Abstract: The objective in this thesis is to apply dynamic and convex optimization as well as robust optimization techniques to tackle the external radiotherapy problem. The model undertaken in this thesis is based on the famous linear-quadratic (LQ) dose-response model. The mathematical model achieved by such formulation is generally a non-convex quadratically constrained quadratic problem (QCQP). We offer several approaches to solve this problem and address the inherent uncertainties in the formulation. The methods employed are convex optimization, dynamic programming, and robust optimization techniques to tackle inherent radiobiological uncertainties in radiation therapy treatment planning.
Publications:
Ajdari, A., Ghate, A. (2016). Robust spatiotemporally integrated fractionation in radiotherapy. Operations Research Letter. 44(4): 544-549.
Ajdari, A, Ghate, A, Kim, M. (2018). Adaptive treatment-length optimization in spatiobiologically integrated radiotherapy, Physics in Medicine & Biology 63(7):075009.
Ajdari, A, Saberian, F, Ghate, A. (2018). A theoretical framework for learning tumor dose-response uncertainty in individualized spatiobiologically integrated radiotherapy, INFORMS Journal on Computing (accepted for publication).
