Angle optimization of intensity modulated radiation therapy via learning algorithms

The internship describes a novel method for improvement of Intensity Modulated Radiation Therapy for cancer treatment. The intern’s team will implement a learning algorithm approach to design the number and value of orientations of the gantry that will optimize the radiation dose to the planned target volume while at the same time minimizing dose and damage to organs at risk. This is very important for cases such as prostate cancer, and will significantly reduce the time in designing cancer treatment protocols. To achieve this, the intern will code the learning algorithm, design optimal cancer treatment protocols for phantoms, compare them with protocols designed by current state-of-the-art methods, and then design for true patient data. This method is expected to yield highly improved dose-volume histograms in a much shorter time as compared to current state-of-the-art inverse planning methods.

Intern: 
Mark Sak
Faculty Supervisor: 
Dr. Chitra Rangan
Province: 
Ontario
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