Description
Central to the planning of radiation therapy for cancer treatment is the calculation of radiation dose delivered to a patient. The optimization and subsequent delivery of a radiation therapy plan is only as good as the accuracy of the underlying radiation dose estimate. This work recasts the radiation dose estimation problem as a supervised regression task, with the goal of approximating slow, but accurate Monte Carlo-based dose calculations to within clinically acceptable error levels by using a fast to run machine learning algorithm.