Positron emission tomography (PET)-guided dynamic tumor tracking for radiation therapy

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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.


Type of resource text
Form electronic; electronic resource; remote
Extent 1 online resource.
Publication date 2014
Issuance monographic
Language English


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.)


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

© 2014 by Jaewon Yang
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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