{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/86737"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/86737","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"New Methods in Optimization Algorithms and Technology Applied to VMAT and IMRT Inverse Treatment Planning","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Mathews, Joshua; 0000-0003-2565-8186"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Nazareth, Daryl","Radiology"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-02-21T21:44:34Z","date_published":"2025-02-21T21:44:34Z","updated_at":"2026-07-27T19:05:34Z","subjects":["computational physics","applied physics"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/86737","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Nazareth, Daryl","Radiology"]},{"key":"dc:creator","label":"Author","values":["Mathews, Joshua; 0000-0003-2565-8186"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-02-21T21:44:34Z","2020"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["computational physics","applied physics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/86737"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","A TPS produces IMRT and VMAT plans by applying an optimization process to an objective function, followed by an accurate calculation of the final, deliverable dose. We developed two novel methods to optimize these plans. The first method involves using Monte Carlo (MC) routines. MC is considered to be the gold standard of dose calculation due to its high accuracy; however, it is currently too slow for practical comprehensive VMAT optimization. Instead, we developed an approach called enhanced optimization (EO), which employs the TPS VMAT plan as a starting point, and applies small perturbations, called beamlets, which are calculated using MC, to nudge the solution closer to a true objective minimum. A significant framework was developed in order to efficiently run parallelized MC simulations. The second method involves optimization of IMRT plans using new computer hardware: the Hamiltonian Engine for Radiotherapy Optimization (HERO). For both proposed novel methods, DICOM files for clinical VMAT plans files are exported from the TPS and used to generate input files for the EGSnrc MC toolkit or the HERO. For EO, a simple greedy search algorithm is applied to minimize the objective function, and the resulting modified control point parameters are imported into the TPS to calculate the final, deliverable dose, and to compare the EO plan with the original. EO produced improved objective scores (by 6% to 60%) and DVH's for the brain plans and the head and neck plans. Although EO also reduced the objective scores for the prostate plans (by 46% and 79%), their absolute score and DVH improvements were not substantial. Further development will reduce the EO beamlet computation time and result in more sophisticated EO treatment planning methods. The HERO was able to quickly (under 45 seconds each) find solutions to seven prostate IMRT plans and produced objective scores 93.8% to 99.3% lower than the TPS objective scores. In the future, these novel methods will be useful clinically in finding more optimal solutions to IMRT and VMAT optimization problems.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["New Methods in Optimization Algorithms and Technology Applied to VMAT and IMRT Inverse Treatment Planning"]}]}],"canonical_facts":{"dc:contributor":["Nazareth, Daryl","Radiology"],"dc:creator":["Mathews, Joshua; 0000-0003-2565-8186"],"dc:date":["2025-02-21T21:44:34Z","2020"],"dc:description":["Ph.D.","A TPS produces IMRT and VMAT plans by applying an optimization process to an objective function, followed by an accurate calculation of the final, deliverable dose. We developed two novel methods to optimize these plans. The first method involves using Monte Carlo (MC) routines. MC is considered to be the gold standard of dose calculation due to its high accuracy; however, it is currently too slow for practical comprehensive VMAT optimization. Instead, we developed an approach called enhanced optimization (EO), which employs the TPS VMAT plan as a starting point, and applies small perturbations, called beamlets, which are calculated using MC, to nudge the solution closer to a true objective minimum. A significant framework was developed in order to efficiently run parallelized MC simulations. The second method involves optimization of IMRT plans using new computer hardware: the Hamiltonian Engine for Radiotherapy Optimization (HERO). For both proposed novel methods, DICOM files for clinical VMAT plans files are exported from the TPS and used to generate input files for the EGSnrc MC toolkit or the HERO. For EO, a simple greedy search algorithm is applied to minimize the objective function, and the resulting modified control point parameters are imported into the TPS to calculate the final, deliverable dose, and to compare the EO plan with the original. EO produced improved objective scores (by 6% to 60%) and DVH's for the brain plans and the head and neck plans. Although EO also reduced the objective scores for the prostate plans (by 46% and 79%), their absolute score and DVH improvements were not substantial. Further development will reduce the EO beamlet computation time and result in more sophisticated EO treatment planning methods. The HERO was able to quickly (under 45 seconds each) find solutions to seven prostate IMRT plans and produced objective scores 93.8% to 99.3% lower than the TPS objective scores. In the future, these novel methods will be useful clinically in finding more optimal solutions to IMRT and VMAT optimization problems.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/86737"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["computational physics","applied physics"],"dc:title":["New Methods in Optimization Algorithms and Technology Applied to VMAT and IMRT Inverse Treatment Planning"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:34Z"}