Convex optimization methods for adaptive radiation therapy
Abstract/Contents
- Abstract
- Cancer radiation therapy involves fundamental tensions between tumor coverage and damage to surrounding healthy tissue. In this dissertation, we present an optimization-based framework for adaptive radiation treatment planning. We focus on two specific planning challenges: (1) satisfying dose-volume constraints and (2) handling nonlinear patient health dynamics. For each situation, we show how to formulate the treatment planning problem as a nonconvex optimization problem and obtain a good estimate of the solution by solving a series of convex approximations. We demonstrate the effectiveness of our method on several clinical examples. Finally, we release an open-source Python library that implements our method using generic convex solvers. The last part of this dissertation concerns applications of optimization beyond radiation therapy. We develop a domain-specific language (DSL) for formulating and solving a broad class of convex optimization problems. Then, we describe an implementation of our DSL in R, a popular programming language for statistical modeling. Our resulting software package, CVXR, allows users to construct optimization problems in a natural mathematical syntax. CVXR automatically verifies the problem's convexity and converts it into the standard form required by a specific solver. We illustrate CVXR's modeling framework with a variety of examples drawn from statistics, engineering, and radiation treatment planning.
Description
Type of resource | text |
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Form | electronic resource; remote; computer; online resource |
Extent | 1 online resource. |
Place | California |
Place | [Stanford, California] |
Publisher | [Stanford University] |
Copyright date | 2021; ©2021 |
Publication date | 2021; 2021 |
Issuance | monographic |
Language | English |
Creators/Contributors
Author | Fu, Anqi | |
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Degree supervisor | Boyd, Stephen P | |
Thesis advisor | Boyd, Stephen P | |
Thesis advisor | Duchi, John | |
Thesis advisor | Narasimhan, Balasubramanian | |
Thesis advisor | Xing, Lei | |
Degree committee member | Duchi, John | |
Degree committee member | Narasimhan, Balasubramanian | |
Degree committee member | Xing, Lei | |
Associated with | Stanford University, Department of Electrical Engineering |
Subjects
Genre | Theses |
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Genre | Text |
Bibliographic information
Statement of responsibility | Anqi Fu. |
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Note | Submitted to the Department of Electrical Engineering. |
Thesis | Thesis Ph.D. Stanford University 2021. |
Location | https://purl.stanford.edu/yk503fd5318 |
Access conditions
- Copyright
- © 2021 by Anqi Fu
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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