Publications by Year: 2014

Magnetic resonance spectroscopic imaging (MRSI) is often used to estimate the concentration of several brain metabolites. Abnormalities in these concentrations can indicate specific pathology, which can be quite useful in understanding the disease mechanism underlying those changes. Due to higher concentration, metabolites such as N-acetylaspartate (NAA), Creatine (Cr) and Choline (Cho) can be readily estimated using standard Fourier transform techniques. However, metabolites such as Glutamate (Glu) and Glutamine (Gln) occur in significantly lower concentrations and their resonance peaks are very close to each other making it difficult to accurately estimate their concentrations (separately). In this work, we propose to use the theory of ’Spectral Zooming’ or high-resolution spectral analysis to separate the Glutamate and Glutamine peaks and accurately estimate their concentrations. The method works by estimating a unique power spectral density, which corresponds to the maximum entropy solution of a zero-mean stationary Gaussian process. We demonstrate our estimation technique on several physical phantom data sets as well as on in-vivo brain spectroscopic imaging data. The proposed technique is quite general and can be used to estimate the concentration of any other metabolite of interest.

RATIONALE: Muscle wasting in chronic obstructive pulmonary disease (COPD) is associated with a poor prognosis and is not readily assessed by measures of body mass index (BMI). BMI does not discriminate between relative proportions of adipose tissue and lean muscle and may be insensitive to early pathologic changes in body composition. Computed tomography (CT)-based assessments of the pectoralis muscles may provide insight into the clinical significance of skeletal muscles in smokers. OBJECTIVES: We hypothesized that objective assessment of the pectoralis muscle area on chest CT scans provides information that is clinically relevant and independent of BMI.

The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.

Emphysema has distinct and well-defined visually apparent CT patterns called centrilobular and panlobular emphysema. Existing studies concentrated on the classification of these patterns but they have not looked at the complete evolution of this disease as the destruction of lung parenchyma progresses from normal lung tissue to mild, moderate, and severe disease with complete effacement of the lung architecture. In this paper, we discretize this continuous process into five classes of increasing disease severity and construct a training set of 1161 CT patches. We exploit three solutions to this monotonic multi-class classification problem: a global rankSVM for ranking, hierarchical SVM for classification and a combination of these two, which we call a hierarchical rankSVM. Results showed that both hierarchical approaches were computationally efficient. The classification accuracies were slightly better for hierarchical SVM. However, in addition to classification, ranking approaches also provided a ranking of patterns, which can be utilized as a continuous disease progression score. In terms of the classification accuracy and ratio of pair-wise constraints satisfied, hierarchical rankSVM outperformed the global rankSVM.

RATIONALE AND OBJECTIVES: Computed tomography (CT) of the chest can be used to assess pectoralis muscle area (PMA) and subcutaneous adipose tissue (SAT) area. Adipose tissue content is associated with inflammatory mediators in chronic obstructive pulmonary disease (COPD) subjects. Based on gender differences in body composition, we aimed to assess the hypothesis that in subjects with COPD, the relationships between PMA, SAT, and blood biomarkers of inflammation differ by gender. MATERIALS AND METHODS: We compared chest CT measures of PMA and SAT on a single slice at aortic arch and supraesternal notch levels from 73 subjects (28 women) with COPD between genders. The relationships of PMA and SAT area to biomarkers were assessed using within-gender regression models.

RATIONALE AND OBJECTIVES: Asthma is associated with chronic airflow obstruction. Our goal was to assess the association of computed tomographic measures of airway wall volume and lumen volume with the FEV1 and chronic airflow obstruction in smokers with childhood-onset asthma. METHODS: We analyzed clinical, lung function, and volumetric computed tomographic airway volume data from 7,266 smokers, including 590 with childhood-onset asthma. Small wall volume and small lumen volume of segmental airways were defined as measures 1 SD below the mean. We assessed the association between small wall volume, small lumen volume, FEV1, and chronic airflow obstruction (post-bronchodilator FEV1/FVC ratio 0.7) using linear and logistic models. MEASUREMENTS AND MAIN RESULTS: Compared with subjects without childhood-onset asthma, those with childhood-onset asthma had smaller wall volume and lumen volume (P 0.0001) of segmental airways. Among subjects with childhood-onset asthma, those with the smallest wall volume and lumen volume had the lowest FEV1 and greatest odds of chronic airflow obstruction. A similar tendency was seen in those without childhood-onset asthma. When comparing these two groups, both small wall volume and small lumen volume were more strongly associated with FEV1 and chronic airflow obstruction among subjects with childhood-asthma in multivariate models. CONCLUSION: In smokers with childhood-onset asthma, smaller airways are associated with reduced lung function and chronic airflow obstruction. Clinical trial registered with www.clinicaltrials.gov (NCT00608764).

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is characterized by marked phenotypic heterogeneity. Most previous studies have focused on COPD subjects with FEV1 

RATIONALE: The demographic, physiological, and computed tomography (CT) features associated with pneumothorax in smokers with and without chronic obstructive pulmonary disease (COPD) are not clearly defined. OBJECTIVES: We evaluated the hypothesis that pneumothorax in smokers is associated with male sex, tall and thin stature, airflow obstruction, and increased total and subpleural emphysema. METHODS: The study included smokers with and without COPD from the COPDGene Study, with quantitative chest CT analysis. Pleural-based emphysema was assessed on the basis of local histogram measures of emphysema. Pneumothorax history was defined by subject self-report. MEASUREMENTS AND MAIN RESULTS: Pneumothorax was reported in 286 (3.2%) of 9,062 participants. In all participants, risk of prior pneumothorax was significantly higher in men (odds ratio [OR], 1.55; 95% confidence interval [CI], 1.08-2.22) and non-Hispanic white subjects (OR, 1.90; 95% CI, 1.34-2.69). Risk of prior pneumothorax was associated with increased percent CT emphysema in all participants and participants with COPD (OR, 1.04 for each 1% increase in emphysema; 95% CI, 1.03-1.06). Increased pleural-based emphysema was independently associated with risk of past pneumothorax in all participants (OR, 1.05 for each 1% increase; 95% CI, 1.01-1.10). In smokers with normal spirometry, risk of past pneumothorax was associated with non-Hispanic white race and lifetime smoking intensity (OR, 1.20 for every 10 pack-years; 95% CI, 1.09-1.33). CONCLUSIONS: Among smokers, pneumothorax is associated with male sex, non-Hispanic white race, and increased percentage of total and subpleural CT emphysema. Pneumothorax was not independently associated with height or lung function, even in participants with COPD. Clinical trial registered with www.clinicaltrials.gov (NCT00608764).

RATIONALE: Bronchodilator responsiveness (BDR) is a common but variable phenomenon in COPD. The CT characteristics of airway dimensions that differentiate COPD subjects with BDR from those without BDR have not been well described. We aimed to assess airway dimensions in COPD subjects with and without BDR.

The main contribution of this work is a framework to register anatomical structures characterized as a point set where each point has an associated symmetric matrix. These matrices can represent problem-dependent characteristics of the registered structure. For example, in airways, matrices can represent the orientation and thickness of the structure. Our framework relies on a dense tensor field representation which we implement sparsely as a kernel mixture of tensor fields. We equip the space of tensor fields with a norm that serves as a similarity measure. To calculate the optimal transformation between two structures we minimize this measure using an analytical gradient for the similarity measure and the deformation field, which we restrict to be a diffeomorphism. We illustrate the value of our tensor field model by comparing our results with scalar and vector field based models. Finally, we evaluate our registration algorithm on synthetic data sets and validate our approach on manually annotated airway trees.