Convex and Dynamic Optimization with Learning for Adaptive Biologically Conformal Radiotherapy

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dc.contributor.advisorGhate, Archisen_US
dc.contributor.authorSaberian, Fatemehen_US
dc.date.accessioned2015-09-29T21:22:42Z
dc.date.available2015-09-29T21:22:42Z
dc.date.issued2015-09-29
dc.date.submitted2015en_US
dc.descriptionThesis (Ph.D.)--University of Washington, 2015en_US
dc.description.abstractThe research objective of this dissertation is to apply convex and dynamic optimization methods to establish a rigorous mathematical framework called Adaptive Biologically Conformal Radiotherapy (ABCRT) for spatiotemporally integrated radiotherapy planning. The standard log-linear-quadratic survival model is employed to model the tumor’s and the normal tissue’s dose-response throughout this dissertation.en_US
dc.embargo.termsOpen Accessen_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.otherSaberian_washington_0250E_14644.pdfen_US
dc.identifier.urihttp://hdl.handle.net/1773/33982
dc.language.isoen_USen_US
dc.rightsCopyright is held by the individual authors.en_US
dc.subject.otherIndustrial engineeringen_US
dc.subject.otherindustrial engineeringen_US
dc.titleConvex and Dynamic Optimization with Learning for Adaptive Biologically Conformal Radiotherapyen_US
dc.typeThesisen_US

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