Calculation of Scalar Isosurface Area and Applications

The problem of calculating iso-surface statistics in turbulent flows is interesting for a number of reasons, some of them being combustion modeling, entrainment through turbulent/non-turbulent interfaces, calculating mass flux through iso-scalar surfaces and mapping of scalar fields. A fundamental effect of fuid turbulence is to wrinkle scalar iso-surfaces. A review of the literature shows that iso-surface calculations have primarily been done with geometric methods, which have challenges when used to calculate surfaces that have high complexity, such as in turbulent flows. In this thesis, we propose an alternative integral method and test it against analytical solutions. We present a parallelized algorithm and code to enable in-simulation calculation of isosurface area. We then use this code to calculate area statistics for data obtained from Direct Numerical Simulations and make predictions about the variation of the iso-scalar surface area with Taylor Peclet numbers between 9.8 and 4429 and Taylor Reynolds numbers between 98 and 633

Dynamic Thermal Imaging for Intraoperative Monitoring of Neuronal Activity and Cortical Perfusion

Neurosurgery is a demanding medical discipline that requires a complex interplay of several neuroimaging techniques. This allows structural as well as functional information to be recovered and then visualized to the surgeon. In the case of tumor resections this approach allows more fine-grained differentiation of healthy and pathological tissue which positively influences the postoperative outcome as well as the patient's quality of life. In this work, we will discuss several approaches to establish thermal imaging as a novel neuroimaging technique to primarily visualize neural activity and perfusion state in case of ischaemic stroke. Both applications require novel methods for data-preprocessing, visualization, pattern recognition as well as regression analysis of intraoperative thermal imaging. Online multimodal integration of preoperative and intraoperative data is accomplished by a 2D-3D image registration and image fusion framework with an average accuracy of 2.46 mm. In navigated surgeries, the proposed framework generally provides all necessary tools to project intraoperative 2D imaging data onto preoperative 3D volumetric datasets like 3D MR or CT imaging. Additionally, a fast machine learning framework for the recognition of cortical NaCl rinsings will be discussed throughout this thesis. Hereby, the standardized quantification of tissue perfusion by means of an approximated heating model can be achieved. Classifying the parameters of these models yields a map of connected areas, for which we have shown that these areas correlate with the demarcation caused by an ischaemic stroke segmented in postoperative CT datasets. Finally, a semiparametric regression model has been developed for intraoperative neural activity monitoring of the somatosensory cortex by somatosensory evoked potentials. These results were correlated with neural activity of optical imaging. We found that thermal imaging yields comparable results, yet doesn't share the limitations of optical imaging. In this thesis we would like to emphasize that thermal imaging depicts a novel and valid tool for both intraoperative functional and structural neuroimaging

Proceedings, MSVSCC 2013

Proceedings of the 7th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 11, 2013 at VMASC in Suffolk, Virginia

Blood Vessel Segmentation and shape analysis for quantification of Coronary Artery Stenosis in CT Angiography

This thesis presents an automated framework for quantitative vascular shape analysis of the coronary arteries, which constitutes an important and fundamental component of an automated image-based diagnostic system. Firstly, an automated vessel segmentation algorithm is developed to extract the coronary arteries based on the framework of active contours. Both global and local intensity statistics are utilised in the energy functional calculation, which allows for dealing with non-uniform brightness conditions, while evolving the contour towards to the desired boundaries without being trapped in local minima. To suppress kissing vessel artifacts, a slice-by-slice correction scheme, based on multiple regions competition, is proposed to identify and track the kissing vessels throughout the transaxial images of the CTA data. Based on the resulting segmentation, we then present a dedicated algorithm to estimate the geometric parameters of the extracted arteries, with focus on vessel bifurcations. In particular, the centreline and associated reference surface of the coronary arteries, in the vicinity of arterial bifurcations, are determined by registering an elliptical cross sectional tube to the desired constituent branch. The registration problem is solved by a hybrid optimisation method, combining local greedy search and dynamic programming, which ensures the global optimality of the solution and permits the incorporation of any hard constraints posed to the tube model within a natural and direct framework. In contrast with conventional volume domain methods, this technique works directly on the mesh domain, thus alleviating the need for image upsampling. The performance of the proposed framework, in terms of efficiency and accuracy, is demonstrated on both synthetic and clinical image data. Experimental results have shown that our techniques are capable of extracting the major branches of the coronary arteries and estimating the related geometric parameters (i.e., the centreline and the reference surface) with a high degree of agreement to those obtained through manual delineation. Particularly, all of the major branches of coronary arteries are successfully detected by the proposed technique, with a voxel-wise error at 0.73 voxels to the manually delineated ground truth data. Through the application of the slice-by-slice correction scheme, the false positive metric, for those coronary segments affected by kissing vessel artifacts, reduces from 294% to 22.5%. In terms of the capability of the presented framework in defining the location of centrelines across vessel bifurcations, the mean square errors (MSE) of the resulting centreline, with respect to the ground truth data, is reduced by an average of 62.3%, when compared with initial estimation obtained using a topological thinning based algorithm

