Publications

BACKGROUND: CT scan measures of emphysema and airway disease have been correlated with lung function in cohorts of subjects with a range of COPD severity. The contribution of CT scan-assessed airway disease to objective measures of lung function and respiratory symptoms such as dyspnea in severe emphysema is less clear. METHODS: Using data from 338 subjects in the National Emphysema Treatment Trial (NETT) Genetics Ancillary Study, densitometric measures of emphysema using a threshold of -950 Hounsfield units (%LAA-950) and airway wall phenotypes of the wall thickness (WT) and the square root of wall area (SRWA) of a 10-mm luminal perimeter airway were calculated for each subject. Linear regression analysis was performed for outcome variables FEV(1) and percent predicted value of FEV(1) with CT scan measures of emphysema and airway disease.

The relationship between quantitative airway measurements on computed tomography (CT) and airflow limitation in individuals with severe alpha (1)-antitrypsin deficiency (AATD) is undefined. Thus, we planned to clarify the relationship between CT-based airway indices and airflow limitation in AATD. 52 patients with AATD underwent chest CT and pre-bronchodilator spirometry at three institutions. In the right upper (RUL) and lower (RLL) lobes, wall area percent (WA%) and luminal area (Ai) were measured in the third, fourth, and fifth generations of the bronchi. The severity of emphysema was also calculated in each lobe and expressed as low attenuation area percent (LAA%). Correlations between obtained measurements and FEV(1)% predicted (FEV(1)%P) were evaluated by the Spearman rank correlation test. In RUL, WA% of all generations was significantly correlated with FEV(1)%P (3rd, R = -0.33, p = 0.02; 4th, R = -0.39, p = 0.004; 5th, R = -0.57, p 0.001; respectively). Ai also showed significant correlations (3rd, R = 0.32, p = 0.02; 4th, R = 0.34, p = 0.01; 5th, R = 0.56, p 0.001; respectively). Measured correlation coefficients improved when the airway progressed distally from the third to fifth generations. LAA% also correlated with FEV(1)%P (R = -0.51, p 0.001). In RLL, WA% showed weak correlations with FEV(1)%P in all generations (3rd, R = -0.34, p = 0.01; 4th, R = -0.30, p = 0.03; 5th, R = -0.31, p = 0.03; respectively). Only Ai from the fifth generation significantly correlated with FEV(1)%P in this lobe (R = 0.34, p = 0.01). LAA% strongly correlated with FEV(1)%P (R = -0.71, p 0.001). We conclude therefore that quantitative airway measurements are significantly correlated with airflow limitation in AATD, particularly in the distal airways of RUL. Emphysema of the lower lung is the predominant component; however, airway disease also has a significant impact on airflow limitation in AATD.

Particle systems have gained importance as a methodology for sampling implicit surfaces and segmented objects to improve mesh generation and shape analysis. We propose that particle systems have a significantly more general role in sampling structure from unsegmented data. We describe a particle system that computes samplings of crease features (i.e. ridges and valleys, as lines or surfaces) that effectively represent many anatomical structures in scanned medical data. Because structure naturally exists at a range of sizes relative to the image resolution, computer vision has developed the theory of scale-space, which considers an n-D image as an (n+1)-D stack of images at different blurring levels. Our scale-space particles move through continuous four-dimensional scale-space according to spatial constraints imposed by the crease features, a particle-image energy that draws particles towards scales of maximal feature strength, and an inter-particle energy that controls sampling density in space and scale. To make scale-space practical for large three-dimensional data, we present a spline-based interpolation across scale from a small number of pre-computed blurrings at optimally selected scales. The configuration of the particle system is visualized with tensor glyphs that display information about the local Hessian of the image, and the scale of the particle. We use scale-space particles to sample the complex three-dimensional branching structure of airways in lung CT, and the major white matter structures in brain DTI.

We propose an integrated registration and clustering algorithm, called "consistency clustering", that automatically constructs a probabilistic white-matter atlas from a set of multi-subject diffusion weighted MR images. We formulate the atlas creation as a maximum likelihood problem which the proposed method solves using a generalized Expectation Maximization (EM) framework. Additionally, the algorithm employs an outlier rejection and denoising strategy to produce sharp probabilistic maps of certain bundles of interest. We test this algorithm on synthetic and real data, and evaluate its stability against initialization. We demonstrate labeling a novel subject using the resulting spatial atlas and evaluate the accuracy of this labeling. Consistency clustering is a viable tool for completely automatic white-matter atlas construction for sub-populations and the resulting atlas is potentially useful for making diffusion measurements in a common coordinate system to identify pathology related changes or developmental trends.

