Project Abstract
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.
