dc.contributor.author | Ziegenhein, P | |
dc.contributor.author | Pirner, S | |
dc.contributor.author | Ph Kamerling, C | |
dc.contributor.author | Oelfke, U | |
dc.date.accessioned | 2020-07-24T15:00:57Z | |
dc.date.issued | 2015-08 | |
dc.identifier.citation | Physics in medicine and biology, 2015, 60 (15), pp. 6097 - 6111 | |
dc.identifier.issn | 0031-9155 | |
dc.identifier.uri | https://repository.icr.ac.uk/handle/internal/3864 | |
dc.identifier.eissn | 1361-6560 | |
dc.identifier.doi | 10.1088/0031-9155/60/15/6097 | |
dc.description.abstract | Monte-Carlo (MC) simulations are considered to be the most accurate method for calculating dose distributions in radiotherapy. Its clinical application, however, still is limited by the long runtimes conventional implementations of MC algorithms require to deliver sufficiently accurate results on high resolution imaging data. In order to overcome this obstacle we developed the software-package PhiMC, which is capable of computing precise dose distributions in a sub-minute time-frame by leveraging the potential of modern many- and multi-core CPU-based computers. PhiMC is based on the well verified dose planning method (DPM). We could demonstrate that PhiMC delivers dose distributions which are in excellent agreement to DPM. The multi-core implementation of PhiMC scales well between different computer architectures and achieves a speed-up of up to 37[Formula: see text] compared to the original DPM code executed on a modern system. Furthermore, we could show that our CPU-based implementation on a modern workstation is between 1.25[Formula: see text] and 1.95[Formula: see text] faster than a well-known GPU implementation of the same simulation method on a NVIDIA Tesla C2050. Since CPUs work on several hundreds of GB RAM the typical GPU memory limitation does not apply for our implementation and high resolution clinical plans can be calculated. | |
dc.format | Print-Electronic | |
dc.format.extent | 6097 - 6111 | |
dc.language | eng | |
dc.language.iso | eng | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | |
dc.subject | Radiotherapy, Computer-Assisted | |
dc.subject | Monte Carlo Method | |
dc.subject | Software | |
dc.title | Fast CPU-based Monte Carlo simulation for radiotherapy dose calculation. | |
dc.type | Journal Article | |
rioxxterms.versionofrecord | 10.1088/0031-9155/60/15/6097 | |
rioxxterms.licenseref.uri | https://creativecommons.org/licenses/by/4.0 | |
rioxxterms.licenseref.startdate | 2015-08 | |
rioxxterms.type | Journal Article/Review | |
dc.relation.isPartOf | Physics in medicine and biology | |
pubs.issue | 15 | |
pubs.notes | Not known | |
pubs.organisational-group | /ICR | |
pubs.organisational-group | /ICR/Primary Group | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Radiotherapy Physics Modelling | |
pubs.organisational-group | /ICR | |
pubs.organisational-group | /ICR/Primary Group | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Radiotherapy Physics Modelling | |
pubs.publication-status | Published | |
pubs.volume | 60 | |
pubs.embargo.terms | Not known | |
icr.researchteam | Radiotherapy Physics Modelling | en_US |
dc.contributor.icrauthor | Oelfke, Uwe | |