The Estimation and Correction of Rigid Motion in Helical Computed Tomography

X-ray CT is a tomographic imaging tool used in medicine and industry. Although technological developments have significantly improved the performance of CT systems, the accuracy of images produced by state-of-the-art scanners is still often limited by artefacts due to object motion. To tackle this problem, a number of motion estimation and compensation methods have been proposed. However, no methods with the demonstrated ability to correct for rigid motion in helical CT scans appear to exist. The primary aims of this thesis were to develop and evaluate effective methods for the estimation and correction of arbitrary six degree-of-freedom rigid motion in helical CT. As a first step, a method was developed to accurately estimate object motion during CT scanning with an optical tracking system, which provided sub-millimetre positional accuracy. Subsequently a motion correction method, which is analogous to a method previously developed for SPECT, was adapted to CT. The principle is to restore projection consistency by modifying the source-detector orbit in response to the measured object motion and reconstruct from the modified orbit with an iterative reconstruction algorithm. The feasibility of this method was demonstrated with a rapidly moving brain phantom, and the efficacy of correcting for a range of human head motions acquired from healthy volunteers was evaluated in simulations. The methods developed were found to provide accurate and artefact-free motion corrected images with most types of head motion likely to be encountered in clinical CT imaging, provided that the motion was accurately known. The method was also applied to CT data acquired on a hybrid PET/CT scanner demonstrating its versatility. Its clinical value may be significant by reducing the need for repeat scans (and repeat radiation doses), anesthesia and sedation in patient groups prone to motion, including young children

Partially Coherent Lab Based X-ray Micro Computed Tomography

X-ray micro computed tomography (CT) is a useful tool for imaging 3-D internal structures. It has many applications in geophysics, biology and materials science. Currently, micro-CT’s capability are limited due to validity of assumptions used in modelling the machines’ physical properties, such as penumbral blurring due to non-point source, and X-ray refraction. Therefore many CT research in algorithms and models are being carried out to overcome these limitations. This thesis presents methods to improve image resolution and noise, and to enable material property estimation of the micro-CT machine developed and in use at the ANU CTLab. This thesis is divided into five chapters as outlined below. The broad background topics of X-ray modelling and CT reconstruction are explored in Chapter 1, as required by later chapters. It describes each X-ray CT component, including the machines used at the ANU CTLab. The mathematical and statistical tools, and electromagnetic physical models are provided and used to characterise the scalar X-ray wave. This scalar wave equation is used to derive the projection operator through matter and free space, and basic reconstruction and phase retrieval algorithms. It quantifies the four types of X-ray interaction with matter for X-ray energy between 1 and 1000 keV, and presents common assumptions used for the modelling of lab based X-ray micro-CT. Chapter 2 is on X-ray source deblurring. The penumbral source blurring for X-ray micro-CT systems are limiting its resolution. This chapter starts with a geometrical framework to model the penumbral source blurring. I have simulated the effect of source blurring, assuming the geometry of the high-cone angle CT system, used at the ANU CTLab. Also, I have developed the Multislice Richardson-Lucy method that overcomes the computational complexity of the conjugate gradient method, while produces less artefacts compared to the standard Richardson-Lucy method. Its performance is demonstrated for both simulated and real experimental data. X-ray refraction, phase contrast and phase retrieval (PR) are investigated in Chapter 3. For weakly attenuating samples, intensity variation due to phase contrast is a significant fraction of the total signal. If phase contrast is incorrectly modelled, the reconstruction would not correctly account the phase contrast, therefore it would contribute to undesirable artefacts in the reconstruction volume. Here I present a novel Linear Iterative multi-energy PR algorithm. It enables material property estimation for the near field submicron X-ray CT system and reduces the noise and artefacts. This PR algorithm expands the validity range in comparison to the single material and data constrained modelling methods. I have also extended this novel PR algorithm to assume a polychromatic incident spectrum for a non-weakly absorbing object. Chapter 4 outlines the space filling X-ray source trajectory and reconstruction, on which I contributed in a minor capacity. This space filling trajectory reconstruction have improved the detector utilisation and reduced nonuniform resolution over the state-of-the-art 3-D Katsevich’s helical reconstruction, this patented work was done in collaboration with FEI Company. Chapter 5 concludes my PhD research work and provides future directions revealed by the present research

Spatial Resolution Analysis of a Variable Resolution X-ray Cone-beam Computed Tomography System

A new cone-beam computed tomography (CBCT) system is designed and implemented that can adaptively provide high resolution CT images for objects of different sizes. The new system, called Variable Resolution X-ray Cone-beam CT (VRX-CBCT) uses a CsI-based amorphous silicon flat panel detector (FPD) that can tilt about its horizontal (u) axis and vertical (v) axis independently. The detector angulation improves the spatial resolution of the CT images by changing the effective size of each detector cell. Two components of spatial resolution of the system, namely the transverse and axial modulation transfer functions (MTF), are analyzed in three different situations: (1) when the FPD is tilted only about its vertical axis (v), (2) when the FPD is tilted only about its horizontal axis (u), and (3) when the FPD is tilted isotropically about both its vertical and horizontal axes. Custom calibration and MTF phantoms were designed and used to calibrate and measure the spatial resolution of the system for each case described above. A new 3D reconstruction algorithm was developed and tested for the VRX-CBCT system, which combined with a novel 3D reconstruction algorithm, has improved the overall resolution of the system compared to an FDK-based algorithm

Development and Application of Computational Tools for the Study and Optimization of Variable Resolution X-ray (VRX) Computed Tomography Scanners

