured by CT scan than do male smokers. This difference may explain, in part, gender differences in the prevalence of COPD and airflow limitation.

BACKGROUND: The coexistence of COPD and asthma is widely recognized but has not been well described. This study characterizes clinical features, spirometry, and chest CT scans of smoking subjects with both COPD and asthma. METHODS: We performed a cross-sectional study comparing subjects with COPD and asthma to subjects with COPD alone in the COPDGene Study.

RATIONALE: The characterization of young adults who develop late-onset diseases may augment the detection of novel genes and promote new pathogenic insights.

In this paper, we introduce a new approach for tensor field segmentation based on the definition of mixtures of Gaussians on tensors as a statistical model. Working over the well-known Geodesic Active Regions segmentation framework, this scheme presents several interesting advantages. First, it yields a more flexible model than the use of a single Gaussian distribution, which enables the method to better adapt to the complexity of the data. Second, it can work directly on tensor-valued images or, through a parallel scheme that processes independently the intensity and the local structure tensor, on scalar textured images. Two different applications have been considered to show the suitability of the proposed method for medical imaging segmentation. First, we address DT-MRI segmentation on a dataset of 32 volumes, showing a successful segmentation of the corpus callosum and favourable comparisons with related approaches in the literature. Second, the segmentation of bones from hand radiographs is studied, and a complete automatic-semiautomatic approach has been developed that makes use of anatomical prior knowledge to produce accurate segmentation results.

High Angular Resolution Diffusion Imaging (HARDI) demands a higher amount of data measurements compared to Diffusion Tensor Imaging (DTI), restricting its use in practice. We propose to represent the probabilistic Orientation Distribution Function (ODF) in the frame of Spherical Wavelets (SW), where it is highly sparse. From a reduced subset of measurements (nearly four times less than the standard for HARDI), we pose the estimation as an inverse problem with sparsity regularization. This allows the fast computation of a positive, unit-mass, probabilistic ODF from 14-16 samples, as we show with both synthetic diffusion signals and real HARDI data with typical parameters.

In this paper, we propose a new nonparametric Bayesian framework to cluster white matter fiber tracts into bundles using a hierarchical Dirichlet processes mixture (HDPM) model. The number of clusters is automatically learned driven by data with a Dirichlet process (DP) prior instead of being manually specified. After the models of bundles have been learned from training data without supervision, they can be used as priors to cluster/classify fibers of new subjects for comparison across subjects. When clustering fibers of new subjects, new clusters can be created for structures not observed in the training data. Our approach does not require computing pairwise distances between fibers and can cluster a huge set of fibers across multiple subjects. We present results on several data sets, the largest of which has more than 120,000 fibers.

PURPOSE: To test the hypothesis-given the increasing emphasis on quantitative computed tomographic (CT) phenotypes of chronic obstructive pulmonary disease (COPD)-that a relationship exists between COPD exacerbation frequency and quantitative CT measures of emphysema and airway disease. MATERIALS AND METHODS: This research protocol was approved by the institutional review board of each participating institution, and all participants provided written informed consent. One thousand two subjects who were enrolled in the COPDGene Study and met the GOLD (Global Initiative for Chronic Obstructive Lung Disease) criteria for COPD with quantitative CT analysis were included. Total lung emphysema percentage was measured by using the attenuation mask technique with a -950-HU threshold. An automated program measured the mean wall thickness and mean wall area percentage in six segmental bronchi. The frequency of COPD exacerbation in the prior year was determined by using a questionnaire. Statistical analysis was performed to examine the relationship of exacerbation frequency with lung function and quantitative CT measurements.

Parametric statistical methods, such as Z-, t-, and F-values, are traditionally employed in functional magnetic resonance imaging (fMRI) for identifying areas in the brain that are active with a certain degree of statistical significance. These parametric methods, however, have two major drawbacks. First, it is assumed that the observed data are Gaussian distributed and independent; assumptions that generally are not valid for fMRI data. Second, the statistical test distribution can be derived theoretically only for very simple linear detection statistics. With nonparametric statistical methods, the two limitations described above can be overcome. The major drawback of non-parametric methods is the computational burden with processing times ranging from hours to days, which so far have made them impractical for routine use in single-subject fMRI analysis. In this work, it is shown how the computational power of cost-efficient graphics processing units (GPUs) can be used to speed up random permutation tests. A test with 10000 permutations takes less than a minute, making statistical analysis of advanced detection methods in fMRI practically feasible. To exemplify the permutation-based approach, brain activity maps generated by the general linear model (GLM) and canonical correlation analysis (CCA) are compared at the same significance level.

BACKGROUND: Schizophrenia is believed to result from abnormal functional integration of neural processes thought to arise from aberrant brain connectivity. However, evidence for anatomical dysconnectivity has been equivocal, and few studies have examined axonal fiber connectivity in schizophrenia at the level of whole-brain networks. METHODS: Cortico-cortical anatomical connectivity at the scale of axonal fiber bundles was modeled as a network. Eighty-two network nodes demarcated functionally specific cortical regions. Sixty-four direction diffusion tensor-imaging coupled with whole-brain tractography was performed to map the architecture via which network nodes were interconnected in each of 74 patients with schizophrenia and 32 age- and gender-matched control subjects. Testing was performed to identify pairs of nodes between which connectivity was impaired in the patient group. The connectional architecture of patients was tested for changes in five network attributes: nodal degree, small-worldness, efficiency, path length, and clustering. RESULTS: Impaired connectivity in the patient group was found to involve a distributed network of nodes comprising medial frontal, parietal/occipital, and the left temporal lobe. Although small-world attributes were conserved in schizophrenia, the cortex was interconnected more sparsely and up to 20% less efficiently in patients. Intellectual performance was found to be associated with brain efficiency in control subjects but not in patients. CONCLUSIONS: This study presents evidence of widespread dysconnectivity in white-matter connectional architecture in a large sample of patients with schizophrenia. When considered from the perspective of recent evidence for impaired synaptic plasticity, this study points to a multifaceted pathophysiology in schizophrenia encompassing axonal as well as putative synaptic mechanisms.

Automatic segmentation of spinal nerve bundles that originate within the dural sac and exit the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves seen in high resolution myelographic MR images makes segmentation a challenging task. In this paper, we present an automatic tracking method for nerve segmentation based on particle filters. We develop a novel approach to particle representation and dynamics, based on B\ ezier splines. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We demonstrate accurate and fast nerve tracking and compare it to expert manual segmentation.