Publications by Year: 2010

A spectrum of brain-related disorders are nowadays known to manifest themselves in degradation of the integrity and connectivity of neural tracts in the white matter of the brain. Such damage tends to affect the pattern of water diffusion in the white matter—the information which can be quantified by diffusion MRI (dMRI). Unfortunately, practical implementation of dMRI still poses a number of challenges which hamper its wide-spread integration into regular clinical practice. Chief among these is the problem of long scanning times. In particular, in the case of High Angular Resolution Diffusion Imaging (HARDI), the scanning times are known to increase linearly with the number of diffusion-encoding gradients. In this research, we use the theory of compressive sampling (aka compressed sensing) to substantially reduce the number of diffusion gradients without compromising the informational content of HARDI signals. The experimental part of our study compares the proposed method with a number of alternative approaches, and shows that the former results in more accurate estimation of HARDI data in terms of the mean squared error.

Visualization and analysis of the micro-architecture of brain parenchyma by means of magnetic resonance imaging is nowadays believed to be one of the most powerful tools used for the assessment of various cerebral conditions as well as for understanding the intracerebral connectivity. Unfortunately, the conventional diffusion tensor imaging (DTI) used for estimating the local orientations of neural fibers is incapable of performing reliably in the situations when a voxel of interest accommodates multiple fiber tracts. In this case, a much more accurate analysis is possible using the high angular resolution diffusion imaging (HARDI) that represents local diffusion by its apparent coefficients measured as a discrete function of spatial orientations. In this note, a novel approach to enhancing and modeling the HARDI signals using multiresolution bases of spherical ridgelets is presented. In addition to its desirable properties of being adaptive, sparsifying, and efficiently computable, the proposed modeling leads to analytical computation of the orientation distribution functions associated with the measured diffusion, thereby providing a fast and robust analytical solution for q-ball imaging.

Recent advances in diffusion weighted MR imaging (dMRI) has made it a tool of choice for investigating white matter abnormalities of the brain and central nervous system. In this work, we design a system that detects abnormal features (biomarkers) of first-episode schizophrenia patients and then classifies them using these features. We use two different models of the dMRI data, namely, spherical harmonics and the two-tensor model. The algorithm works by first computing several diffusion measures from each model. An affine-invariant representation of each subject is then computed, thus avoiding the need for registration. This representation is used within a kernel based feature selection algorithm to determine the biomarkers that are statistically different between the two populations. Confirmation of how well these biomarkers identify each population is obtained by using several classifiers such as, k-nearest neighbors, Parzen window classifier, and support vector machines to separate 21 first-episode patients from 20 age-matched normal controls. Classification results using leave-many-out cross-validation scheme are given for each representation. This algorithm is a first step towards early detection of schizophrenia.

RATIONALE AND OBJECTIVES: The aim of this study is to compare two subjective methods for the identification of changes suggestive of early interstitial lung disease (ILD) on chest computed tomographic (CT) scans.

BACKGROUND: Recently, it has been shown that emphysematous destruction of the lung is associated with a decrease in the total number of terminal bronchioles. It is unknown whether a similar decrease is visible in the more proximal airways. We aimed to assess the relationships between proximal airway count, CT imaging measures of emphysema, and clinical prognostic factors in smokers, and to determine whether airway count predicts the BMI, airflow obstruction, dyspnea, and exercise capacity (BODE) index.

We present a fully automatic lung lobe segmentation algorithm that is effective in high resolution computed tomography (CT) datasets in the presence of confounding factors such as incomplete fissures (anatomical structures indicating lobe boundaries), advanced disease states, high body mass index (BMI), and low-dose scanning protocols. In contrast to other algorithms that leverage segmentations of auxiliary structures (esp. vessels and airways), we rely only upon image features indicating fissure locations. We employ a particle system that samples the image domain and provides a set of candidate fissure locations. We follow this stage with maximum a posteriori (MAP) estimation to eliminate poor candidates and then perform a post-processing operation to remove remaining noise particles. We then fit a thin plate spline (TPS) interpolating surface to the fissure particles to form the final lung lobe segmentation. Results indicate that our algorithm performs comparably to pulmonologist-generated lung lobe segmentations on a set of challenging cases.

OBJECTIVE: To assess the association of emphysema and airway disease assessed by volumetric computed tomography (CT) with exercise capacity in subjects with chronic obstructive pulmonary disease (COPD).

RATIONALE AND OBJECTIVES: To evaluate the relationship between measurements of lung volume (LV) on inspiratory/expiratory computed tomography (CT) scans, pulmonary function tests (PFT), and CT measurements of emphysema in individuals with chronic obstructive pulmonary disease. MATERIALS AND METHODS: Forty-six smokers (20 females and 26 males; age range 46-81 years), enrolled in the Lung Tissue Research Consortium, underwent PFT and chest CT at full inspiration and expiration. Inspiratory and expiratory LV values were automatically measured by open-source software, and the expiratory/inspiratory (E/I) ratio of LV was calculated. Mean lung density (MLD) and low attenuation area percent (

OBJECTIVE: The purpose of this study was to evaluate the relation between bronchial wall attenuation on thin-section CT images and airflow limitation in persons with chronic obstructive pulmonary disease. SUBJECTS AND METHODS: One hundred fourteen subjects (65 men, 49 women; age range, 56-74 years) enrolled in the National Lung Screening Trial underwent chest CT and prebronchodilation spirometry at a single institution. At CT, mean peak wall attenuation, wall area percentage, and luminal area were measured in the third, fourth, and fifth generations of the right B(1) and B(10) segmental bronchi. Correlations with forced expiratory volume in the first second of expiration (FEV(1)) expressed as percentage of predicted value were evaluated with Spearman’s rank correlation test.

RATIONALE AND OBJECTIVES: Pulmonary vascular alteration is one of the characteristic features of chronic obstructive pulmonary disease (COPD). Recent studies suggest that vascular alteration is closely related to endothelial dysfunction and may be further influenced by emphysema. However, the relationship between morphological alteration of small pulmonary vessels and the extent of emphysema has not been assessed in vivo. The objectives of this study are: to evaluate the correlation of total cross-sectional area (CSA) of small pulmonary vessels with the extent of emphysema and airflow obstruction using CT scans and to assess the difference of total CSA between COPD phenotypes. MATERIALS AND METHODS: We measured CSA less than 5 mm(2) and 5-10 mm(2), and calculated the percentage of the total CSA for the lung area (%CSA 5, and %CSA5-10, respectively) using CT scans in 191 subjects. The extent of emphysema (%LAA-950) was calculated, and the correlations of %CSA 5 and %CSA5-10 with %LAA-950 and results of pulmonary function tests (PFTs) were evaluated. The differences in %CSA between COPD phenotypes were also assessed.