Positron emission tomography (PET)-guided dynamic tumor tracking for radiation therapy
Abstract/Contents
- Abstract
- External beam radiation therapy plays an important role in the treatment of lung cancers. The success of this treatment depends on motion management, as lung tumors move during treatment due to respiration. The ultimate strategy for motion management is dynamic tumor tracking by anatomical imaging systems such as X-ray, ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI). However, positron emission tomography (PET), a functional imaging system, has not been incorporated with radiotherapy systems due to the limitations of PET in spite of higher sensitivity and specificity of PET for lung cancer. In the first phase of the dissertation, an investigation was performed into the potential and feasibility of PET for dynamic lung tumor tracking. We developed a tracking algorithm and focused on evaluating the accuracy of the algorithm, simulating real-time applications in a dynamic phantom study. In a commercial PET scanner, list-mode PET data were acquired from a phantom programmed to move with tumor and respiratory motion traces measured during radiotherapy. The results give an estimate of the real-world accuracy of the algorithm, showing an overall time-averaged error of approxi-mately 2 mm. The dynamic PET tumor targeting method was applied to the list mode PET data of lung cancer patients, demonstrating clinical feasibility of real-time tumor tracking. To maximize PET-tracking sensitivity in the presence of motion, it is important to maintain breathing regularity, which links to the second phase of this dissertation. In the second phase of the dissertation, we demonstrated the impact of audiovisual (AV) biofeedback on PET. AV biofeedback, a respiratory training system for patients, has been demonstrated to improve breathing regularity in the previous studies. In a dynamic phantom study, AV biofeedback significantly reduced motion blurring artifacts, but in a 5-patient study, AV biofeedback did not demonstrate statistically significant results. The findings from the patient study will be used to optimize the human-computer interface and include patient training sessions for improved comprehension and capability.
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 | Yang, Jaewon |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering. |
| Primary advisor | Graves, Edward (Edward Elliot), 1974- |
| Thesis advisor | Graves, Edward (Edward Elliot), 1974- |
| Thesis advisor | Keall, Paul |
| Thesis advisor | Levin, Craig |
| Thesis advisor | Pauly, John (John M.) |
| Advisor | Keall, Paul |
| Advisor | Levin, Craig |
| Advisor | Pauly, John (John M.) |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Jaewon Yang. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Ph.D. Stanford University 2014 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2014 by Jaewon Yang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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