Applying pytorch toolkit to plan optimization for circular cone based robotic radiotherapy.

ABSTRACT:

Background

Robotic linac is ideally suited to deliver hypo-fractionated radiotherapy due to its compact head and flexible positioning. The non-coplanar treatment space improves the delivery versatility but the complexity also leads to prolonged optimization and treatment time.

Methods

In this study, we attempted to use the deep learning (pytorch) framework for the plan optimization of circular cone based robotic radiotherapy. The optimization problem was topologized into a simple feedforward neural network, thus the treatment plan optimization was transformed into network training. With this transformation, the pytorch toolkit with high-efficiency automatic differentiation (AD) for gradient calculation was used as the optimization solver. To improve the treatment efficiency,

SUBMITTER: Liang B 

PROVIDER: S-EPMC9022303 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature