Beam Orientation Optimization for Intensity Modulated Radiation Therapy using Adaptive l1 Minimization

TL;DR

This paper introduces an adaptive l1 minimization algorithm for beam orientation optimization in IMRT, effectively selecting fewer beam angles to improve treatment plan quality while reducing complexity.

Contribution

The paper presents a novel adaptive l1 minimization approach for BOO that adaptively adjusts beam angle weights to identify and remove unimportant beams.

Findings

Optimized beam angles yield better plan quality than equiangular configurations.

The algorithm converges reliably and effectively selects important beam angles.

Validated across multiple prostate and head-and-neck cases with consistent improvements.

Abstract

Beam orientation optimization (BOO) is a key component in the process of IMRT treatment planning. It determines to what degree one can achieve a good treatment plan quality in the subsequent plan optimization process. In this paper, we have developed a BOO algorithm via adaptive l_1 minimization. Specifically, we introduce a sparsity energy function term into our model which contains weighting factors for each beam angle adaptively adjusted during the optimization process. Such an energy term favors small number of beam angles. By optimizing a total energy function containing a dosimetric term and the sparsity term, we are able to identify the unimportant beam angles and gradually remove them without largely sacrificing the dosimetric objective. In one typical prostate case, the convergence property of our algorithm, as well as the how the beam angles are selected during the optimization process, is demonstrated. Fluence map optimization (FMO) is then performed based on the optimized beam angles. The resulted plan quality is presented and found to be better than that obtained from unoptimized (equiangular) beam orientations. We have further systematically validated our algorithm in the contexts of 5-9 coplanar beams for 5 prostate cases and 1 head and neck case. For each case, the final FMO objective function value is used to compare the optimized beam orientations and the equiangular ones. It is found that, our BOO algorithm can lead to beam configurations which attain lower FMO objective function values than corresponding equiangular cases, indicating the effectiveness of our BOO algorithm.