Engineering Commons^™

Atlas-Based Shared-Boundary Deformable Multi-Surface Models Through Multi-Material And Two-Manifold Dual Contouring, Tanweer Rashid, Sharmin Sultana, Mallar Chakravarty, Michel Albert Audette

Electrical & Computer Engineering Faculty Publications

This paper presents a multi-material dual “contouring” method used to convert a digital 3D voxel-based atlas of basal ganglia to a deformable discrete multi-surface model that supports surgical navigation for an intraoperative MRI-compatible surgical robot, featuring fast intraoperative deformation computation. It is vital that the final surface model maintain shared boundaries where appropriate so that even as the deep-brain model deforms to reflect intraoperative changes encoded in ioMRI, the subthalamic nucleus stays in contact with the substantia nigra, for example, while still providing a significantly sparser representation than the original volumetric atlas consisting of hundreds of millions of voxels. The …

Post-Acquisition Processing Confounds In Brain Volumetric Quantification Of White Matter Hyperintensities, Ahmed A. Bahrani, Omar M. Al-Janabi, Erin L. Abner, Shoshana H. Bardach, Richard J. Kryscio, Donna M. Wilcock, Charles D. Smith, Gregory A. Jicha

Neurology Faculty Publications

BACKGROUND: Disparate research sites using identical or near-identical magnetic resonance imaging (MRI) acquisition techniques often produce results that demonstrate significant variability regarding volumetric quantification of white matter hyperintensities (WMH) in the aging population. The sources of such variability have not previously been fully explored.

NEW METHOD: 3D FLAIR sequences from a group of randomly selected aged subjects were analyzed to identify sources-of-variability in post-acquisition processing that can be problematic when comparing WMH volumetric data across disparate sites. The methods developed focused on standardizing post-acquisition protocol processing methods to develop a protocol with less than 0.5% inter-rater variance.

RESULTS: A series …

Distinct Patterns Of Default Mode And Executive Control Network Circuitry Contribute To Present And Future Executive Function In Older Adults, Christopher A. Brown, Frederick A. Schmitt, Charles D. Smith, Brian T. Gold

Neuroscience Faculty Publications

Executive function (EF) performance in older adults has been linked with functional and structural profiles within the executive control network (ECN) and default mode network (DMN), white matter hyperintensities (WMH) burden and levels of Alzheimer's disease (AD) pathology. Here, we simultaneously explored the unique contributions of these factors to baseline and longitudinal EF performance in older adults. Thirty-two cognitively normal (CN) older adults underwent neuropsychological testing at baseline and annually for three years. Neuroimaging and AD pathology measures were collected at baseline. Separate linear regression models were used to determine which of these variables predicted composite EF scores at baseline …

A Pilot Study Identifying Brain-Targeting Adaptive Immunity In Pediatric Extracorporeal Membrane Oxygenation Patients With Acquired Brain Injury, Sterling B. Ortega, Poornima Pandiyan, Jana Windsor, Vanessa O. Torres, Uma M. Selvaraj, Amy Lee, Michael Morriss, Fenghua Tian, Lakshmi Raman, Ann M. Stowe

Neurology Faculty Publications

OBJECTIVES: Extracorporeal membrane oxygenation provides short-term cardiopulmonary life support, but is associated with peripheral innate inflammation, disruptions in cerebral autoregulation, and acquired brain injury. We tested the hypothesis that extracorporeal membrane oxygenation also induces CNS-directed adaptive immune responses which may exacerbate extracorporeal membrane oxygenation-associated brain injury.

DESIGN: A single center prospective observational study.

SETTING: Pediatric and cardiac ICUs at a single tertiary care, academic center.

PATIENTS: Twenty pediatric extracorporeal membrane oxygenation patients (0-14 yr; 13 females, 7 males) and five nonextracorporeal membrane oxygenation Pediatric Logistic Organ Dysfunction score matched patients.

INTERVENTIONS: None.

MEASUREMENTS AND MAIN RESULTS: Venous blood samples were …

A Comparative Study Of Two Prediction Models For Brain Tumor Progression, Deqi Zhou, Loc Tran, Jihong Wang, Jiang Li, Karen O. Egiazarian (Ed.), Sos S. Agaian (Ed.), Atanas P. Gotchev (Ed.)

Electrical & Computer Engineering Faculty Publications

MR diffusion tensor imaging (DTI) technique together with traditional T1 or T2 weighted MRI scans supplies rich information sources for brain cancer diagnoses. These images form large-scale, high-dimensional data sets. Due to the fact that significant correlations exist among these images, we assume low-dimensional geometry data structures (manifolds) are embedded in the high-dimensional space. Those manifolds might be hidden from radiologists because it is challenging for human experts to interpret high-dimensional data. Identification of the manifold is a critical step for successfully analyzing multimodal MR images.

We have developed various manifold learning algorithms (Tran et al. 2011; Tran et al. …

The Multimodal Brain Tumor Image Segmentation Benchmark (Brats), Bjoern H. Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Khan M. Iftekharuddin, Syed M.S. Reza

Electrical & Computer Engineering Faculty Publications

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low-and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions …

Prediction Of Brain Tumor Progression Using Multiple Histogram Matched Mri Scans, Debrup Banerjee, Loc Tran, Jiang Li, Yuzhong Shen, Frederic Mckenzie, Jihong Wang, Ronald M. Summers (Ed.), Bram Van Ginneken (Ed.)

Electrical & Computer Engineering Faculty Publications

In a recent study [1], we investigated the feasibility of predicting brain tumor progression based on multiple MRI series and we tested our methods on seven patients' MRI images scanned at three consecutive visits A, B and C. Experimental results showed that it is feasible to predict tumor progression from visit A to visit C using a model trained by the information from visit A to visit B. However, the trained model failed when we tried to predict tumor progression from visit B to visit C, though it is clinically more important. Upon a closer look at the MRI scans …