Determining micro- and macro- geometry of fabric and fabric reinforced composites

Doctor of PhilosophyDepartment of Mechanical and Nuclear EngineeringYouqi WangTextile composites are made from textile fabric and resin. Depending on the weaving pattern, composite reinforcements can be characterized into two groups: uniform fabric and near-net shape fabric. Uniform fabric can be treated as an assembly of its smallest repeating pattern also called a unit cell; the latter is a single component with complex structure. Due to advantages of cost savings and inherent toughness, near-net shape fabric has gained great success in composite industries, for application such as turbine blades. Mechanical properties of textile composites are mainly determined by the geometry of the composite reinforcements. The study of a composite needs a computational tool to link fabric micro- and macro-geometry with the textile weaving process and composite manufacturing process. A textile fabric consists of a number of yarns or tows, and each yarn is a bundle of fibers. In this research, a fiber-level approach known as the digital element approach (DEA) is adopted to model the micro- and macro-geometry of fabric and fabric reinforced composites. This approach determines fabric geometry based on textile weaving mechanics. A solver with a dynamic explicit algorithm is employed in the DEA. In modeling a uniform fabric, the topology of the fabric unit cell is first established based on the weaving pattern, followed by yarn discretization. An explicit algorithm with a periodic boundary condition is then employed during the simulation. After its detailed geometry is obtained, the unit cell is then assembled to yield a fabric micro-geometry. Fabric micro-geometry can be expressed at both fiber- and yarn-levels. In modeling a near-net shape fabric component, all theories used in simulating the uniform fabric are kept except the periodic boundary condition. Since simulating the entire component at the fiber-level requires a large amount of time and memory, parallel program is used during the simulation. In modeling a net-shape composite, a dynamic molding process is simulated. The near-net shape fabric is modeled using the DEA. Mold surfaces are modeled by standard meshes. Long vertical elements that only take compressive forces are proposed. Finally, micro- and macro-geometry of a fabric reinforced net-shape composite component is obtained

Novel imaging-processing-based analysis of fMRI data

Since its development in the early 1990s, functional MRI has emerged as a useful tool to explore the functional behavior of the human brain. Image processing for fMRI data analysis has been playing a very important role for determining which parts of the brain are activated by different types of stimuli. In my thesis, two novel image-processing-based approaches have been proposed. The first approach adopts three partially spatial temporal adaptive processing (STAP) schemes to reduce dimensionality of the fully STAP algorithm and make it more tractable. Computer simulations incorporating actual MRI noise and human data analysis indicate that these three partially adaptive STAP algorithms, especially element space, can attain the performance approximating that of fully adaptive STAP while significantly decreasing the processing time and maximum memory requirements. The second approach presents the idea of transforming the fMRI activation detection problem into a traditional image segmentation problem. With the incorporation of a Bayesian image segmentation technique, the expectation-maximization/maximization of the posterior marginals (EM/MPM) algorithm, to signal detection for event-related functional MRI (fMRI), the critical prior information in the spatial domain is preserved, overcoming a notable drawback of conventional fMRI analysis. The proposed EM/MPM-based approach is demonstrated to enhance detection performance over traditional GLM and ICA analysis, and yields activation that is comparable to those methods, suggesting that the new analysis procedure is a viable option for future use and development in the context of fMRI analysis. The success of this EM/MPM-based method will enable the very rich collection of advanced image segmentation techniques to be applied to detection of fMRI activation

A novel image analysis method based on bayesian segmentation for event-related functional MRI

This paper presents the application of the expectation-maximization/ maximization of the posterior marginals (EM/MPM) algorithm to signal detection for functional MRI (fMRI). On basis of assumptions for fMRI 3-D image data, a novel analysis method is proposed and applied to synthetic data and human brain data. Synthetic data analysis is conducted using two statistical noise models (white and autoregressive of order 1) and, for low contrast-to-noise ratio (CNR) data, reveals better sensitivity and specificity for the new method than for the traditional General Linear Model (GLM) approach. When applied to human brain data, functional activation regions are found to be consistent with those obtained using the GLM approach

