Finite element modelling of electrostatic fields in process tomography capacitive electrode systems for flow response evaluation

Various aspects and results of 2-D finite element (FE) modeling of electrostatic fields in 12-electrode capacitive systems for two-phase flow imaging are described. The capacitive technique relies on changes in capacitances between electrodes (mounted on the outer surface of the flow pipe) due to the change in permittivities of flow components. The measured capacitances between various electrode pairs and the field computation data are used to reconstruct the cross sectional image of the flow components. FE modeling of the electric field is necessary to optimize design variables and evaluate the system response to various flow regimes, likely to be encountered in practice. Results are presented in terms of normalized capacitances for various flow regimes. The effects of key geometric parameters of the electrode system are presented and analyzed

Pure phase-encoded MRI and classification of solids

Here, the authors combine a pure phase-encoded magnetic resonance imaging (MRI) method with a new tissue-classification technique to make geometric models of a human tooth. They demonstrate the feasibility of three-dimensional imaging of solids using a conventional 11.7-T NMR spectrometer. In solid-state imaging, confounding line-broadening effects are typically eliminated using coherent averaging methods. Instead, the authors circumvent them by detecting the proton signal at a fixed phase-encode time following the radio-frequency excitation. By a judicious choice of the phase-encode time in the MRI protocol, the authors differentiate enamel and dentine sufficiently to successfully apply a new classification algorithm. This tissue-classification algorithm identifies the distribution of different material types, such as enamel and dentine, in volumetric data. In this algorithm, the authors treat a voxel as a volume, not as a single point, and assume that each voxel may contain more than one material. They use the distribution of MR image intensities within each voxel-sized volume to estimate the relative proportion of each material using a probabilistic approach. This combined approach, involving MRI and data classification, is directly applicable to bone imaging and hard-tissue contrast-based modeling of biological solids

Polarimetric Imaging of Large Cavity Structures in the Pre-transitional Protoplanetary Disk around PDS 70: Observations of the disk

We present high resolution H-band polarized intensity (PI; FWHM = 0."1: 14 AU) and L'-band imaging data (FWHM = 0."11: 15 AU) of the circumstellar disk around the weak-lined T Tauri star PDS 70 in Centaurus at a radial distance of 28 AU (0."2) up to 210 AU (1."5). In both images, a giant inner gap is clearly resolved for the first time, and the radius of the gap is ~70 AU. Our data show that the geometric center of the disk shifts by ~6 AU toward the minor axis. We confirm that the brown dwarf companion candidate to the north of PDS 70 is a background star based on its proper motion. As a result of SED fitting by Monte Carlo radiative transfer modeling, we infer the existence of an optically thick inner disk at a few AU. Combining our observations and modeling, we classify the disk of PDS 70 as a pre-transitional disk. Furthermore, based on the analysis of L'-band imaging data, we put an upper limit mass of companions at ~30 to ~50MJ within the gap. Taking account of the presence of the large and sharp gap, we suggest that the gap could be formed by dynamical interactions of sub-stellar companions or multiple unseen giant planets in the gap.Comment: accepted by APJ

Implementation and evaluation of medical imaging techniques based on conformal geometric algebra

Medical imaging tasks, such as segmentation, 3D modeling, and registration of medical images, involve complex geometric problems, usually solved by standard linear algebra and matrix calculations. In the last few decades, conformal geometric algebra (CGA) has emerged as a new approach to geometric computing that offers a simple and efficient representation of geometric objects and transformations. However, the practical use of CGA-based methods for big data image processing in medical imaging requires fast and efficient implementations of CGA operations to meet both real-time processing constraints and accuracy requirements. The purpose of this study is to present a novel implementation of CGA-based medical imaging techniques that makes them effective and practically usable. The paper exploits a new simplified formulation of CGA operators that allows significantly reduced execution times while maintaining the needed result precision. We have exploited this novel CGA formulation to re-design a suite of medical imaging automatic methods, including image segmentation, 3D reconstruction and registration. Experimental tests show that the re-formulated CGA-based methods lead to both higher precision results and reduced computation times, which makes them suitable for big data image processing applications. The segmentation algorithm provides the Dice index, sensitivity and specificity values of 98.14%, 98.05% and 97.73%, respectively, while the order of magnitude of the errors measured for the registration methods is 10-5

Biomedical visual computing: case studies and challenges

pre-printAdvances in computational geometric modeling, imaging, and simulation let researchers build and test models of increasing complexity, generating unprecedented amounts of data. As recent research in biomedical applications illustrates, visualization will be critical in making this vast amount of data usable; it's also fundamental to understanding models of complex phenomena

Reconstructing CMEs with Coordinated Imaging and In Situ Observations: Global Structure, Kinematics, and Implications for Space Weather Forecasting

See the pdf for detailsComment: 45 pages, 16 figures, ApJ, in pres

Multi-function based modeling of 3D heterogeneous wound scaffolds for improved wound healing

This paper presents a new multi-function based modeling of 3D heterogeneous porous wound scaffolds to improve wound healing process for complex deep acute or chronic wounds. An imaging-based approach is developed to extract 3D wound geometry and recognize wound features. Linear healing fashion of the wound margin towards the wound center is mimicked. Blending process is thus applied to the extracted geometry to partition the scaffold into a number of uniformly gradient healing regions. Computer models of 3D engineered porous wound scaffolds are then developed for solid freeform modeling and fabrication. Spatial variation over biomaterial and loaded bio-molecule concentration is developed based on wound healing requirements. Release of bio-molecules over the uniform healing regions is controlled by varying their amount and entrapping biomaterial concentration. Thus, localized controlled release is developed to improve wound healing. A prototype multi-syringe single nozzle deposition system is used to fabricate a sample scaffold. Proposed methodology is implemented and illustrative examples are presented in this paper