The intensity modulated radiation therapy (IMRT) optimizes the beam’s intensity to deliver the prescribed dose to the target while minimizing the radiation exposure to normal structures. The IMRT optimization is a complex optimization problem because of the multiple conflicting objectives in it. Due to the complexity of the optimization, the IMRT treatment planning is still a trial and error process. Hierarchical optimization was proposed to automate the treatment planning process, but its potential has not been demonstrated in a clinical setting. Moreover, hierarchical optimization is slower than the traditional optimization. The dissertation studied a sampling algorithm to reduce the hierarchical optimization time, customized an open source optimization solver to solve the nonlinear optimization formulation and demonstrated the potential of hierarchical optimization to automate the treatment planning process in a clinical setting. We generated the treatment plans of 31 prostate patients by hierarchical optimization using the same criteria as used by planners to prepare the treatment plans at Memorial Sloan Kettering Cancer Center. We found that hierarchical optimization produced the same or better treatment plans than that produced by a planner using the Eclipse treatment planning system. Therefore, the dissertation demonstrated that hierarchical optimization could automate the treatment planning process and shift the paradigm of the treatment planning from manual trial and error to an ideal automated process.
","abstract_html":"<p>The intensity modulated radiation therapy (IMRT) optimizes the beam’s intensity to deliver the prescribed dose to the target while minimizing the radiation exposure to normal structures. The IMRT optimization is a complex optimization problem because of the multiple conflicting objectives in it. Due to the complexity of the optimization, the IMRT treatment planning is still a trial and error process. Hierarchical optimization was proposed to automate the treatment planning process, but its potential has not been demonstrated in a clinical setting. Moreover, hierarchical optimization is slower than the traditional optimization. The dissertation studied a sampling algorithm to reduce the hierarchical optimization time, customized an open source optimization solver to solve the nonlinear optimization formulation and demonstrated the potential of hierarchical optimization to automate the treatment planning process in a clinical setting. We generated the treatment plans of 31 prostate patients by hierarchical optimization using the same criteria as used by planners to prepare the treatment plans at Memorial Sloan Kettering Cancer Center. We found that hierarchical optimization produced the same or better treatment plans than that produced by a planner using the Eclipse treatment planning system. Therefore, the dissertation demonstrated that hierarchical optimization could automate the treatment planning process and shift the paradigm of the treatment planning from manual trial and error to an ideal automated process.</p>","abstract_has_math":false,"creators":["Tiwari, Paras Babu"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Computer Science & Engineering","degree_department":null,"school":null,"contributors":["Yixin Chen","Eric Klein, Roger Chamberlain, Sanmay Das"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-12-15T08:00:00Z","date_published":"2015-12-15T08:00:00Z","updated_at":"2026-07-24T06:13:49Z","subjects":["Automation","Hierarchical Optimization","Medical Physics","Optimization","Treatment Planning","Engineering"],"languages":["English (en)"],"rights":["I have not registered my thesis with the U.S. Copyright Office, and do not intend to."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://openscholarship.wustl.edu/eng_etds/140"],"render_values":[{"text":"https://openscholarship.wustl.edu/eng_etds/140","href":"https://openscholarship.wustl.edu/eng_etds/140","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.7936/K7XW4H26","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Yixin Chen","Eric Klein, Roger Chamberlain, Sanmay Das"]},{"key":"dc:creator","label":"Author","values":["Tiwari, Paras Babu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2015-12-15T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science & Engineering","McKelvey School of Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Automation","Hierarchical Optimization","Medical Physics","Optimization","Treatment Planning","Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English (en)"]},{"key":"dc:rights","label":"Dc Rights","values":["I have not registered my thesis with the U.S. Copyright Office, and do not intend to."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.7936/K7XW4H26","https://openscholarship.wustl.edu/eng_etds/140"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["https://doi.org/10.7936/K7XW4H26"]},{"key":"dc:description.abstract","label":"Abstract","values":["The intensity modulated radiation therapy (IMRT) optimizes the beam’s intensity to deliver the prescribed dose to the target while minimizing the radiation exposure to normal structures. The IMRT optimization is a complex optimization problem because of the multiple conflicting objectives in it. Due to the complexity of the optimization, the IMRT treatment planning is still a trial and error process. Hierarchical optimization was proposed to automate the treatment planning process, but its potential has not been demonstrated in a clinical setting. Moreover, hierarchical optimization is slower than the traditional optimization. The dissertation studied a sampling algorithm to reduce the hierarchical optimization time, customized an open source optimization solver to solve the nonlinear optimization formulation and demonstrated the potential of hierarchical optimization to automate the treatment planning process in a clinical setting. We generated the treatment plans of 31 prostate patients by hierarchical optimization using the same criteria as used by planners to prepare the treatment plans at Memorial Sloan Kettering Cancer Center. We found that hierarchical optimization produced the same or better treatment plans than that produced by a planner using the Eclipse treatment planning system. Therefore, the dissertation demonstrated that hierarchical optimization could automate the treatment planning process and shift the paradigm of the treatment planning from manual trial and error to an ideal automated process.
"]},{"key":"dc:title","label":"Title","values":["Automating Intensity Modulated Radiation Therapy Treatment Planning by using Hierarchical Optimization"]}]}],"canonical_facts":{"dc:contributor":["Yixin Chen","Eric Klein, Roger Chamberlain, Sanmay Das"],"dc:creator":["Tiwari, Paras Babu"],"dc:date.available":["2015-12-15T08:00:00Z"],"dc:description":["https://doi.org/10.7936/K7XW4H26"],"dc:description.abstract":["The intensity modulated radiation therapy (IMRT) optimizes the beam’s intensity to deliver the prescribed dose to the target while minimizing the radiation exposure to normal structures. The IMRT optimization is a complex optimization problem because of the multiple conflicting objectives in it. Due to the complexity of the optimization, the IMRT treatment planning is still a trial and error process. Hierarchical optimization was proposed to automate the treatment planning process, but its potential has not been demonstrated in a clinical setting. Moreover, hierarchical optimization is slower than the traditional optimization. The dissertation studied a sampling algorithm to reduce the hierarchical optimization time, customized an open source optimization solver to solve the nonlinear optimization formulation and demonstrated the potential of hierarchical optimization to automate the treatment planning process in a clinical setting. We generated the treatment plans of 31 prostate patients by hierarchical optimization using the same criteria as used by planners to prepare the treatment plans at Memorial Sloan Kettering Cancer Center. We found that hierarchical optimization produced the same or better treatment plans than that produced by a planner using the Eclipse treatment planning system. Therefore, the dissertation demonstrated that hierarchical optimization could automate the treatment planning process and shift the paradigm of the treatment planning from manual trial and error to an ideal automated process.
"],"dc:identifier":["https://doi.org/10.7936/K7XW4H26","https://openscholarship.wustl.edu/eng_etds/140"],"dc:language":["English (en)"],"dc:rights":["I have not registered my thesis with the U.S. Copyright Office, and do not intend to."],"dc:subject":["Automation","Hierarchical Optimization","Medical Physics","Optimization","Treatment Planning","Engineering"],"dc:title":["Automating Intensity Modulated Radiation Therapy Treatment Planning by using Hierarchical Optimization"],"thesis:degree_discipline":["Computer Science & Engineering","McKelvey School of Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T06:13:49Z"}