Kinetic data structures for the optimization of intensity-modulated radiation therapy treatment plans

Radiation therapy is an indispensable tool in the current fight against many forms of cancer. There are many ways in which radiation can be employed to treat cancer that involve exposing the malignant cancer cells to a heightened dose of radiation in order to shrink or eradicate them. The exposure of cancerous cells to radiation degrades their DNA and induces cell death. One form of this treatment involves using a linear accelerator to create a beam of high energy radiation that is directed to the tumor site. However, healthy tissue can be damaged by the radiation beam as well as the diseased tissue, and treatment plans are designed to minimize this damage as it can harm the patient. Developing a treatment plan is a task of optimizing constraints: ensuring a sufficient dose of radiation reaches the tumor while minimizing healthy tissue exposure. This task is complicated by the natural motion of the tumor and other organs in the patient's body that may occur in response to the patient's normal breathing and other involuntary movements. This dissertation describes a novel technique that utilizes kinetic data structures to model the movement of the tumor and other surrounding organs and find the times at which the radiation dose could be increased so as to ensure the optimal delivery of radiation to the tumor site while reducing the amount of radiation absorbed by the healthy tissues.