Publications

OBJECT: Concussion, or mild traumatic brain injury (mTBI), is a commonly occurring sports-related injury, especially in contact sports such as hockey. Cerebral microbleeds (CMBs), which appear as small, hypointense lesions on T$_2$*-weighted images, can result from TBI. The authors use susceptibility-weighted imaging (SWI) to automatically detect small hypointensities that may be subtle signs of chronic and acute damage due to both subconcussive and concussive injury. The goal was to investigate how the burden of these hypointensities changes over time, over a playing season, and postconcussion, in comparison with subjects who did not suffer a medically observed and diagnosed concussion. METHODS: Images were obtained in 45 university-level adult male and female ice hockey players before and after a single Canadian Interuniversity Sports season. In addition, 11 subjects (5 men and 6 women) underwent imaging at 72 hours, 2 weeks, and 2 months after concussion. To identify subtle changes in brain tissue and potential CMBs, nonvessel clusters of hypointensities on SWI were automatically identified, and a hypointensity burden index was calculated for all subjects at the beginning of the season (BOS), the end of the season (EOS), and at postconcussion time points (where applicable). RESULTS: A statistically significant increase in the hypointensity burden, relative to the BOS, was observed for male subjects with concussions at the 2-week postconcussion time point. A smaller, nonsignificant rise in the burden for female subjects with concussions was also observed within the same time period. There were no significant changes in burden for nonconcussed subjects of either sex between the BOS and EOS time points. However, there was a statistically significant difference in the burden between male and female subjects in the nonconcussed group at both the BOS and EOS time points, with males having a higher burden. CONCLUSIONS: This method extends the utility of SWI from the enhancement and detection of larger (> 5 mm) CMBs, which are often observed in more severe cases of TBI, to cases involving smaller lesions in which visual detection of injury is difficult. The hypointensity burden metric proposed here shows statistically significant changes over time in the male subjects. A smaller, nonsignificant increase in the burden metric was observed in the female subjects.

BACKGROUND: The study of individuals at clinical high risk (CHR) for psychosis provides an important opportunity for unraveling pathological mechanisms underlying schizophrenia and related disorders. A small number of diffusion tensor magnetic resonance imaging (DTI) studies in CHR samples have yielded anatomically inconsistent results. The present study is the first to apply tract-based spatial statistics (TBSS) to perform a whole-brain DTI analysis in CHR subjects. METHODS: A total of 28 individuals meeting CHR criteria and 34 healthy controls underwent DTI. TBSS was used for a group comparison of fractional anisotropy (FA), as well as axial, radial, and mean diffusivity (AD, RD, and MD). Conversion to psychosis was monitored during a mean follow-up period of 12.3 months. RESULTS: The rate of conversion to psychosis was relatively low (4%). TBSS revealed increased MD in several clusters in the right hemisphere, most notably in the superior longitudinal fasciculus (SLF), posterior corona radiata, and corpus callosum (splenium and body). Increased RD was restricted to a smaller area in the posterior parietal lobe. CONCLUSION: We present further evidence that white matter microstructure is abnormal in CHR individuals, even in a sample in which the vast majority do not transition to psychosis over the following year. In accord with previous studies on CHR individuals and patients with early-onset schizophrenia, our findings suggest an important pathological role for the parietal lobe and especially the SLF. The latter is known to undergo particularly dynamic microstructural changes during adolescence and early adulthood, a critical phase for the development of psychotic illness.

We present a novel airway labeling algorithm based on a Hidden Markov Tree Model (HMTM). We obtain a collection of discrete points along the segmented airway tree using particles sampling [1] and establish topology using Kruskal’s minimum spanning tree algorithm. Following this, our HMTM algorithm probabilistically assigns labels to each point. While alternative methods label airway branches out to the segmental level, we describe a general method and demonstrate its performance out to the subsubsegmental level (two generations further than previously published approaches). We present results on a collection of 25 computed tomography (CT) datasets taken from a Chronic Obstructive Pulmonary Disease (COPD) study.

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