{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/12106"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/12106","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Investigations into a positron emission imaging algorithm","abstract":"A positron emission imaging algorithm which makes use of the entire set of lines-of-response in list-mode form is presented. The algorithm parameterises the lines-of-response by a Cartesian mesh over the field-of-view of a Positron Emission Tomography (PET) scanner to find their density distribution throughout the mesh. The algorithm is applied to PET image reconstruction and Positron Emission Particle Tracking (PEPT). For the PET image reconstruction, a redistribution of the lines-of-response is employed to remove the discrete nature of the data caused by the finite size of the detector cells, and once the density distribution has been determined, it is filtered and corrected for attenuation. The algorithm is applied to static and dynamic systems of hard phantoms, biological specimens and fluid flow through a column. In the dynamic systems, timesteps as low as 1 second are achieved. The results from the algorithm are compared to the standard Radon transform reconstruction algorithm, and the presented algorithm is observed to produce images with superior edge contrast, smoothness and representation of the physical system.","abstract_html":"A positron emission imaging algorithm which makes use of the entire set of lines-of-response in list-mode form is presented. The algorithm parameterises the lines-of-response by a Cartesian mesh over the field-of-view of a Positron Emission Tomography (PET) scanner to find their density distribution throughout the mesh. The algorithm is applied to PET image reconstruction and Positron Emission Particle Tracking (PEPT). For the PET image reconstruction, a redistribution of the lines-of-response is employed to remove the discrete nature of the data caused by the finite size of the detector cells, and once the density distribution has been determined, it is filtered and corrected for attenuation. The algorithm is applied to static and dynamic systems of hard phantoms, biological specimens and fluid flow through a column. In the dynamic systems, timesteps as low as 1 second are achieved. The results from the algorithm are compared to the standard Radon transform reconstruction algorithm, and the presented algorithm is observed to produce images with superior edge contrast, smoothness and representation of the physical system.","abstract_has_math":false,"creators":["Bickell, Matthew"],"institution":"Department of Physics","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Buffler, Andy","Govender, Indresan"],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012","date_published":"2012","updated_at":"2026-07-22T22:22:50Z","subjects":[],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/12106","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Buffler, Andy","Govender, Indresan"]},{"key":"dc:creator","label":"Author","values":["Bickell, Matthew"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2015-01-11T13:32:48Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2015-01-11T13:32:48Z"]},{"key":"dc:date.issued","label":"Date","values":["2012"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Physics"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cape Town"]},{"key":"dc:type","label":"Dc Type","values":["Master Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["MSc"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/12106"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Includes abstract.","Includes bibliographical references."]},{"key":"dc:description.abstract","label":"Abstract","values":["A positron emission imaging algorithm which makes use of the entire set of lines-of-response in list-mode form is presented. The algorithm parameterises the lines-of-response by a Cartesian mesh over the field-of-view of a Positron Emission Tomography (PET) scanner to find their density distribution throughout the mesh. The algorithm is applied to PET image reconstruction and Positron Emission Particle Tracking (PEPT). For the PET image reconstruction, a redistribution of the lines-of-response is employed to remove the discrete nature of the data caused by the finite size of the detector cells, and once the density distribution has been determined, it is filtered and corrected for attenuation. The algorithm is applied to static and dynamic systems of hard phantoms, biological specimens and fluid flow through a column. In the dynamic systems, timesteps as low as 1 second are achieved. 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The algorithm parameterises the lines-of-response by a Cartesian mesh over the field-of-view of a Positron Emission Tomography (PET) scanner to find their density distribution throughout the mesh. The algorithm is applied to PET image reconstruction and Positron Emission Particle Tracking (PEPT). For the PET image reconstruction, a redistribution of the lines-of-response is employed to remove the discrete nature of the data caused by the finite size of the detector cells, and once the density distribution has been determined, it is filtered and corrected for attenuation. The algorithm is applied to static and dynamic systems of hard phantoms, biological specimens and fluid flow through a column. In the dynamic systems, timesteps as low as 1 second are achieved. 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