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.
Download data and solver from the links below, and unzip in the same directory as code.
- Main: Functions for loading data, computing fluence maps, and plotting results
- Script: Example script for using FluenceMapOpt
- Examples: Code to reproduce the examples from our paper
- Figures: Figures from our paper
- minConf solver
- CORT dataset
- Prostate case data: ftp://parrot.genomics.cn/gigadb/pub/10.5524/100001_101000/100110/PROSTATE.zip
- Prostate DICOM data: ftp://parrot.genomics.cn/gigadb/pub/10.5524/100001_101000/100110/Prostate_Dicom.zip