{"id":{"repo_id":"cork","oai_identifier":"oai:cora.ucc.ie:10468/18410"},"canonical_url":"https://search.dev.ndltd.org/etd/cork/oai:cora.ucc.ie:10468/18410","repository":{"repo_id":"cork","name":"University College Cork","base_url":"https://cora.ucc.ie/server/oai/request"},"display":{"title":"Statistical analysis of 11CO2 contamination correction in 11C-acetate PET imaging studies","abstract":"Positron Emission Tomography (PET) is a noninvasive imaging modality widely used in cancer management and medical research. It supports diagnosis, prognosis, treatment monitoring, and clinical decisions by tracking radiotracers labeled with positron-emitting isotopes. Tracer choice depends on the biological process of interest, such as tumor metabolism, cell proliferation, or blood flow. The most common tracer is fluorodeoxyglucose ([18F]FDG), which accumulates in tissues with high glucose metabolism (e.g., brain, heart, and many tumors), and is used for cancer detection, staging, and treatment monitoring, as well as neurological and cardiac assessment. Another key tracer is 11C-acetate, involved in cell membrane synthesis and oxidative metabolism. It complements FDG, especially in tissues with low or variable glucose uptake. However, its oxidative metabolism generates 11CO2, which is indistinguishable from the PET measured signal. The main goal of this thesis is to develop a statistical model to estimate and correct the contribution of metabolically produced 11CO2 in 11C-acetate PET imaging. The model is constructed using non-parametric residue mapping (NPRM) and the extended semi-parametric residue mapping (SPRM), based on a dynamic PET dataset acquired with 11CO2-11C-acetate dual injection. The correction performance is evaluated at the voxel and region-of-interest (ROI) levels. Furthermore, this thesis explores the feasibility of performing 11CO2 contamination correction using only 11C-acetate PET data, without requiring a separate 11CO2 scan. Promising results are presented to demonstrate the potential of this approach.","abstract_html":"Positron Emission Tomography (PET) is a noninvasive imaging modality widely used in cancer management and medical research. It supports diagnosis, prognosis, treatment monitoring, and clinical decisions by tracking radiotracers labeled with positron-emitting isotopes. Tracer choice depends on the biological process of interest, such as tumor metabolism, cell proliferation, or blood flow. The most common tracer is fluorodeoxyglucose ([18F]FDG), which accumulates in tissues with high glucose metabolism (e.g., brain, heart, and many tumors), and is used for cancer detection, staging, and treatment monitoring, as well as neurological and cardiac assessment. Another key tracer is 11C-acetate, involved in cell membrane synthesis and oxidative metabolism. It complements FDG, especially in tissues with low or variable glucose uptake. However, its oxidative metabolism generates 11CO2, which is indistinguishable from the PET measured signal. The main goal of this thesis is to develop a statistical model to estimate and correct the contribution of metabolically produced 11CO2 in 11C-acetate PET imaging. The model is constructed using non-parametric residue mapping (NPRM) and the extended semi-parametric residue mapping (SPRM), based on a dynamic PET dataset acquired with 11CO2-11C-acetate dual injection. The correction performance is evaluated at the voxel and region-of-interest (ROI) levels. Furthermore, this thesis explores the feasibility of performing 11CO2 contamination correction using only 11C-acetate PET data, without requiring a separate 11CO2 scan. Promising results are presented to demonstrate the potential of this approach.","abstract_has_math":false,"creators":["Lan, Tao"],"institution":"University College Cork","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["O'Sullivan, Finbarr","Huang, Jian"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-24T01:48:42Z","subjects":["Dynamic PET","Semi-parametric residue mapping","11C-Acetate PET imaging","11CO2 correction in 11C-acetate PET imaging","Dual-injection scans"],"languages":["en"],"rights":["© 2025, Tao Lan."],"rights_urls":["https://creativecommons.org/licenses/by/4.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10468/18410","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["O'Sullivan, Finbarr","Huang, Jian"]},{"key":"dc:creator","label":"Author","values":["Lan, Tao"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-01-19T16:11:04Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-01-19T16:11:04Z"]},{"key":"dc:date.issued","label":"Date","values":["2025"]},{"key":"dc:publisher","label":"Institution","values":["University College Cork"]},{"key":"dc:type","label":"Dc Type","values":["Doctoral thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["PhD - Doctor of Philosophy"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Dynamic PET","Semi-parametric residue mapping","11C-Acetate PET imaging","11CO2 correction in 11C-acetate PET imaging","Dual-injection scans"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["© 2025, Tao Lan."]