End‐to‐end differentiable volumetric modulated arc therapy optimization via computation graph–driven automatic differentiation

Abstract

Conventional volumetric modulated arc therapy (VMAT) optimization relies on gradient‐based methods in which analytical gradients must be explicitly derived for each objective function and machine constraint. This dedicated mathematical derivation imposes substantial barriers to extending the optimization framework with new clinical objectives or delivery constraints, and the resulting CPU‐based implementations do not natively exploit modern GPU hardware.