Fluence map optimization for intensity-modulated radiation therapy planning can be formulated as a large-scale inverse problem with multi-objectives on the tumors URLans-at-risk. Unfortunately, clinically relevant dose-volume constraints are nonconvex, so convex formulations and algorithms cannot be directly applied to the problem. We propose a novel approach to handle dose-volume constraints while preserving their nonconvexity, as opposed to previous efforts which focused on convex approximations. The proposed method is amenable to efficient algorithms based on partial minimization and naturally adapts to handle maximum-dose constraints and cases of infeasibility . We demonstrate our approach using the CORT dataset, and show that it is easily adaptable to radiation treatment planning with dose-volume constraints for multiple tumors URLans-at-risk.
Fluence map optimization for intensity-modulated radiation therapy planning can be formulated as a large-scale inverse problem with multi-objectives on the tumors URLans-at-risk. Unfortunately, clinically relevant dose-volume constraints are nonconvex, so convex formulations and algorithms cannot be directly applied to the problem. We propose a novel approach to handle dose-volume constraints while preserving their nonconvexity, as opposed to previous efforts which focused on convex approximations. We develop efficient, provably convergent algorithms based on partial minimization , and show how to adapt them to handle maximum-dose constraints and infeasible problems . We demonstrate our approach using the CORT dataset, and show that it is easily adaptable to radiation treatment planning with dose-volume constraints for multiple tumors URLans-at-risk.
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The proposed method is amenable to efficient
We develop efficient, provably convergent
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and naturally adapts to
, and show how to adapt them to
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cases of infeasibility
infeasible problems