Operator Splitting for Adaptive Radiation Therapy with Nonlinear Health Dynamics
Optimization Methods and Software, 37(6):2300–2323, 2022.
We present an optimization-based approach to radiation treatment planning
over time. Our approach formulates treatment planning as an optimal control
problem with nonlinear patient health dynamics derived from the standard
linear-quadratic cell survival model. As the formulation is nonconvex, we
propose a method for obtaining an approximate solution by solving a sequence
of convex optimization problems. This method is fast, efficient, and robust
to model error, adapting readily to changes in the patient's health between
treatment sessions. Moreover, we that show it can be combined with the operator
splitting method ADMM to produce an algorithm that is highly scalable and
can handle large clinical cases. We introduce an open-source Python
implementation of our algorithm, AdaRad, and demonstrate its performance
on several examples.