Contributions to spectral CT
Thesis DisciplineElectrical Engineering
Degree GrantorUniversity of Canterbury
Degree NameDoctor of Philosophy
Spectral x-ray computed tomography (CT) is an important nascent imaging modality with several exciting potential applications. The research presented in this thesis separates into two primary areas with the common underlying theme of spectral CT; the first area is Compton scatter estimation and the second is interior tomography.
First, the research is framed and outputs are identified. Background on the concepts used in the thesis is offered, including x-ray imaging and computed tomography, CT scanner architecture, spectral imaging, interior tomography and x-ray scatter. The mathematical background of techniques for image reconstruction from x-ray transmission measurements are presented. Many of the tools used to perform the research, both hardware and software, are described. An algorithm is developed for estimating the intensity of Compton scattered photons within a spectral CT scan, and a major approximation used by the algorithm is analysed. One proposed interior reconstruction algorithm is briefly evaluated; while this is not directly linked to spectral CT, it is related to the work on a novel hybrid spectral interior micro-CT architecture. Conclusions are summarised and suggestions for future work are offered.
Scatter is known to cause artefacts in CT reconstructions, and several methods exist to correct data that has been corrupted by scatter. Compton scatter affects the energy of photons, therefore spectral CT measurements offer the potential to correct for this phenomenon more accurately than conventional measurements. A Compton scatter algorithm is developed and is found to match very well to Monte Carlo validation simulations, with the constraints that the object be at the micro-CT scale and that electron-binding effects are omitted. Development of the algorithm uses an approximation of the post-scatter attenuation to simplify the estimation problem and enable implementation. The consequences of this approximation are analysed, and the error introduced is found to be less than 5% in most biomedical micro-CT situations.
Interior tomography refers to the incomplete data situation caused by the truncation of some or all CT projections, and is an active research area. A recently proposed interior reconstruction algorithm is evaluated with regard to its sensitivity to input error, and is found to have mediocre performance in this respect. Published results are not found to be reproducible, suggesting some omission from the published algorithm.
A novel hybrid spectral interior architecture is described, along with an iterative reconstruction algorithm for hybrid data sets. The system combines a full field of view conventional imaging chain and an interior field of view spectral imaging chain to enable spectral measurement of a region of interest, and addresses some important limitations of spectral x-ray detectors; promising results are shown. Spectral reconstructions from interior data are shown to have sufficient information to distinguish two k-edge contrast agents (iodine and gadolinium) not only within the interior field of view but also beyond it. The architecture is further explored in the context of radiation exposure reduction, including testing of an analytical hybrid reconstruction algorithm.