{"id":{"repo_id":"hull","oai_identifier":"oai:hull-repository.worktribe.com:4212879"},"canonical_url":"https://search.dev.ndltd.org/etd/hull/oai:hull-repository.worktribe.com:4212879","repository":{"repo_id":"hull","name":"University of Hull","base_url":"https://hull-repository.worktribe.com/oaiprovider"},"display":{"title":"Optimisation of computed radiography chest imaging utilising a novel simulation technique derived from real patient computed tomography data sets","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Moore, Craig Steven"],"institution":"University of Hull","degree_name":"PhD","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Beavis, Andy","Saunderson, John"],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011","date_published":"2011","updated_at":"2026-07-24T02:33:31Z","subjects":["Computer science"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["oai:hull-repository.worktribe.com:4212879"],"render_values":[{"text":"oai:hull-repository.worktribe.com:4212879","href":null,"code":true}]}]},"links":{"outbound_url":"https://hull-repository.worktribe.com/4212879/1/Thesis","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Beavis, Andy","Saunderson, John"]},{"key":"dc:contributor.sponsor","label":"Sponsor","values":["Humberside Radiology Trust Fund"]},{"key":"dc:creator","label":"Author","values":["Moore, Craig Steven"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2011-09-01"]},{"key":"dc:date.issued","label":"Date","values":["2011"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Hull"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://hull-repository.worktribe.com/output/4212879"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["PhD"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computer science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["oai:hull-repository.worktribe.com:4212879"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hull-repository.worktribe.com/4212879/1/Thesis"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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. 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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."]},{"key":"dc:title","label":"Title","values":["Optimisation of computed radiography chest imaging utilising a novel simulation technique derived from real patient computed tomography data sets"]}]}],"canonical_facts":{"dc:contributor.advisor":["Beavis, Andy","Saunderson, John"],"dc:contributor.sponsor":["Humberside Radiology Trust Fund"],"dc:creator":["Moore, Craig Steven"],"dc:date":["2011-09-01"],"dc:date.issued":["2011"],"dc:description.abstract":["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. 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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. 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