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Title: Optimisation of computed radiography chest imaging utilising a novel simulation technique derived from real patient computed tomography data sets
Author: Moore, Craig Steven
ISNI:       0000 0004 2722 8099
Awarding Body: University of Hull
Current Institution: University of Hull
Date of Award: 2011
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To optimise any medical digital imaging system for chest radiography, it is vital that the images used for optimisation contain projected anatomy, or in other words, anatomical noise. In this thesis, a method to produce and validate a digitally reconstructed radiograph (DRR) computer algorithm that utilises real patient computed tomography (CT) data sets is presented. The algorithm uses a ray casting DRR calculation method to project X-ray pencil beams through CT data and derive the photon energy absorbed in a virtual computed radiography (CR) phosphor. Radiation scatter and CR system noise are added post DRR calculation. Quantitative and qualitative validation has shown the algorithm simulates chest CR images of average and obese patients with realistic anatomical and system noise. This has allowed images to be generated using various X-ray exposure parameters, i.e. tube potential, scatter rejection and receptor dose, which can then be used in the optimisation exercise. However, the algorithm is not without limitations; the impact of these on the resulting images is discussed. Simulated images reconstructed at the various X-ray exposure parameters and techniques were scored by experienced image evaluators; optimum tube potential, scatter rejection technique and receptor doses for clinical CR chest radiography have been derived. At the outset of this work, CR chest exposure factors across the Hull & East Yorkshire Hospitals NHS Trust (HEY) were not standardised, and therefore not optimised; this thesis concludes with recommendations to the HEY Radiology Department for optimum exposure factors and technique for chest radiography. These were implemented across the Trust as a result of this work. In summary, a DRR computer algorithm has been produced (and validated) that adequately simulates anatomical and system noise; image evaluators are able to grade simulated chest images presented at different X-ray exposure parameters in order to optimise radiographic technique for clinical CR chest radiography, without the need for repeat patient exposures.
Supervisor: Beavis, Andrew. ; Saunderson, John. Sponsor: Humberside Radiology Trust Fund
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Computer science