Advanced computing and automation in radiation therapy treatment planning
Abstract/Contents
- Abstract
- \prefacesection{Abstract} The Boltzmann transport equation describes the macroscopic behavior of radiation particles such as neutrons, photons, and electrons as they travel through and interact with matter. The widely known Monte Carlo (MC) method is a general approach to obtaining open form solutions to the linearized Boltzmann transport equation. Monte Carlo algorithm is thte most accurate way to predict how dose is delivered inside a patient. However, the computation time inhibits its routine use in the clinic. For a patient, MC carlo simulation takes anywhere from 10 hours to several days. In this dissertation, we use the state-of-the-art cloud computing technology to accelerate Monte Carlo methods. The approach is evaluated for pencil beams and broad beams of high-energy electrons and photons. The cloud-based Monte Carlo simulation is compared to single-threaded implementation and demonstrates a 47x speed up. Current clinical treatment planning requires multiple trial-and-error adjustments of system model parameters. Producing a treatment plan is time consuming. A team of physician, dosimetrist, and physicist manually adjust parameters in a commercial planning environment. In this dissertation, an autonomous treatment planning technique is implemented in a clinical platform. An outer-loop decision function interacts on-the-fly with an inner-loop clinical treatment planning system (TPS). The approach is applied to 3 head and neck volumetric modulated arc therapy (VMAT) cases and one prostate intensity-modulated radiation therapy (IMRT) case. A strategy of using population-based prior patient data was explored. An upper and lower bound for the dose-volume segments are derived from a group of previously treated patients. The bounds are then used for new case to provide a dosimetric range for acceptability. An heuristic algorithm adjusts the constraints for the optimization using a stochastic approach. Rather than setting a deterministic value for each dose-volume segments, the constraint is changed during each iteration of the outer-loop optimization. The proposed algorithm is applied to a head and neck VMAT case and a prostate IMRT case on a clinical treatment planning system. Results obtained show a comparable dose volume histogram (DVH) compared to manual planning.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Wang, Henry |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering. |
| Primary advisor | Xing, Lei |
| Thesis advisor | Xing, Lei |
| Thesis advisor | Pauly, John (John M.) |
| Thesis advisor | Ye, Yinyu |
| Advisor | Pauly, John (John M.) |
| Advisor | Ye, Yinyu |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Henry Wang. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Huan Wang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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