GPU-based ultra-fast direct aperture optimization for online adaptive radiation therapy

TL;DR

This paper introduces a GPU-accelerated direct aperture optimization algorithm for online adaptive radiation therapy, achieving real-time treatment plan generation that significantly advances clinical feasibility.

Contribution

The work develops a GPU-based implementation of DAO using convex programming and column generation, enabling ultra-fast treatment planning for online ART.

Findings

Plan generation time: 0.7 to 2.5 seconds

Applicable to prostate and head-and-neck cases

Utilizes 50 million beamlet apertures

Abstract

Online adaptive radiation therapy (ART) has great promise to significantly reduce normal tissue toxicity and/or improve tumor control through real-time treatment adaptations based on the current patient anatomy. However, the major technical obstacle for clinical realization of online ART, namely the inability to achieve real-time efficiency in treatment re-planning, has yet to be solved. To overcome this challenge, this paper presents our work on the implementation of an intensity modulated radiation therapy (IMRT) direct aperture optimization (DAO) algorithm on graphics processing unit (GPU) based on our previous work on CPU. We formulate the DAO problem as a large-scale convex programming problem, and use an exact method called column generation approach to deal with its extremely large dimensionality on GPU. Five 9-field prostate and five 5-field head-and-neck IMRT clinical cases with 5\times5 mm2 beamlet size and 2.5\times2.5\times2.5 mm3 voxel size were used to evaluate our algorithm on GPU. It takes only 0.7~2.5 seconds for our implementation to generate optimal treatment plans using 50 MLC apertures on an NVIDIA Tesla C1060 GPU card. Our work has therefore solved a major problem in developing ultra-fast (re-)planning technologies for online ART.