Optimization Approaches for Intensity Modulated Proton Therapy Treatment Planning

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dc.contributor.advisorZinchenko, Yuriy
dc.contributor.authorMousazadeh, Bahar
dc.contributor.committeememberGreenberg, Matthew
dc.contributor.committeememberBadescu, Alexandru
dc.date2023-11
dc.date.accessioned2023-08-03T19:24:54Z
dc.date.available2023-08-03T19:24:54Z
dc.date.issued2023-07
dc.description.abstractRadiation therapy is a critical modality in the field of oncology. The primary goal of radiation therapy is to destroy or control the growth of cancerous cells while minimizing damage to healthy tissues. Intensity Modulated Proton Therapy (IMPT) is a type of radiation therapy that utilizes protons to irradiate the tumor. The unique physical properties of protons enable precise control over the radiation dose distribution within the tumor and more effective sparing of healthy tissues. Typically, radiation therapy treatment planning is posed as a multi-criteria optimization problem, whereby the challenge is finding the best possible treatment plan. In this study, we formulate and compare two optimization approaches for IMPT treatment planning. We first explore a linear programming (LP) approach, followed by a moment-based approach where we incorporate the dose-volume requirements into the fluence map optimization (FMO) problem. The evaluation of these models is conducted using anonymized patient data corresponding to a lung cancer case, with a focus on generating a good-quality initial plan that is amenable to further refinement. The moment-based approach has a drawback in terms of its high memory usage. To mitigate this limitation, we explore several sparsification strategies aimed at reducing memory requirements. Employing an aggressive sparsification method, we demonstrate that the moment-based approach outperforms the LP model in dosimetric outcomes and computational run-time. We highlight a trade-off between the quality of the treatment plan and computational run-time when utilizing different sparcification strategies for the moment-based approach. By adopting a less strict sparsification method, we anticipate achieving higher-quality treatment plans at the expense of increased computational run-time.
dc.identifier.citationMousazadeh, B. (2023). Optimization approaches for Intensity Modulated Proton Therapy treatment planning (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.urihttps://hdl.handle.net/1880/116823
dc.identifier.urihttps://dx.doi.org/10.11575/PRISM/41665
dc.language.isoen
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgary
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectOptimization
dc.subjectIntensity Modulated Proton Therapy
dc.subjectTreatment Planning
dc.subjectCancer
dc.subjectLinear Programming
dc.subjectMoment
dc.subject.classificationEducation--Mathematics
dc.titleOptimization Approaches for Intensity Modulated Proton Therapy Treatment Planning
dc.typemaster thesis
thesis.degree.disciplineMathematics & Statistics
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.thesis.accesssetbystudentI do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.
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