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Fast 3D dosimetric verifications based on an electronic portal imaging device using a GPU calculation engine

PURPOSE: To use a graphic processing unit (GPU) calculation engine to implement a fast 3D pre-treatment dosimetric verification procedure based on an electronic portal imaging device (EPID). METHODS: The GPU algorithm includes the deconvolution and convolution method for the fluence-map calculations...

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Autores principales: Zhu, Jinhan, Chen, Lixin, Chen, Along, Luo, Guangwen, Deng, Xiaowu, Liu, Xiaowei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4399436/
https://www.ncbi.nlm.nih.gov/pubmed/25885567
http://dx.doi.org/10.1186/s13014-015-0387-7
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author Zhu, Jinhan
Chen, Lixin
Chen, Along
Luo, Guangwen
Deng, Xiaowu
Liu, Xiaowei
author_facet Zhu, Jinhan
Chen, Lixin
Chen, Along
Luo, Guangwen
Deng, Xiaowu
Liu, Xiaowei
author_sort Zhu, Jinhan
collection PubMed
description PURPOSE: To use a graphic processing unit (GPU) calculation engine to implement a fast 3D pre-treatment dosimetric verification procedure based on an electronic portal imaging device (EPID). METHODS: The GPU algorithm includes the deconvolution and convolution method for the fluence-map calculations, the collapsed-cone convolution/superposition (CCCS) algorithm for the 3D dose calculations and the 3D gamma evaluation calculations. The results of the GPU-based CCCS algorithm were compared to those of Monte Carlo simulations. The planned and EPID-based reconstructed dose distributions in overridden-to-water phantoms and the original patients were compared for 6 MV and 10 MV photon beams in intensity-modulated radiation therapy (IMRT) treatment plans based on dose differences and gamma analysis. RESULTS: The total single-field dose computation time was less than 8 s, and the gamma evaluation for a 0.1-cm grid resolution was completed in approximately 1 s. The results of the GPU-based CCCS algorithm exhibited good agreement with those of the Monte Carlo simulations. The gamma analysis indicated good agreement between the planned and reconstructed dose distributions for the treatment plans. For the target volume, the differences in the mean dose were less than 1.8%, and the differences in the maximum dose were less than 2.5%. For the critical organs, minor differences were observed between the reconstructed and planned doses. CONCLUSIONS: The GPU calculation engine was used to boost the speed of 3D dose and gamma evaluation calculations, thus offering the possibility of true real-time 3D dosimetric verification.
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spelling pubmed-43994362015-04-17 Fast 3D dosimetric verifications based on an electronic portal imaging device using a GPU calculation engine Zhu, Jinhan Chen, Lixin Chen, Along Luo, Guangwen Deng, Xiaowu Liu, Xiaowei Radiat Oncol Research PURPOSE: To use a graphic processing unit (GPU) calculation engine to implement a fast 3D pre-treatment dosimetric verification procedure based on an electronic portal imaging device (EPID). METHODS: The GPU algorithm includes the deconvolution and convolution method for the fluence-map calculations, the collapsed-cone convolution/superposition (CCCS) algorithm for the 3D dose calculations and the 3D gamma evaluation calculations. The results of the GPU-based CCCS algorithm were compared to those of Monte Carlo simulations. The planned and EPID-based reconstructed dose distributions in overridden-to-water phantoms and the original patients were compared for 6 MV and 10 MV photon beams in intensity-modulated radiation therapy (IMRT) treatment plans based on dose differences and gamma analysis. RESULTS: The total single-field dose computation time was less than 8 s, and the gamma evaluation for a 0.1-cm grid resolution was completed in approximately 1 s. The results of the GPU-based CCCS algorithm exhibited good agreement with those of the Monte Carlo simulations. The gamma analysis indicated good agreement between the planned and reconstructed dose distributions for the treatment plans. For the target volume, the differences in the mean dose were less than 1.8%, and the differences in the maximum dose were less than 2.5%. For the critical organs, minor differences were observed between the reconstructed and planned doses. CONCLUSIONS: The GPU calculation engine was used to boost the speed of 3D dose and gamma evaluation calculations, thus offering the possibility of true real-time 3D dosimetric verification. BioMed Central 2015-04-11 /pmc/articles/PMC4399436/ /pubmed/25885567 http://dx.doi.org/10.1186/s13014-015-0387-7 Text en © Zhu et al.; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Zhu, Jinhan
Chen, Lixin
Chen, Along
Luo, Guangwen
Deng, Xiaowu
Liu, Xiaowei
Fast 3D dosimetric verifications based on an electronic portal imaging device using a GPU calculation engine
title Fast 3D dosimetric verifications based on an electronic portal imaging device using a GPU calculation engine
title_full Fast 3D dosimetric verifications based on an electronic portal imaging device using a GPU calculation engine
title_fullStr Fast 3D dosimetric verifications based on an electronic portal imaging device using a GPU calculation engine
title_full_unstemmed Fast 3D dosimetric verifications based on an electronic portal imaging device using a GPU calculation engine
title_short Fast 3D dosimetric verifications based on an electronic portal imaging device using a GPU calculation engine
title_sort fast 3d dosimetric verifications based on an electronic portal imaging device using a gpu calculation engine
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4399436/
https://www.ncbi.nlm.nih.gov/pubmed/25885567
http://dx.doi.org/10.1186/s13014-015-0387-7
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