IMPROVED IMAGE QUALITY IN CONE-BEAM COMPUTED TOMOGRAPHY FOR IMAGE-GUIDED INTERVENTIONS

In the past few decades, cone-beam computed tomography (CBCT) emerged as a rapidly developing imaging modality that provides single rotation 3D volumetric reconstruction with sub-millimeter spatial resolution. Compared to the conventional multi-detector CT (MDCT), CBCT exhibited a number of characteristics that are well suited to applications in image-guided interventions, including improved mechanical simplicity, higher portability, and lower cost. Although the current generation of CBCT has shown strong promise for high-resolution and high-contrast imaging (e.g., visualization of bone structures and surgical instrumentation), it is often believed that CBCT yields inferior contrast resolution compared to MDCT and is not suitable for soft-tissue imaging. Aiming at expanding the utility of CBCT in image-guided interventions, this dissertation concerns the development of advanced imaging systems and algorithms to tackle the challenges of soft-tissue contrast resolution. The presented material includes work encompassing: (i) a comprehensive simulation platform to generate realistic CBCT projections (e.g., as training data for deep learning approaches); (ii) a new projection domain statistical noise model to improve the noise-resolution tradeoff in model-based iterative reconstruction (MBIR); (iii) a novel method to avoid CBCT metal artifacts by optimization of the source-detector orbit; (iv) an integrated software pipeline to correct various forms of CBCT artifacts (i.e., lag, glare, scatter, beam hardening, patient motion, and truncation); (v) a new 3D reconstruction method that only reconstructs the difference image from the image prior for use in CBCT neuro-angiography; and (vi) a novel method for 3D image reconstruction (DL-Recon) that combines deep learning (DL)-based image synthesis network with physics-based models based on Bayesian estimation of the statical uncertainty of the neural network. Specific clinical challenges were investigated in monitoring patients in the neurological critical care unit (NCCU) and advancing intraoperative soft-tissue imaging capability in image-guided spinal and intracranial neurosurgery. The results show that the methods proposed in this work substantially improved soft-tissue contrast in CBCT. The thesis demonstrates that advanced imaging approaches based on accurate system models, novel artifact reduction methods, and emerging 3D image reconstruction algorithms can effectively tackle current challenges in soft-tissue contrast resolution and expand the application of CBCT in image-guided interventions

Light localization in biological media

Light in natural and biological media is known as freely diffusing. When light undergoes multiple scattering through inhomogeneous dielectric biomacromolecules, interference is ignored. If scattered waves return to their origin points along the time-reversed paths for constructive interference, outgoing waves can be forbidden, occasionally being on-resonance or off-resonance with Anderson localized modes. However, such a phenomenon in optics requires high-refractive-index contrasts, which intrinsically do not exist in organic molecules (e.g. proteins, lipids, carbohydrates, and nucleic acids). Here we predict and show experimentally that the long-standing perception of diffusive nature of light in biological media is broken by an exquisite distribution of silk protein structures produced from Bombyx mori (silkworm) and layered aragonite structures produced from Haliotis fulgens (abalone). We demonstrate how optical transmission quantities (e.g. light flux in transmission channels) are analyzed satisfying critical values of the Anderson localization transition. We find that the size of modes is smaller than specimen sizes and that the statistics of modes, decomposed from excitation at the gain-loss equilibrium, clearly differentiates silk and nacre from diffusive structures sharing the microscopic morphological similarity. This explains how the dimension, the size, and the distribution of disordered biological nanostructures result in enhanced light-matter interactions, in spite of low refractive index contrasts of constituent materials. As wave localization is universal, the presented results of electromagnetic waves will allow us to extend insight into electronic and mechanical waves in biological systems. Importantly, such optical resonances are extremely sensitive to subtle nanoscale perturbations, and thus can be implemented to biosensing platforms

