Operator splitting for adaptive radiation therapy with nonlinear health dynamics. (2nd November 2022)

• Record Type: Journal Article
• Authors: Fu, Anqi; Xing, Lei; Boyd, Stephen
• Is Part Of: Optimization methods and software. Volume 37:Number 6(2022)
• Page Start: 2300
• Page End: 2323
• Publication Date: 2022-11-02
• Subjects: Radiation therapy -- treatment planning -- nonconvex optimization -- distributed optimization -- optimal control -- model predictive control -- mathematical programming; 90C26 -- 90C06 -- 90C90 -- 92C50; Mathematical optimization -- Periodicals; Algorithms -- Periodicals; 519.7
• DOI: 10.1080/10556788.2022.2078824
• Languages: English
• ISSNs: 1055-6788

Abstract

ABSTRACT: 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 show that 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.