Convex methods for radiation treatment planning
Abstract/Contents
- Abstract
- Upwards of 1.6 million cases of cancer are diagnosed each year in the US alone, and two-thirds of those patients receive radiation therapy (RT). For each such patient, clinicians design an individualized treatment plan based on anatomy scans and the capabilities of their clinic's radiation delivery hardware. Treatment planning involves fundamental tensions between tumor coverage and toxicity to surrounding healthy tissues. Increasingly sophisticated RT hardware and planning algorithms have been designed to address these challenges, but come with commensurate increases in computational burden. This thesis addresses two planning challenges common to all modern RT modalities: (1) efficiently solving large scale intensity optimization problems, and (2) satisfying the (nonconvex) radiation dose constraints derived from clinical trials. We formulate the treatment planning problem as a convex minimization. We introduce methods for approximately solving treatment planning problems at dramatically reduced computational cost, and appeal to our convex problem formulation and duality theory to obtain bounds on the suboptimality of these approximations. We then present a framework for handling all desired clinical objectives via convex surrogates. We implement these methods as open-source software packages that make use of modern, general purpose convex solvers. Collectively, this thesis lays the groundwork for a core, modality-agnostic, intensity optimization module that can incorporated as-is into virtually all planning algorithms used in current practice or proposed in the literature.
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 | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Ungun, Baris |
|---|---|
| Degree supervisor | Xing, Lei |
| Thesis advisor | Xing, Lei |
| Thesis advisor | Altman, Russ |
| Thesis advisor | Boyd, Stephen P |
| Degree committee member | Altman, Russ |
| Degree committee member | Boyd, Stephen P |
| Associated with | Stanford University, Department of Bioengineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Baris Ungun. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Baris Ungun
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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