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A Fast Online Replanning Algorithm Based on Intensity Field Projection for Adaptive Radiotherapy
Purpose: The purpose of this work was to propose an online replanning algorithm, named intensity field projection (IFP), that directly adjusts intensity distributions for each beam based on the deformation of structures. IFP can be implemented within a reasonably acceptable time frame. Methods and M...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7063069/ https://www.ncbi.nlm.nih.gov/pubmed/32195188 http://dx.doi.org/10.3389/fonc.2020.00287 |
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author | Liu, Xiaomeng Liang, Yueqiang Zhu, Jian Yu, Gang Yu, Yanyan Cao, Qiang Li, X. Allen Li, Baosheng |
author_facet | Liu, Xiaomeng Liang, Yueqiang Zhu, Jian Yu, Gang Yu, Yanyan Cao, Qiang Li, X. Allen Li, Baosheng |
author_sort | Liu, Xiaomeng |
collection | PubMed |
description | Purpose: The purpose of this work was to propose an online replanning algorithm, named intensity field projection (IFP), that directly adjusts intensity distributions for each beam based on the deformation of structures. IFP can be implemented within a reasonably acceptable time frame. Methods and Materials: The online replanning method is based on the gradient-based free form deformation (GFFD) algorithm, which we have previously proposed. The method involves the following steps: The planning computed tomography (CT) and cone-beam CT image are registered to generate a three-dimensional (3-D) deformation field. According to the 3-D deformation field, the registered image and a new delineation are generated. The two-dimensional (2-D) deformation field of ray intensity in each beam direction is determined based on the 3-D deformation field in the region of interest. The 2-D ray intensity distribution in the corresponding beam direction is deformed to generate a new 2-D ray intensity distribution. According to the new 2-D ray intensity distribution, corresponding multi-leaf collimator (MLC), and jaw motion data are generated. The feasibility and advantages of our method have been demonstrated in 20 lung cancer intensity modulated radiation therapy (IMRT) cases. Results: Substantial underdosing in the CTV is seen in the original and the repositioning plans. The average prescription dose coverage (V100%) and D95 for CTV were 100% and 60.3 Gy for the IFP plans compared to 82.6% (P < 0.01) and 44.0 Gy (P < 0.01) for original plans, 86.7% (P < 0.01), and 58.5 Gy (P < 0.01) for repositioning plans. On average, the mean total lung doses were 12.2 Gy for the IFP plan compared to the 12.4 Gy (P < 0.01) and 12.6 Gy (P < 0.01) for the original and the repositioning plans. The entire process of IFP can be completed within 3 min. Conclusions: We proposed an online replanning strategy for automatically correcting interfractional anatomy variations. The preliminary results indicate that the IFP method substantially increased planning speed for online adaptive replanning. |
format | Online Article Text |
id | pubmed-7063069 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-70630692020-03-19 A Fast Online Replanning Algorithm Based on Intensity Field Projection for Adaptive Radiotherapy Liu, Xiaomeng Liang, Yueqiang Zhu, Jian Yu, Gang Yu, Yanyan Cao, Qiang Li, X. Allen Li, Baosheng Front Oncol Oncology Purpose: The purpose of this work was to propose an online replanning algorithm, named intensity field projection (IFP), that directly adjusts intensity distributions for each beam based on the deformation of structures. IFP can be implemented within a reasonably acceptable time frame. Methods and Materials: The online replanning method is based on the gradient-based free form deformation (GFFD) algorithm, which we have previously proposed. The method involves the following steps: The planning computed tomography (CT) and cone-beam CT image are registered to generate a three-dimensional (3-D) deformation field. According to the 3-D deformation field, the registered image and a new delineation are generated. The two-dimensional (2-D) deformation field of ray intensity in each beam direction is determined based on the 3-D deformation field in the region of interest. The 2-D ray intensity distribution in the corresponding beam direction is deformed to generate a new 2-D ray intensity distribution. According to the new 2-D ray intensity distribution, corresponding multi-leaf collimator (MLC), and jaw motion data are generated. The feasibility and advantages of our method have been demonstrated in 20 lung cancer intensity modulated radiation therapy (IMRT) cases. Results: Substantial underdosing in the CTV is seen in the original and the repositioning plans. The average prescription dose coverage (V100%) and D95 for CTV were 100% and 60.3 Gy for the IFP plans compared to 82.6% (P < 0.01) and 44.0 Gy (P < 0.01) for original plans, 86.7% (P < 0.01), and 58.5 Gy (P < 0.01) for repositioning plans. On average, the mean total lung doses were 12.2 Gy for the IFP plan compared to the 12.4 Gy (P < 0.01) and 12.6 Gy (P < 0.01) for the original and the repositioning plans. The entire process of IFP can be completed within 3 min. Conclusions: We proposed an online replanning strategy for automatically correcting interfractional anatomy variations. The preliminary results indicate that the IFP method substantially increased planning speed for online adaptive replanning. Frontiers Media S.A. 2020-03-03 /pmc/articles/PMC7063069/ /pubmed/32195188 http://dx.doi.org/10.3389/fonc.2020.00287 Text en Copyright © 2020 Liu, Liang, Zhu, Yu, Yu, Cao, Li and Li. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Liu, Xiaomeng Liang, Yueqiang Zhu, Jian Yu, Gang Yu, Yanyan Cao, Qiang Li, X. Allen Li, Baosheng A Fast Online Replanning Algorithm Based on Intensity Field Projection for Adaptive Radiotherapy |
title | A Fast Online Replanning Algorithm Based on Intensity Field Projection for Adaptive Radiotherapy |
title_full | A Fast Online Replanning Algorithm Based on Intensity Field Projection for Adaptive Radiotherapy |
title_fullStr | A Fast Online Replanning Algorithm Based on Intensity Field Projection for Adaptive Radiotherapy |
title_full_unstemmed | A Fast Online Replanning Algorithm Based on Intensity Field Projection for Adaptive Radiotherapy |
title_short | A Fast Online Replanning Algorithm Based on Intensity Field Projection for Adaptive Radiotherapy |
title_sort | fast online replanning algorithm based on intensity field projection for adaptive radiotherapy |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7063069/ https://www.ncbi.nlm.nih.gov/pubmed/32195188 http://dx.doi.org/10.3389/fonc.2020.00287 |
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