An estimator of the Orientation Probability Density Function (OPDF) of fiber tracts in the white matter of the brain from High Angular Resolution Diffusion data is presented. Unlike Q-Balls, which use the Funk-Radon transform to estimate the radial projection of the 3D Probability Density Function, the Jacobian of the spherical coordinates is included in the Funk-Radon approximation to the radial integral. Thus, true angular marginalizations are computed, which allows a strict probabilistic interpretation. Extensive experiments with both synthetic and real data show the better capability of our method to characterize complex micro-architectures compared to other related approaches (Q-Balls and Diffusion Orientation Transform), especially for low values of the diffusion weighting parameter.

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 learnt from data with a Dirichlet process (DP) prior instead of being manually specified. After the models of bundles have been learnt from training data without supervision, they can be used as priors to cluster/classify fibers of new 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 without subsampling. We present results on multiple data sets, the largest of which has more than 120, 000 fibers.

The computed tomographic (CT) densities of imaged structures are a function of the CT scanning protocol, the structure size, and the structure density. For objects that are of a dimension similar to the scanner point spread function, CT will underestimate true structure density. Prior investigation suggests that this process, termed contrast reduction, could be used to estimate the strength of thin structures, such as cortical bone. In this investigation, we endeavored to exploit this process to provide a CT-based measure of airway disease that can assess changes in airway wall thickening and density that may be associated with the mural remodeling process in subjects with chronic obstructive pulmonary disease (COPD). An initial computer-based study using a range of simulated airway wall sizes and densities suggested that CT measures of airway wall attenuation could detect changes in both wall thickness and structure density. A second phantom-based study was performed using a series of polycarbonate tubes of known density. The results of this again demonstrated the process of contrast reduction and further validated the computer-based simulation. Finally, measures of airway wall attenuation, wall thickness, and wall area (WA) divided by total cross-sectional area, WA percent (WA%), were performed in a cohort of 224 subjects with COPD and correlated with spirometric measures of lung function. The results of this analysis demonstrated that wall attenuation is comparable to WA% in predicting lung function on univariate correlation and remain as a statistically significant correlate to the percent forced expiratory volume in 1 s predicted when adjusted for measures of both emphysema and WA%. These latter findings suggest that the quantitative assessment of airway wall attenuation may offer complementary information to WA% in characterizing airway disease in subjects with COPD.

Least Squares (LS) and its weighted version are standard techniques to estimate the Diffusion Tensor (DT) from Diffusion Weighted Images (DWI). They require to linearize the problem by computing the logarithm of the DWI. For the single-coil Rician noise model it has been shown that this model does not introduce a significant bias, but for multiple array coils and parallel imaging, the noise cannot longer be modeled as Rician. As a result the validity of LS approaches is not assured. An analytical study of noise statistics for a multiple coil system is carried out, together with the Weighted LS formulation and noise analysis for this model. Results show that the bias in the computation of the components of the DT may be comparable to their variance in many cases, stressing the importance of unbiased filtering previous to DT estimation.

One known issue in Q-Ball imaging is the blurring in the radial integral defining the Orientation Distribution Function of fiber bundles, due to the computation of the Funk-Radon Transform (FRT). Three novel techniques to overcome this problem are presented, all of them based upon different assumptions about the behavior of the attenuation signal outside the sphere densely sampled from HARDI data sets. A systematic study with synthetic data has been carried out to show that the FRT blurring is not as important as the error introduced by some unrealistic assumptions, and only one of the three techniques (the one with the less restrictive assumption) improves the accuracy of Q-Balls.

We present a modulated gradient spin-echo method, which uses a train of sinusoidally shaped gradient pulses separated by 180 degrees radio-frequency (RF) pulses. The RF pulses efficiently refocus chemical shifts and de-phasing due to susceptibility differences, resulting in undistorted, high-resolution diffusion weighted spectra. This allows for the simultaneous spectral characterization of the diffusion of several molecular species with different chemical shifts. The technique is robust against susceptibility artifacts, field inhomogeneity and imperfections in the gradient generating equipment. The feasibility of the technique is demonstrated by measuring the diffusion of water, oil, and water-soluble salt in a highly concentrated water-in-oil emulsion. The diffusion of water and salt reveal precise information about the droplet size distribution below the mum-range. Common droplet size distribution explains both the data for water with finite long-range diffusion and the data for salt with negligible long-range diffusion. The results of water diffusion show that the technique is efficient in deconvolving the effects of molecular exchange between droplets and restricted diffusion within droplets. The effects of water exchange suggest that droplets of different sizes are uniformly distributed within the sample.