Evaluating the effect and mechanism of upper limb motor function recovery induced by immersive virtual-reality-based rehabilitation for subacute stroke subjects: study protocol for a randomized controlled trial

Abstract Background There is compelling evidence of beneficial effects of non-immersive virtual reality (VR)-based intervention in the rehabilitation of patients with stroke, whereby patients experience both the real world and the virtual environment. However, to date, research on immersive VR-based rehabilitation is minimal. This study aims to design a randomized controlled trial to assess the effectiveness of immersive VR-based upper extremity rehabilitation in patients with subacute stroke and explore the underlying brain mechanisms of immersive VR-based rehabilitation. Methods Subjects (n = 60) with subacute stroke (defined as more than 1 week and less than 12 weeks after stroke onset) will be recruited to participate in a single-blinded, randomized controlled trial. Subjects will be randomized 1:1 to either (1) an experimental intervention group, or (2) a conventional group (control). Over a 3-week time period immediately following baseline assessments and randomization, subjects in the experimental group will receive both immersive VR and conventional rehabilitation, while those in the control group will receive conventional rehabilitation only. During the rehabilitation period and over the following 12 weeks, upper extremity function, cognitive function, mental status, and daily living activity performance will be evaluated in the form of questionnaires. To trace brain reorganization in which upper extremity functions previously performed by ischemic-related brain areas are assumed by other brain areas, subjects will have brain scans immediately following enrollment but before randomization, immediately following the conclusion of rehabilitation, and 12 weeks after rehabilitation has concluded. Discussion Effectiveness is assessed by evaluating motor improvement using the arm motor section of the Fugl-Meyer assessment. The study utilizes a cutting-edge brain neuroimaging approach to longitudinally trace the effectiveness of both VR-based and conventional training on stroke rehabilitation, which will hopefully describe the effects of the brain mechanisms of the intervention on recovery from stroke. Findings from the trial will greatly contribute to evidence on the use of immersive-VR-based training for stroke rehabilitation. Trial registration ClinicalTrials.gov, NCT03086889. Registered on March 22, 2017

Partially Adaptive STAP for fMRI: A Method for Detecting Brain Activation Regions in Simulation and Human Data

This paper introduces three partially adaptive space-time processing (STAP) schemes for analyzing fMRI data. Element space partially adaptive STAP can achieve performance close to that of fully adaptive STAP while greatly decreasing the CPU running time and memory requirements when applied to both synthetic as well as real human brain data. In synthetic analyses, partially adaptive STAP algorithms exhibit better detection characteristics than the traditional cross-correlation method. This is supported by human data in which element space and fully adaptive STAP produce activation maps that closely resemble those of cross-correlation

Reproducibility of structural, resting-state BOLD and DTI data between identical scanners.

Increasingly, clinical trials based on brain imaging are adopting multiple sites/centers to increase their subject pool and to expedite the studies, and more longitudinal studies are using multiple imaging methods to assess structural and functional changes. Careful investigation of the test-retest reliability and image quality of inter- or intra- scanner neuroimaging measurements are critical in the design, statistical analysis and interpretation of results. We propose a framework and specific metrics to quantify the reproducibility and image quality for neuroimaging studies (structural, BOLD and Diffusion Tensor Imaging) collected across identical scanners and following a major hardware repair (gradient coil replacement). We achieved consistent measures for the proposed metrics: structural (mean volume in specific regions and stretch factor), functional (temporal Signal-to-Noise ratio), diffusion (mean Fractional Anisotropy and Mean Diffusivity in multiple regions). The proposed frame work of imaging metrics should be used to perform daily quality assurance testing and incorporated into multi-center studies

Element Space Partially Adaptive STAP: A Method For Detecting Brain Activation Regions in Real fMRI Human Data,

Element space partially adaptive STAP is introduced and compared to fully adaptive STAP and cross-correlation as a means of forming functional brain maps from real human brain data undergoing asynchronous finger tapping and visual stimulus. Results of fully and partially adaptive STAP are in close agreement to those of cross-correlation