# A nonconvex optimization approach to IMRT planning with dose-volume constraints

## TL;DR

This paper introduces a novel relaxation method for nonconvex dose-volume constraints in IMRT planning, enabling more accurate optimization in radiation therapy with proven convergence and adaptability to complex clinical scenarios.

## Contribution

It presents a new relaxation approach and efficient algorithms for nonconvex dose-volume constraints in IMRT, improving upon previous convex approximation methods.

## Key findings

- Demonstrated effectiveness on the CORT dataset
- Algorithms are provably convergent
- Adaptable to multiple tumors and organs-at-risk

## Abstract

Fluence map optimization for intensity-modulated radiation therapy planning can be formulated as a large-scale inverse problem with competing objectives and constraints associated with the tumors and organs-at-risk. Unfortunately, clinically relevant dose-volume constraints are nonconvex, so standard algorithms for convex problems cannot be directly applied. While prior work focused on convex approximations for these constraints, we propose a novel relaxation approach to handle nonconvex dose-volume constraints. 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 and organs-at-risk.