An Efficient Estimation Method for Reducing the Axial Intensity Drop in Circular Cone-Beam CT

Reconstruction algorithms for circular cone-beam (CB) scans have been extensively studied in the literature. Since insufficient data are measured, an exact reconstruction is impossible for such a geometry. If the reconstruction algorithm assumes zeros for the missing data, such as the standard FDK algorithm, a major type of resulting CB artifacts is the intensity drop along the axial direction. Many algorithms have been proposed to improve image quality when faced with this problem of data missing; however, development of an effective and computationally efficient algorithm remains a major challenge. In this work, we propose a novel method for estimating the unmeasured data and reducing the intensity drop artifacts. Each CB projection is analyzed in the Radon space via Grangeat's first derivative. Assuming the CB projection is taken from a parallel beam geometry, we extract those data that reside in the unmeasured region of the Radon space. These data are then used as in a parallel beam geometry to calculate a correction term, which is added together with Hu's correction term to the FDK result to form a final reconstruction. More approximations are then made on the calculation of the additional term, and the final formula is implemented very efficiently. The algorithm performance is evaluated using computer simulations on analytical phantoms. The reconstruction comparison with results using other existing algorithms shows that the proposed algorithm achieves a superior performance on the reduction of axial intensity drop artifacts with a high computation efficiency

Model-Based Iterative Reconstruction in Cone-Beam Computed Tomography: Advanced Models of Imaging Physics and Prior Information

Cone-beam computed tomography (CBCT) represents a rapidly developing imaging modality that provides three-dimensional (3D) volumetric images with sub-millimeter spatial resolution and soft-tissue visibility from a single gantry rotation. CBCT tends to have small footprint, mechanical simplicity, open geometry, and low cost compared to conventional diagnostic CT. Because of these features, CBCT has been used in a variety of specialty diagnostic applications, image-guided radiation therapy (on-board CBCT), and surgical guidance (e.g., C-arm based CBCT). However, the current generation of CBCT systems face major challenges in low-contrast, soft-tissue image quality – for example, in the detection of acute intracranial hemorrhage (ICH), which requires a fairly high level of image uniformity, spatial resolution, and contrast resolution. Moreover, conventional approaches in both diagnostic and image-guided interventions that involve a series of imaging studies fail to leverage the wealth of patient-specific anatomical information available from previous scans. Leveraging the knowledge gained from prior images holds the potential for major gains in image quality and dose reduction. Model-based iterative reconstruction (MBIR) attempts to make more efficient use of the measurement data by incorporating a forward model of physical detection processes. Moreover, MBIR allows incorporation of various forms of prior information into image reconstruction, ranging from image smoothness and sharpness to patient-specific anatomical information. By leveraging such advantages, MBIR has demonstrated improved tradeoffs between image quality and radiation dose in multi-detector CT in the past decade and has recently shown similar promise in CBCT. However, the full potential of MBIR in CBCT is yet to be realized. This dissertation explores the capabilities of MBIR in improving image quality (especially low-contrast, soft-tissue image quality) and reducing radiation dose in CBCT. The presented work encompasses new MBIR methods that: i) modify the noise model in MBIR to compensate for noise amplification from artifact correction; ii) design regularization by explicitly incorporating task-based imaging performance as the objective; iii) mitigate truncation effects in a computationally efficient manner; iv) leverage a wealth of patient-specific anatomical information from a previously acquired image; and v) prospectively estimate the optimal amount of prior image information for accurate admission of specific anatomical changes. Specific clinical challenges are investigated in the detection of acute ICH and surveillance of lung nodules. The results show that MBIR can substantially improve low-contrast, soft-tissue image quality in CBCT and enable dose reduction techniques in sequential imaging studies. The thesis demonstrates that novel MBIR methods hold strong potential to overcome conventional barriers to CBCT image quality and open new clinical applications that would benefit from high-quality 3D imaging

Advanced industrial X-ray computed tomography for defect detection and characterisation of composite structures

X-ray Computer Tomography (CT) is well suited to the inspection of Fibre-Reinforced-Plastic (FRP) composite materials. However, a range of limitations currently restrict its uptake. The aim of the present research was to develop advanced inspection procedures that overcome these limitations and increase the scope of composite structures that can be inspected by industrial cone beam CT. Region of Interest (ROI) CT inspection of FRP laminated panels was investigated and two data completion methods developed to overcome reconstruction errors caused by truncated projection data. These allow accurate, highly magnified regions to be reconstructed on objects that extend beyond the Field-of-View (FOV) of the detector. The first method extended the truncated projection data using a cosine signal tailing off to zero attenuation. This method removed the strong 'glowing' artefacts but an inherent error existed across the reconstructed ROI. This did not affect the defect detectability of the inspection but was viewed as problematic for applications requiring accurate density measurements. The second method used prior knowledge of the test object so that a model could be created to estimate the missing data. This technique removed errors associated with ROI reconstruction thus significantly improving the accuracy. Techniques for extending the FOV were developed and applied to the inspection of FRP wind turbine blades; over 1.5X larger than the conventional scanning FOV. Two data completion methods were developed requiring an asymmetrically positioned detector. The first was based on the cosine tailing technique and the second used fan beam ray redundancy properties to estimate the missing data. Both produced accurate reconstructions for the 'offset' projection data, demonstrating that it was possible to approximately double the FOV. The cosine tailing method was found to be the more reliable. A dual energy image CT technique was developed to extend the optimum dynamic range and improve defect detectability for multi-density objects. This was applied to FRP composite/Titanium lap joints showing improved detectability of both volumetric and planar defects within the low density FRP. The dual energy procedure was validated using statistical performance measures on a specially fabricated multi-density phantom. The results showed a significant improvement in the detail SNR when compared to conventional CT scans.EThOS - Electronic Theses Online ServiceTWI LtdThe Engineering and Physical Sciences Research Board (EPSRC)GBUnited Kingdo

Topics in Adaptive Optics

Advances in adaptive optics technology and applications move forward at a rapid pace. The basic idea of wavefront compensation in real-time has been around since the mid 1970s. The first widely used application of adaptive optics was for compensating atmospheric turbulence effects in astronomical imaging and laser beam propagation. While some topics have been researched and reported for years, even decades, new applications and advances in the supporting technologies occur almost daily. This book brings together 11 original chapters related to adaptive optics, written by an international group of invited authors. Topics include atmospheric turbulence characterization, astronomy with large telescopes, image post-processing, high power laser distortion compensation, adaptive optics and the human eye, wavefront sensors, and deformable mirrors

Rapid, Reliable Tissue Fractionation Algorithm for Commercial Scale Biorefineries

Increasing demand, limited supply, and the impact on the environment raise significant concerns about the consumption of fossil fuels. Because of this, global economies are facing two significant energy challenges: i) securing the supply of reliable and affordable energy and ii) achieving the transformation to a low-carbon, high-efficiency, and sustainable energy system. Recently, there has been growing interest in developing portable transportation fuels from biomass in order to reduce the petroleum consumption in the transportation sector - a major contributor to greenhouse gas emission. A cost-effective conversion process to produce biofuels from lignocellulosic biomass material relies not just on the material quality, but also on the biorefinery’s ability to measure the quality of the source biomass. The quality of the feedstock is crucial for a commercially viable conversion platform. This research mainly focuses on developing sensing techniques using 3D X-ray imaging to study quality factors like material composition, ash content and moisture content which affect the conversion efficiency, equipment wear, and product yield in the bioethanol production in a real-time or near real-time basis