Ultra-fast treatment plan optimization for volumetric modulated arc therapy (VMAT)

TL;DR

This paper presents a novel, highly efficient aperture-based algorithm for VMAT treatment plan optimization that produces high-quality, deliverable plans in minutes, significantly improving clinical workflow and dose conformity.

Contribution

The paper introduces a new convex programming and column generation approach for VMAT optimization, achieving rapid plan generation with superior dose distribution.

Findings

Plans deliver lower doses to critical structures compared to IMRT

Algorithm generates plans in 5-8 minutes on CPU and seconds on GPU

Plans are highly conformal and clinically deliverable

Abstract

Purpose: To develop a novel aperture-based algorithm for volumetric modulated arc therapy (VMAT) treatment plan optimization with high quality and high efficiency. Methods: The VMAT optimization problem is formulated as a large-scale convex programming problem solved by a column generation approach. We consider a cost function consisting two terms, the first which enforces a desired dose distribution while the second guarantees a smooth dose rate variation between successive gantry angles. At each iteration of the column generation method, a subproblem is first solved to generate one more deliverable MLC aperture which potentially decreases the cost function most effectively. A subsequent master problem is then solved to determine the dose rate at all currently generated apertures by minimizing the cost function. The iteration of such an algorithm yields a set of deliverable apertures, as well as dose rates, at all gantry angles. Results: The algorithm was preliminarily tested on five prostate and five head-and-neck clinical cases, each with one full gantry rotation and without any couch/collimator rotations. Compared to corresponding co-planar IMRT treatment plans (9 fields for prostate cases and 5 fields for head-and-neck cases), the VMAT plans delivered much lower doses to critical structures and more conformal doses to targets. Moreover, extremely high efficiency has been achieved in our algorithm. It takes only 5~8 minutes on CPU (MATLAB code on an Intel Xeon 2.27 GHz CPU) and 18~31 seconds on GPU (CUDA code on an NVIDIA Tesla C1060 GPU card) to generate such a plan. Conclusions: We have developed an aperture-based VMAT optimization algorithm which can generate clinically deliverable and high quality treatment plans at very high efficiency.