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 |
|---|---|
| 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 |
|---|---|
| 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 |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Anqi Fu. |
|---|---|
| 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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