Development of a GPU-superposition Monte Carlo code for fast dose calculation in magnetic fields. (21st June 2022)
- Record Type:
- Journal Article
- Title:
- Development of a GPU-superposition Monte Carlo code for fast dose calculation in magnetic fields. (21st June 2022)
- Main Title:
- Development of a GPU-superposition Monte Carlo code for fast dose calculation in magnetic fields
- Authors:
- Li, Yongbao
Sun, Wenzhao
Liu, Hongdong
Ding, Shouliang
Wang, Bin
Huang, Xiaoyan
Song, Ting - Abstract:
- Abstract: Objective. To develop and validate a graphics processing unit (GPU) based superposition Monte Carlo (SMC) code for efficient and accurate dose calculation in magnetic fields. Approach. A series of mono-energy photons ranging from 25 keV to 7.7 MeV were simulated with EGSnrc in a water phantom to generate particle tracks database. SMC physics was extended with charged particle transport in magnetic fields and subsequently programmed on GPU as gSMC. Optimized simulation scheme was designed by combining variance reduction techniques to relieve the thread divergence issue in general GPU-MC codes and improve the calculation efficiency. The gSMC code's dose calculation accuracy and efficiency were assessed through both phantoms and patient cases. Main results. gSMC accurately calculated the dose in various phantoms for both B = 0 T and B = 1.5 T, and it matched EGSnrc well with a root mean square error of less than 1.0% for the entire depth dose region. Patient cases validation also showed a high dose agreement with EGSnrc with 3D gamma passing rate (2%/2 mm) large than 97% for all tested tumor sites. Combined with photon splitting and particle track repeating techniques, gSMC resolved the thread divergence issue and showed an efficiency gain of 186–304 relative to EGSnrc with 10 CPU threads. Significance. A GPU-superposition Monte Carlo code called gSMC was developed and validated for dose calculation in magnetic fields. The developed code's high calculation accuracyAbstract: Objective. To develop and validate a graphics processing unit (GPU) based superposition Monte Carlo (SMC) code for efficient and accurate dose calculation in magnetic fields. Approach. A series of mono-energy photons ranging from 25 keV to 7.7 MeV were simulated with EGSnrc in a water phantom to generate particle tracks database. SMC physics was extended with charged particle transport in magnetic fields and subsequently programmed on GPU as gSMC. Optimized simulation scheme was designed by combining variance reduction techniques to relieve the thread divergence issue in general GPU-MC codes and improve the calculation efficiency. The gSMC code's dose calculation accuracy and efficiency were assessed through both phantoms and patient cases. Main results. gSMC accurately calculated the dose in various phantoms for both B = 0 T and B = 1.5 T, and it matched EGSnrc well with a root mean square error of less than 1.0% for the entire depth dose region. Patient cases validation also showed a high dose agreement with EGSnrc with 3D gamma passing rate (2%/2 mm) large than 97% for all tested tumor sites. Combined with photon splitting and particle track repeating techniques, gSMC resolved the thread divergence issue and showed an efficiency gain of 186–304 relative to EGSnrc with 10 CPU threads. Significance. A GPU-superposition Monte Carlo code called gSMC was developed and validated for dose calculation in magnetic fields. The developed code's high calculation accuracy and efficiency make it suitable for dose calculation tasks in online adaptive radiotherapy with MR-LINAC. … (more)
- Is Part Of:
- Physics in medicine & biology. Volume 67:Number 12(2022)
- Journal:
- Physics in medicine & biology
- Issue:
- Volume 67:Number 12(2022)
- Issue Display:
- Volume 67, Issue 12 (2022)
- Year:
- 2022
- Volume:
- 67
- Issue:
- 12
- Issue Sort Value:
- 2022-0067-0012-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-06-21
- Subjects:
- superposition Monte Carlo -- graphics processing unit -- variance reduction -- MR-LINAC
Biophysics -- Periodicals
Medical physics -- Periodicals
610.153 - Journal URLs:
- http://ioppublishing.org/ ↗
http://iopscience.iop.org/0031-9155 ↗ - DOI:
- 10.1088/1361-6560/ac7194 ↗
- Languages:
- English
- ISSNs:
- 0031-9155
- Deposit Type:
- Legaldeposit
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