Publications by Year: 2012

A method for automated location of shape differences in diseased anatomical structures via high resolution biomedical atlases annotated with labels from formal ontologies is described. In particular, a high resolution magnetic resonance image of the myocardium of the human left ventricle was segmented and annotated with structural terms from an extracted subset of the Foundational Model of Anatomy ontology. The atlas was registered to the end systole template of a previous study of left ventricular remodeling in cardiomyopathy using a diffeomorphic registration algorithm. The previous study used thresholding and visual inspection to locate a region of statistical significance which distinguished patients with ischemic cardiomyopathy from those with nonischemic cardiomyopathy. Using semantic technologies and the deformed annotated atlas, this location was more precisely found. Although this study used only a cardiac atlas, it provides a proof-of-concept that ontologically labeled biomedical atlases of any anatomical structure can be used to automate location-based inferences.

The validity of parametric functional magnetic resonance imaging (fMRI) analysis has only been reported for simulated data. Recent advances in computer science and data sharing make it possible to analyze large amounts of real fMRI data. In this study, 1484 rest datasets have been analyzed in SPM8, to estimate true familywise error rates. For a familywise significance threshold of 5%, significant activity was found in 1%-70% of the 1484 rest datasets, depending on repetition time, paradigm and parameter settings. This means that parametric significance thresholds in SPM both can be conservative or very liberal. The main reason for the high familywise error rates seems to be that the global AR(1) auto correlation correction in SPM fails to model the spectra of the residuals, especially for short repetition times. The findings that are reported in this study cannot be generalized to parametric fMRI analysis in general, other software packages may give different results. By using the computational power of the graphics processing unit (GPU), the 1484 rest datasets were also analyzed with a random permutation test. Significant activity was then found in 1%-19% of the datasets. These findings speak to the need for a better model of temporal correlations in fMRI timeseries.

Heart wall myofibers wind as helices around the ventricles, strengthening them in a manner analogous to the reinforcement of concrete cylindrical columns by spiral steel cables [Richart FE, et al. (1929) Univ of Illinois, Eng Exp Stn Bull 190]. A multitude of such fibers, arranged smoothly and regularly, contract and relax as an integrated functional unit as the heart beats. To orchestrate this motion, fiber tangling must be avoided and pumping should be efficient. Current models of myofiber orientation across the heart wall suggest groupings into sheets or bands, but the precise geometry of bundles of myofibers is unknown. Here we show that this arrangement takes the form of a special minimal surface, the generalized helicoid [Blair DE, Vanstone JR (1978) Minimal Submanifolds and Geodesics 13-16], closing the gap between individual myofibers and their collective wall structure. The model holds across species, with a smooth variation in its three curvature parameters within the myocardial wall providing tight fits to diffusion magnetic resonance images from the rat, the dog, and the human. Mathematically it explains how myofibers are bundled in the heart wall while economizing fiber length and optimizing ventricular ejection volume as they contract. The generalized helicoid provides a unique foundation for analyzing the fibrous composite of the heart wall and should therefore find applications in heart tissue engineering and in the study of heart muscle diseases.

PURPOSE: To develop and evaluate image registration methodology for automated re-identification of tumor-suspicious foci from preprocedural MR exams during MR-guided transperineal prostate core biopsy. MATERIALS AND METHODS: A hierarchical approach for automated registration between planning and intra-procedural T2-weighted prostate MRI was developed and evaluated on the images acquired during 10 consecutive MR-guided biopsies. Registration accuracy was quantified at image-based landmarks and by evaluating spatial overlap for the manually segmented prostate and sub-structures. Registration reliability was evaluated by simulating initial mis-registration and analyzing the convergence behavior. Registration precision was characterized at the planned biopsy targets. RESULTS: The total computation time was compatible with a clinical setting, being at most 2 min. Deformable registration led to a significant improvement in spatial overlap of the prostate and peripheral zone contours compared with both rigid and affine registration. Average in-slice landmark registration error was 1.3 ± 0.5 mm. Experiments simulating initial mis-registration resulted in an estimated average capture range of 6 mm and an average in-slice registration precision of ±0.3 mm. CONCLUSION: Our registration approach requires minimum user interaction and is compatible with the time constraints of our interventional clinical workflow. The initial evaluation shows acceptable accuracy, reliability and consistency of the method.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community’s attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome.