The overall goal of this project was to develop and apply important computerized aids for the design and implementation of Variable Resolution X-ray (VRX) CT scanners developed at the University of Tennessee, Memphis. VRX scanners take advantage of the “projective compression” principle that allows the same device to image objects of very different sizes with the same level of detail by adjusting the field of view and the reconstruction resolution. The first part of this project aimed to develop a set of computational tools specifically tailored for the design, implementation and study of VRX scanners. This included creating a reconstruction algorithm that takes into account the unique geometries of the different VRX systems that have been designed, along with improving the calibration algorithm needed to ensure a proper reconstruction. It also included the development of a computer model of VRX scanners that is an invaluable tool for the development and study of these devices. The second part of the project was composed of a small series of experiments in which the computational tools developed proved to be fundamental in the analysis and evaluation of some aspects of VRX imaging. This included a comparison of the performance of different targeting VRX geometries in terms of spatial and contrast resolution, and a study of the effect of the VRX angle on the severity of common artifacts in single-arm images

Geometrical Calibration and Filter Optimization for Cone-Beam Computed Tomography

This thesis will discuss the requirements of a software library for tomography and will derive a framework which can be used to realize various applications in cone-beam computed tomography (CBCT). The presented framework is self-contained and is realized using the MATLAB environment in combination with native low-level technologies (C/C++ and CUDA) to improve its computational performance, while providing accessibility and extendability through to use of a scripting language environment. On top of this framework, the realization of Katsevich’s algorithm on multicore hardware will be explained and the resulting implementation will be compared to the Feldkamp, Davis and Kress (FDK) algorithm. It will also be shown that this helical reconstruction method has the potential to reduce the measurement uncertainty. However, misalignment artifacts appear more severe in the helical reconstructions from real data than in the circular ones. Especially for helical CBCT (H-CBCT), this fact suggests that a precise calibration of the computed tomography (CT) system is inevitable. As a consequence, a self-calibration method will be designed that is able to estimate the misalignment parameters from the cone-beam projection data without the need of any additional measurements. The presented method employs a multi-resolution 2D-3D registration technique and a novel volume update scheme in combination with a stochastic reprojection strategy to achieve a reasonable runtime performance. The presented results will show that this method reaches sub-voxel accuracy and can compete with current state-of-the-art online- and offline-calibration approaches. Additionally, for the construction of filters in the area of limited-angle tomography a general scheme which uses the Approximate Inverse (AI) to compute an optimized set of 2D angle-dependent projection filters will be derived. Optimal sets of filters are then precomputed for two angular range setups and will be reused to perform various evaluations on multiple datasets with a filtered backprojection (FBP)-type method. This approach will be compared to the standard FDK algorithm and to the simultaneous iterative reconstruction technique (SIRT). The results of the study show that the introduced filter optimization produces results comparable to those of SIRT with respect to the reduction of reconstruction artifacts, whereby its runtime is comparable to that of the FDK algorithm

Artefact Reduction Methods for Iterative Reconstruction in Full-fan Cone Beam CT Radiotherapy Applications

A cone beam CT (CBCT) system acquires two-dimensional projection images of an imaging object from multiple angles in one single rotation and reconstructs the object geometry in three dimensions for volumetric visualization. It is mounted on most modern linear accelerators and is routinely used in radiotherapy to verify patient positioning, monitor patient contour changes throughout the course of treatment, and enable adaptive radiotherapy planning. Iterative image reconstruction algorithms use mathematical methods to iteratively solve the reconstruction problem. Iterative algorithms have demonstrated improvement in image quality and / or reduction in imaging dose over traditional filtered back-projection (FBP) methods. However, despite the advancement in computer technology and growing availability of open-source iterative algorithms, clinical implementation of iterative CBCT has been limited. This thesis does not report development of codes for new iterative image reconstruction algorithms. It focuses on bridging the gap between the algorithm and its implementation by addressing artefacts that are the results of imperfections from the raw projections and from the imaging geometry. Such artefacts can severely degrade image quality and cannot be removed by iterative algorithms alone. Practical solutions to solving these artefacts will be presented and this in turn will better enable clinical implementation of iterative CBCT reconstruction

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

Development and Implementation of Fully 3D Statistical Image Reconstruction Algorithms for Helical CT and Half-Ring PET Insert System

X-ray computed tomography: CT) and positron emission tomography: PET) have become widely used imaging modalities for screening, diagnosis, and image-guided treatment planning. Along with the increased clinical use are increased demands for high image quality with reduced ionizing radiation dose to the patient. Despite their significantly high computational cost, statistical iterative reconstruction algorithms are known to reconstruct high-quality images from noisy tomographic datasets. The overall goal of this work is to design statistical reconstruction software for clinical x-ray CT scanners, and for a novel PET system that utilizes high-resolution detectors within the field of view of a whole-body PET scanner. The complex choices involved in the development and implementation of image reconstruction algorithms are fundamentally linked to the ways in which the data is acquired, and they require detailed knowledge of the various sources of signal degradation. Both of the imaging modalities investigated in this work have their own set of challenges. However, by utilizing an underlying statistical model for the measured data, we are able to use a common framework for this class of tomographic problems. We first present the details of a new fully 3D regularized statistical reconstruction algorithm for multislice helical CT. To reduce the computation time, the algorithm was carefully parallelized by identifying and taking advantage of the specific symmetry found in helical CT. Some basic image quality measures were evaluated using measured phantom and clinical datasets, and they indicate that our algorithm achieves comparable or superior performance over the fast analytical methods considered in this work. Next, we present our fully 3D reconstruction efforts for a high-resolution half-ring PET insert. We found that this unusual geometry requires extensive redevelopment of existing reconstruction methods in PET. We redesigned the major components of the data modeling process and incorporated them into our reconstruction algorithms. The algorithms were tested using simulated Monte Carlo data and phantom data acquired by a PET insert prototype system. Overall, we have developed new, computationally efficient methods to perform fully 3D statistical reconstructions on clinically-sized datasets