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

Chunhua Men, Xun Jia, Steve B. Jiang

Research output: Contribution to journalArticlepeer-review

74 Scopus citations


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 the graphics processing unit (GPU) based on our previous work on the CPU. We formulate the DAO problem as a large-scale convex programming problem, and use an exact method called the column generation approach to deal with its extremely large dimensionality on the GPU. Five 9-field prostate and five 5-field head-and-neck IMRT clinical cases with 5 × 5 mm2 beamlet size and 2.5 × 2.5 × 2.5 mm3 voxel size were tested to evaluate our algorithm on the GPU. It takes only 0.7-3.8 s for our implementation to generate high-quality treatment plans 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.

Original languageEnglish (US)
Pages (from-to)4309-4319
Number of pages11
JournalPhysics in medicine and biology
Issue number15
StatePublished - Aug 7 2010

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging


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