Computational methods are now playing a larger role than ever in all aspects of science, including the diagnosis and treatment of disease. No other technology has seen such tremendous advances in the last half century as computer technology. The exponential advances in computer power make it critically important to understand its potential, and leverage its capabilities in our fight against cancer. As a medical physicist, I am interested in applying computational techniques to problems in radiation medicine.
A large and promising branch of computation is optimization, or techniques for selecting values of independent variables that maximize or minimize a particular parameter, or goal. In radiation oncology, there is the goal of maximizing the radiation dose delivered to the tumor region, while at the same time minimizing the dose absorbed by healthy tissue. This is accomplished by varying the independent parameters (such as radiation beam energy, intensity, shape, and delivery geometry) of a treatment plan.
One exciting clinical treatment modality that incorporates these ideas is intensity modulated radiation therapy, or IMRT. Our recent work has focused on applying principles of computation to the optimization of patient-specific parameters in IMRT. We have explored promising optimization techniques, such as the genetic algorithm, which is based on ideas from biological evolution. These methods become even more powerful when implemented in a distributed-computing framework. To this end, we have formed a collaboration with the Center for Computational Research (CCR), an academic supercomputing center that forms part of UB’s Center for Excellence. By employing the CCR’s computational resources, we have applied the genetic algorithm to improve IMRT treatment plans.
Another project involving optimization addresses the patient setup problem: how do we best position a patient for a complex radiation treatment, ensuring that he or she is in the correct 3D location and orientation? We are investigating a novel approach to this problem, by using optical information obtained from a regular digital camera, and applying optimization techniques to determine the 3D geometric parameters accurately. This method will allow us to improve patient setup without subjecting the patient to increased ionizing radiation exposure.
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