In radiation therapy, ionizing radiation is applied to cancerous tissue, damaging the DNA and interfering with the ability of the cancerous cells to grow and divide. Healthy cells are also damaged by the radiation, but they are more able to repair the damage and return to normal function. Treatment plans, which specify the directions of the applied radiation beams, times of exposure, etc., should be designed in a way that delivers a specified dose to the tumor while avoiding an excessive dose to the surrounding healthy tissue and, in particular, to any important nearby organs.
Devices for delivering the radiation allow a significant amount of control over the characteristics of the radiation. For instance, the beam can be shaped and its intensity varied across its width. Newer devices allow even greater control, and consequently even more degrees of freedom in treatment planning. The full potential of these devices to deliver optimal treatment plans has however yet to be realized, due to the complexity of the treatment design process.
By using advanced modeling techniques and state-of-the-art optimization algorithms, this project aims to provide radiation oncologists with important new computational tools for treatment planning. These tools will be flexible enough to adapt to the varying priorities of different planners and different patients, fast enough to be used in clinical practice (where plans must often be formulated or refined in real time), and robust enough to give good solutions to the most difficult planning problems.
The work will focus on three types of radiation therapy in widespread use: the step-and-shoot and IMAT approaches to intensity-modulated radiation therapy, and the Gamma Knife radiosurgery system for treatment of brain tumors. Each of these three therapies has distinct features, but there is substantial commonality in the modeling and optimization issues. Consequently, the methodology and tools will also be applicable to the next generation of radiation therapy devices.