Advanced optimization in image-guided radiation therapy
Abstract/Contents
- Abstract
- Radiotherapy is an image-guided intervention, and medical imaging is involved in every key step of the treatment process, ranging from patient staging, simulation, treatment planning, and radiation delivery, to patient follow-up. Image guided-radiation therapy (IGRT) is the most sophisticated method of radiation treatment to address the issue of tumor movement during treatment. IGRT uses advanced imaging technology such as cone-beam CT (CBCT) using on-board imager (OBI) to provide high-resolution, three-dimensional images to pinpoint tumor sites, adjust patient positioning when necessary, and complete a treatment within the standard treatment time slot. Combined with modern technologies in planning and delivering such as intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT), IGRT improves the accuracy of tumor localization while reducing the radiation exposure of healthy tissues. For radiotherapy, repeated CBCT scan of the patient is often required, but excessive radiation exposure is directly related to the risk of polymorphism of genes involved in DNA damage and repair. Hence, we first demonstrate algorithms CBCT for dose reduction. With a low-dose CBCT protocol, the radiation exposure can be mitigated while signal-to-noise ration (SNR) of the measurement is also lowered. It is shown that the high-quality medical image can be reconstructed from noisy CBCT projections by solving an large-scale optimization problem with reasonable preconditioning techniques. In the presence of patient's movement, CBCT projections are highly undersampled, and the precondition in the full-scan case is no longer available. Instead, a first-order method with linearization is adopted for the reconstruction. To further accelerate the convergence speed, we demonstrate a scaling technique in Fourier space. For inverse planning of IGRT, two issues are introduced: beam direction selection and beamlet based optimization. We present an iterative framework suited to modern IMRT/VMAT.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2014 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Choi, Ki Hwan | |
|---|---|---|
| Associated with | Stanford University, Department of Electrical Engineering. | |
| Primary advisor | Xing, Lei | |
| Thesis advisor | Xing, Lei | |
| Thesis advisor | Boyd, Stephen P | |
| Thesis advisor | Pauly, John (John M.) | |
| Advisor | Boyd, Stephen P | |
| Advisor | Pauly, John (John M.) |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Ki Hwan Choi. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2014. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2014 by Ki Hwan Choi
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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