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://creativecommons.org/licenses/by/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10468/18410"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Positron Emission Tomography (PET) is a noninvasive imaging modality widely used in cancer management and medical research. It supports diagnosis, prognosis, treatment monitoring, and clinical decisions by tracking radiotracers labeled with positron-emitting isotopes. Tracer choice depends on the biological process of interest, such as tumor metabolism, cell proliferation, or blood flow. The most common tracer is fluorodeoxyglucose ([18F]FDG), which accumulates in tissues with high glucose metabolism (e.g., brain, heart, and many tumors), and is used for cancer detection, staging, and treatment monitoring, as well as neurological and cardiac assessment. Another key tracer is 11C-acetate, involved in cell membrane synthesis and oxidative metabolism. It complements FDG, especially in tissues with low or variable glucose uptake. However, its oxidative metabolism generates 11CO2, which is indistinguishable from the PET measured signal. The main goal of this thesis is to develop a statistical model to estimate and correct the contribution of metabolically produced 11CO2 in 11C-acetate PET imaging. The model is constructed using non-parametric residue mapping (NPRM) and the extended semi-parametric residue mapping (SPRM), based on a dynamic PET dataset acquired with 11CO2-11C-acetate dual injection. The correction performance is evaluated at the voxel and region-of-interest (ROI) levels. Furthermore, this thesis explores the feasibility of performing 11CO2 contamination correction using only 11C-acetate PET data, without requiring a separate 11CO2 scan. Promising results are presented to demonstrate the potential of this approach."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Statistical analysis of 11CO2 contamination correction in 11C-acetate PET imaging studies"]}]}],"canonical_facts":{"dc:contributor.advisor":["O'Sullivan, Finbarr","Huang, Jian"],"dc:creator":["Lan, Tao"],"dc:date.accessioned":["2026-01-19T16:11:04Z"],"dc:date.available":["2026-01-19T16:11:04Z"],"dc:date.issued":["2025"],"dc:description.abstract":["Positron Emission Tomography (PET) is a noninvasive imaging modality widely used in cancer management and medical research. It supports diagnosis, prognosis, treatment monitoring, and clinical decisions by tracking radiotracers labeled with positron-emitting isotopes. Tracer choice depends on the biological process of interest, such as tumor metabolism, cell proliferation, or blood flow. The most common tracer is fluorodeoxyglucose ([18F]FDG), which accumulates in tissues with high glucose metabolism (e.g., brain, heart, and many tumors), and is used for cancer detection, staging, and treatment monitoring, as well as neurological and cardiac assessment. Another key tracer is 11C-acetate, involved in cell membrane synthesis and oxidative metabolism. It complements FDG, especially in tissues with low or variable glucose uptake. However, its oxidative metabolism generates 11CO2, which is indistinguishable from the PET measured signal. The main goal of this thesis is to develop a statistical model to estimate and correct the contribution of metabolically produced 11CO2 in 11C-acetate PET imaging. The model is constructed using non-parametric residue mapping (NPRM) and the extended semi-parametric residue mapping (SPRM), based on a dynamic PET dataset acquired with 11CO2-11C-acetate dual injection. The correction performance is evaluated at the voxel and region-of-interest (ROI) levels. Furthermore, this thesis explores the feasibility of performing 11CO2 contamination correction using only 11C-acetate PET data, without requiring a separate 11CO2 scan. Promising results are presented to demonstrate the potential of this approach."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/10468/18410"],"dc:language.iso":["en"],"dc:publisher":["University College Cork"],"dc:rights":["© 2025, Tao Lan."],"dc:rights.uri":["https://creativecommons.org/licenses/by/4.0/"],"dc:subject":["Dynamic PET","Semi-parametric residue mapping","11C-Acetate PET imaging","11CO2 correction in 11C-acetate PET imaging","Dual-injection scans"],"dc:title":["Statistical analysis of 11CO2 contamination correction in 11C-acetate PET imaging studies"],"dc:type":["Doctoral thesis"],"dc:type.qualificationlevel":["Doctoral"],"dc:type.qualificationname":["PhD - Doctor of Philosophy"]},"updated_at":"2026-07-24T01:48:42Z"}