It has been suggested that some cases of schizophrenia may be caused by an interaction between physiological risk factors and exposure to certain neurotropic infectious agents such as Herpes Simplex Virus type 1 (HSV1). This study investigated whether HSV1 exposure was associated with structural brain abnormalities in individuals who, because of genetic or other factors, were deemed at ultra high risk (UHR) of developing psychosis. Twenty-five UHR individuals with a history of HSV1 exposure (HSV1+), 33 UHR participants without a history of HSV1 exposure (HSV1-) and 19 healthy controls participated in the study. All participants underwent a T1-weighted structural MRI scan, and HSV1 exposure was determined based on the presence of IgG class antibodies in the blood serum. Voxel based morphometry revealed that the HSV1+ participants exhibited volumetric gray matter reductions in the cuneus, relative to both the HSV1—and healthy control participants (p

Chronic Traumatic Encephalopathy (CTE) is a neurodegenerative disease thought to be caused, at least in part, by repetitive brain trauma, including concussive and subconcussive injuries. It is thought to result in executive dysfunction, memory impairment, depression and suicidality, apathy, poor impulse control, and eventually dementia. Beyond repetitive brain trauma, the risk factors for CTE remain unknown. CTE is neuropathologically characterized by aggregation and accumulation of hyperphosphorylated tau and TDP-43. Recent postmortem findings indicate that CTE may affect a broader population than was initially conceptualized, particularly contact sport athletes and those with a history of military combat. Given the large population that could potentially be affected, CTE may represent an important issue in public health. Although there has been greater public awareness brought to the condition in recent years, there are still many research questions that remain. Thus far, CTE can only be diagnosed post-mortem. Current research efforts are focused on the creation of clinical diagnostic criteria, finding objective biomarkers for CTE, and understanding the additional risk factors and underlying mechanism that causes the disease. This review examines research to date and suggests future directions worthy of exploration.

This article investigates the suitability of local intensity distributions to analyze six emphysema classes in 342 CT scans obtained from 16 sites hosting scanners by 3 vendors and a total of 9 specific models in subjects with Chronic Obstructive Pulmonary Disease (COPD). We propose using kernel density estimation to deal with the inherent sparsity of local intensity histograms obtained from scarcely populated regions of interest. We validate our approach by leave-one-subject-out classification experiments and full-lung analyses. We compare our results with recently published LBP texture-based methodology. We demonstrate the efficacy of using intensity information alone in multi-scanner cohorts, which is a simpler, more intuitive approach.

The evaluation of analysis methods for diffusion tensor imaging (DTI) remains challenging due to the lack of gold standards and validation frameworks. Significant work remains in developing metrics for comparing fiber bundles generated from streamline tractography. We propose a set of volumetric and tract oriented measures for evaluating tract differences. The different methods developed for this assessment work are: an overlap measurement, a point cloud distance and a quantification of the diffusion properties at similar locations between fiber bundles. The application of the measures in this paper is a comparison of atlas generated tractography to tractography generated in individual images. For the validation we used a database of 37 subject DTIs, and applied the measurements on five specific fiber bundles: uncinate, cingulum (left and right for both bundles) and genu. Each measurments is interesting for specific use: the overlap measure presents a simple and comprehensive metric but is sensitive to partial voluming and does not give consistent values depending on the bundle geometry. The point cloud distance associated with a quantile interpretation of the distribution gives a good intuition of how close and similar the bundles are. Finally, the functional difference is useful for a comparison of the diffusion properties since it is the focus of many DTI analysis to compare scalar invariants. The comparison demonstrated reasonable similarity of results. The tract difference measures are also applicable to comparison of tractography algorithms, quality control, reproducibility studies, and other validation problems.