Accurate tumor tracking remains as a major challenge in radiation therapy. Large margins are added to the clinical target volume (CTV) to ensure the treatment of tumor in presence of patient setup uncertainty and that caused by intra-motion. Fiducial seeds and calypso markers are commonly implanted into the disease sites to further reduce the dose delivery error due to tumor motion. For more accurate dose delivery and improved patient comfort, the use of radioactive tracers in positron emission tomography (PET) as non-invasive tumor markers has been proposed - a concept called emission-guided radiation therapy (EGRT). Instead of using images obtained from a stand-alone PET scanner for treatment guidance, we mount a positron imaging system on a radiation therapy machine. Such an EGRT system is able to track the tumor in real time based on the lines of response (LOR) of the tumor positron events, and perform radiation therapy simultaneously. In this work, we illustrate the EGRT concept using computer simulations and propose a typical treatment scheme. EGRT's advantage on increased dose delivery accuracy is demonstrated using a pancreas tumor case and a lung tumor case without the setup margin and motion margin. The emission process is simulated by Geant4 Application for Tomographic Emission package and Linac dose delivery is simulated using a voxel-based Monte Carlo algorithm. The tumor tracking error can be controlled within 2 mm which indicates margins can be significantly reduced. The dose distributions show that the proposed EGRT can accurately deliver the prescribed dose to the CTV with much less margins. Although still in a preliminary research stage, EGRT has the potential to substantially reduce tumor location uncertainties and to greatly increase the performance of current radiation therapy.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/37277 |
| Date | 20 October 2010 |
| Creators | Fan, Qiyong |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Thesis |
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