3D Scene Reconstruction with Micro-Aerial Vehicles and Mobile Devices

Scene reconstruction is the process of building an accurate geometric model of one\u27s environment from sensor data. We explore the problem of real-time, large-scale 3D scene reconstruction in indoor environments using small laser range-finders and low-cost RGB-D (color plus depth) cameras. We focus on computationally-constrained platforms such as micro-aerial vehicles (MAVs) and mobile devices. These platforms present a set of fundamental challenges - estimating the state and trajectory of the device as it moves within its environment and utilizing lightweight, dynamic data structures to hold the representation of the reconstructed scene. The system needs to be computationally and memory-efficient, so that it can run in real time, onboard the platform. In this work, we present three scene reconstruction systems. The first system uses a laser range-finder and operates onboard a quadrotor MAV. We address the issues of autonomous control, state estimation, path-planning, and teleoperation. We propose the multi-volume occupancy grid (MVOG) - a novel data structure for building 3D maps from laser data, which provides a compact, probabilistic scene representation. The second system uses an RGB-D camera to recover the 6-DoF trajectory of the platform by aligning sparse features observed in the current RGB-D image against a model of previously seen features. We discuss our work on camera calibration and the depth measurement model. We apply the system onboard an MAV to produce occupancy-based 3D maps, which we utilize for path-planning. Finally, we present our contributions to a scene reconstruction system for mobile devices with built-in depth sensing and motion-tracking capabilities. We demonstrate reconstructing and rendering a global mesh on the fly, using only the mobile device\u27s CPU, in very large (300 square meter) scenes, at a resolutions of 2-3cm. To achieve this, we divide the scene into spatial volumes indexed by a hash map. Each volume contains the truncated signed distance function for that area of space, as well as the mesh segment derived from the distance function. This approach allows us to focus computational and memory resources only in areas of the scene which are currently observed, as well as leverage parallelization techniques for multi-core processing

Determination of intrinsic physical properties of porous media by applying Bayesian Optimization to inverse problems in Laplace NMR relaxometry

Nuclear magnetic resonance (NMR) longitudinal (T1) and transverse (T2) relaxation time distributions are widely used for the characterization of porous media. Subject to simplifying assumptions predictions about pore size, permeability and fluid content can be made. Numerical forward models based on high-resolution images are employed to naturally incorporate structural heterogeneity and diffusive motion without limiting assumptions, offering alternate interpretation approaches. Extracting the required multiple intrinsic parameters of the system poses an ill-conditioned inverse problem where multiple scales are covered by the underlying microstructure. Three general and robust inverse solution workflows (ISW) utilizing Bayesian optimization for the inverse problem of estimation of intrinsic physical quantities from the integration of pore-scale forward modeling and experimental measurements of macroscopic system responses are developed. A single-task ISW identifies multiple intrinsic properties for a single core by minimization of the deviation between simulated and measured T2 distributions. A multi-task ISW efficiently identifies the same set of unknown quantities for different cores by leveraging information from completed tasks using transfer learning. Finally, a dual-task ISW inspired by the multi-task ISW incorporates transfer learning for the simultaneous statistical modeling of T1 and T2 distributions, providing robust estimates of T1 and T2 intrinsic properties. A multi-modal search strategy comprising the multi-start L-BFGS-B optimizer and the social-learning particle swarm optimizer, and a multi-modal solution analysis procedure are applied in these workflows for the identification of non-unique solution sets. The performance of the single-task ISW is demonstrated on T2 relaxation responses of a Bentheimer sandstone, extracting three physical parameters simultaneously, and the results facilitate the multi-task ISW to study the spatial variability of the three physical quantities of three Bentheimer sandstone cored from two different blocks. The performance of the dual-task ISW is demonstrated on the identification of the five physical quantities with two extra T1 related unknowns. The effect of SNR on the identified parameter values is demonstrated. Inverse solution workflows enable the use of classical interpretation techniques and local analysis of responses based on numerical simulation

First-principles investigation of carbon-based nanomaterials for supercapacitors

Supercapacitors are electrochemical energy storage devices known for their large power densities and long lifetimes yet limited energy densities. A conventional understanding of supercapacitors relates the high power to fast ion accumulation at the polarized electrode interface, forming the so-called electric double layer (EDL), and the low energy to limited electrode surface area (SA). To improve the energy density, the capacitance may be enhanced by using high SA electrode materials such as carbon-based nanomaterials. While promising results have been experimentally reported, capacitances have also been noted to exhibit a highly non-linear relationship with SA. These interesting observations suggest that a gap exists in our fundamental understanding of charge storage mechanisms in the EDL of carbon nanomaterials. Given that EDLs are typically on the order of 1-3 nm thick, theoretical simulations can elucidate these unknown physical insights in order to identify new design principles for future electrode materials. In this dissertation, we explore two broad types of carbon-based nanomaterials, which are separated into two Parts, using a combined density functional theory and classical molecular dynamics computational approach. In Part I, we study the capacitance using various chemically and/or structurally modified graphene (or graphene-derived) materials which is motivated by previous accounts of the limited capacitance using pristine graphene. Our analysis demonstrates the viability of dramatically improving the capacitance using graphene-derived materials owing to enhancements in the quantum capacitance with marginal effects on the double layer capacitance. In Part II, we investigate the capacitance using nanoporous carbon materials which is motivated by experimental observations that relate capacitance to pore width rather than SA. Our findings confirm that promoting ion confinement through pore width control can enhance capacitance, but also identify pore shape dispersity as another important structural feature that facilitates fast ion dynamics during charging/discharging. The work in this dissertation presents an overview of new insights into charge storage mechanisms using low-dimensional carbon-based nanomaterials and future directions for materials development. Moreover, we anticipate that the established methodologies and analyses can be broadly applicable to the study of other applications utilizing electrified interfaces, including capacitive deionization and liquid-gated field effect transistors.Chemical Engineerin