Publications

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.

The National Alliance for Medical Image Computing (NA-MIC), is a multi-institutional, interdisciplinary community of researchers, who share the recognition that modern health care demands improved technologies to ease suffering and prolong productive life. Organized under the National Centers for Biomedical Computing 7 years ago, the mission of NA-MIC is to implement a robust and flexible open-source infrastructure for developing and applying advanced imaging technologies across a range of important biomedical research disciplines. A measure of its success, NA-MIC is now applying this technology to diseases that have immense impact on the duration and quality of life: cancer, heart disease, trauma, and degenerative genetic diseases. The targets of this technology range from group comparisons to subject-specific analysis.

INTRODUCTION: Higher cognitive functioning is mediated by frontal-subcortical cognitive and limbic feedback sub-loops. The thalamo-cortical projection through the anterior limb of the internal capsule (ALIC) serves as the final step in these feedback sub-loops. We evaluated abnormalities in the ALIC fiber tract in schizophrenia using both structural MRI and diffusion tensor imaging (DTI). METHODS: 20 chronic schizophrenia patients and 22 male, normal controls group matched for handedness, age, and parental SES, underwent structural and DTI brain imaging on a 1.5 Tesla GE system. We manually measured ALIC volume normalized for intracranial contents (ICC) using structural brain images and then registered these high resolution structural brain scan derived ALIC label maps to DTI space allowing for the measurement in the ALIC of diffusion indices including, fractional anisotropy (FA) mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD). RESULTS: We found in the ALIC of chronic schizophrenia subjects, compared with normal controls, bilaterally lower FA and bilaterally higher RD, but no differences in AD, MD, or relative volume. Cognitive correlations in schizophrenia patients showed, in particular, that higher left ALIC FA correlated positively with better verbal and nonverbal declarative/episodic memory performance. DISCUSSION: Using a novel approach to assess both diffusion and volume measures in the ALIC in schizophrenia, we found abnormalities in measures of diffusion, but not volume, supporting their importance as sensitive indices of abnormalities in white matter fiber bundles in schizophrenia. Our findings also support the role of ALIC white matter tract FA abnormalities in declarative/episodic memory in schizophrenia.

It is widely accepted that abnormalities in the frontal area of the brain underpin the pathophysiology of obsessive-compulsive disorder (OCD). Fundamental to this investigation is the delineation of frontal white matter tracts including dorsal and ventral frontal projections of interhemispheric connections. While previous investigations of OCD have examined the dorsal and ventral frontal regions, the corresponding callosal connections have not been investigated, despite their importance. We recruited twenty patients with OCD (15 drug-na ıve and 5 currently unmedicated) and demographically similar healthy controls, and conducted fiber tractography and post hoc quantitative analysis using diffusion tensor imaging. We extracted fractional anisotropy (FA) of the fronto-callosal fibers along the entire length of the tract. Function-specific [by the Brodmann area region-of-interest (ROI) approach] and region-specific (by the length-parameterization approach) tracts were defined. In addition, we devised a new index of dorsal-ventral imbalance (DVII) of fiber integrity. Significant FA decreases were observed in orbitofrontal and dorsolateral prefrontal projections of the corpus callosum (P 0.05, false discovery rate-corrected) with higher function/region sensitivity than voxel-based or ROI-based approaches. Importantly, OCD patients also exhibited significantly higher ventral-greater-than-dorsal asymmetry of FA values than normal controls (P 0.05, FDR-corrected). This study is the first to investigate fiber integrity in the dorsal/ventral frontal parts of the callosal tractography in unmedicated OCD patients. Using a more quantitative method in terms of functional and regional specificity than previous studies, we report abnormalities in interhemispheric connectivity of both dorsal and ventral networks in the pathophysiology of OCD.