Imaging of solid flow in a gravity flow rig using infra-red tomography

Information on flow regimes is vital in the analysis and measurement of industrial process flow. Almost all currently available method of measuring the flow of two-component mixtures in industrial pipelines endeavors to average a property of the flow over the pipe cross-section. They do not give information on the nature of the flow regime and they are unsuitable for accurate measurement where the component distribution is spatially or time varying. The overall aim of this project is to investigate the use of an optical tomography method based on infra-red sensors for real-time monitoring of solid particles conveyed by a rotary valve in a pneumatic pipeline. The infra-red tomography system can be divided into two distinct portions of hardware and software development process. The hardware development process covers the infra-red sensor selection, fixtures and signals conditioning circuits, and control circuits. The software development involves data acquisition system, sensor modeling, image algorithms, and programming for a tomographic display to provide solids flow information in pipeline such as concentration and velocity profiles. Collimating the radiated beam from a light source and passing it via a flow regime ensures that the intensity of radiation detected on the opposite side is linked to the distribution and the absorption coefficients of the different phases in the path of the beam. The information is obtained from the combination of two orthogonal and two diagonal light projection system and 30 cycles of real-time measurements. Those information on the flow captured using upstream and downstream infra-red sensors are digitized by the DAS system before it was passed into a computer for analysis such as image reconstructions and cross-correlation process that provide velocity profiles represented by 16 × 16 pixels mapped onto the pipe cross-section. This project successfully developed and tested an infra-red tomography system to display two-dimensional images of concentration and velocity

Passive element enriched photoacoustic computed tomography (PER PACT) for simultaneous imaging of acoustic propagation properties and light absorption\ud

We present a ‘hybrid’ imaging approach which can image both light absorption properties and acoustic transmission properties of an object in a two-dimensional slice using a computed tomography (CT) photoacoustic imager. The ultrasound transmission measurement method uses a strong optical absorber of small cross-section placed in the path of the light illuminating the sample. This absorber, which we call a passive element acts as a source of ultrasound. The interaction of ultrasound with the sample can be measured in transmission, using the same ultrasound detector used for photoacoustics. Such measurements are made at various angles around the sample in a CT approach. Images of the ultrasound propagation parameters, attenuation and speed of sound, can be reconstructed by inversion of a measurement model. We validate the method on specially designed phantoms and biological specimens. The obtained images are quantitative in terms of the shape, size, location, and acoustic properties of the examined heterogeneitie

Visual Quality Enhancement in Optoacoustic Tomography using Active Contour Segmentation Priors

Segmentation of biomedical images is essential for studying and characterizing anatomical structures, detection and evaluation of pathological tissues. Segmentation has been further shown to enhance the reconstruction performance in many tomographic imaging modalities by accounting for heterogeneities of the excitation field and tissue properties in the imaged region. This is particularly relevant in optoacoustic tomography, where discontinuities in the optical and acoustic tissue properties, if not properly accounted for, may result in deterioration of the imaging performance. Efficient segmentation of optoacoustic images is often hampered by the relatively low intrinsic contrast of large anatomical structures, which is further impaired by the limited angular coverage of some commonly employed tomographic imaging configurations. Herein, we analyze the performance of active contour models for boundary segmentation in cross-sectional optoacoustic tomography. The segmented mask is employed to construct a two compartment model for the acoustic and optical parameters of the imaged tissues, which is subsequently used to improve accuracy of the image reconstruction routines. The performance of the suggested segmentation and modeling approach are showcased in tissue-mimicking phantoms and small animal imaging experiments.Comment: Accepted for publication in IEEE Transactions on Medical Imagin

State of the art: iterative CT reconstruction techniques

Owing to recent advances in computing power, iterative reconstruction (IR) algorithms have become a clinically viable option in computed tomographic (CT) imaging. Substantial evidence is accumulating about the advantages of IR algorithms over established analytical methods, such as filtered back projection. IR improves image quality through cyclic image processing. Although all available solutions share the common mechanism of artifact reduction and/or potential for radiation dose savings, chiefly due to image noise suppression, the magnitude of these effects depends on the specific IR algorithm. In the first section of this contribution, the technical bases of IR are briefly reviewed and the currently available algorithms released by the major CT manufacturers are described. In the second part, the current status of their clinical implementation is surveyed. Regardless of the applied IR algorithm, the available evidence attests to the substantial potential of IR algorithms for overcoming traditional limitations in CT imaging

Tomographic reconstruction algorithms using optoelectronic devices

During the last two decades, iterative computerized tomography (CT) algorithms, such as ART (Algebraic Reconstruction Technique) and SIRT (Simultaneous Iterative Reconstruction Technique), have been applied to the solution of overdetermined and underdetermined systems. These algorithms arrive at the least squares solution of normal equations. In theory, such algorithms converge to the minimum-norm solution when a system is underdetermined if there are no computational errors and the initial vector is chosen properly. In practice, computational errors may lead to failure to converge to a unique solution.;The dissertation introduces a method called the projection iterative reconstruction technique (PIRT) which differs from the other reconstruction algorithms used for solving underdetermined systems. Even though the differences between the method outlined in this dissertation and the algorithms proposed earlier are subtle, the proposed scheme guarantees convergence to a unique minimum-norm solution. Several acceleration techniques are discussed in the dissertation. Furthermore, the iterative algorithm can also be generalized and employed to solve other large and sparse linear systems