Development of a fast Monte Carlo dose calculation system for online adaptive radiation therapy quality assurance

Yuhe Wang, Thomas R. Mazur, Justin C. Park, Deshan Yang, Sasa Mutic, H. Harold Li

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Online adaptive radiation therapy (ART) based on real-time magnetic resonance imaging represents a paradigm-changing treatment scheme. However, conventional quality assurance (QA) methods based on phantom measurements are not feasible with the patient on the treatment couch. The purpose of this work is to develop a fast Monte Carlo system for validating online re-optimized tri-60Co IMRT adaptive plans with both high accuracy and speed. The Monte Carlo system is based on dose planning method (DPM) code with further simplifcation of electron transport and consideration of external magnetic felds. A vendor-provided head model was incorporated into the code. Both GPU acceleration and variance reduction were implemented. Additionally, to facilitate real-time decision support, a C++ GUI was developed for visualizing 3D dose distributions and performing various analyses in an online adaptive setting. A thoroughly validated Monte Carlo code (gPENELOPE) was used to benchmark the new system, named GPUaccelerated DPM with variance reduction (gDPMvr). The comparison using 15 clinical IMRT plans demonstrated that gDPMvr typically runs 43 times faster with only 0.5% loss in accuracy. Moreover, gDPMvr reached 1% local dose uncertainty within 2.3min on average, and thus is well-suited for ART QA.

Original languageEnglish (US)
Pages (from-to)4970-4990
Number of pages21
JournalPhysics in medicine and biology
Volume62
Issue number12
DOIs
StatePublished - May 22 2017
Externally publishedYes

Keywords

  • adaptive radiation therapy
  • GPU
  • Monte Carlo
  • MRI guided radiation therapy
  • variance reduction

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging

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