Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Xiaomeng, Liang, Yueqiang, Zhu, Jian, Yu, Gang, Yu, Yanyan, Cao, Qiang, Li, X. Allen, Li, Baosheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
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
_version_ 1783504638415208448
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
work_keys_str_mv AT liuxiaomeng afastonlinereplanningalgorithmbasedonintensityfieldprojectionforadaptiveradiotherapy
AT liangyueqiang afastonlinereplanningalgorithmbasedonintensityfieldprojectionforadaptiveradiotherapy
AT zhujian afastonlinereplanningalgorithmbasedonintensityfieldprojectionforadaptiveradiotherapy
AT yugang afastonlinereplanningalgorithmbasedonintensityfieldprojectionforadaptiveradiotherapy
AT yuyanyan afastonlinereplanningalgorithmbasedonintensityfieldprojectionforadaptiveradiotherapy
AT caoqiang afastonlinereplanningalgorithmbasedonintensityfieldprojectionforadaptiveradiotherapy
AT lixallen afastonlinereplanningalgorithmbasedonintensityfieldprojectionforadaptiveradiotherapy
AT libaosheng afastonlinereplanningalgorithmbasedonintensityfieldprojectionforadaptiveradiotherapy
AT liuxiaomeng fastonlinereplanningalgorithmbasedonintensityfieldprojectionforadaptiveradiotherapy
AT liangyueqiang fastonlinereplanningalgorithmbasedonintensityfieldprojectionforadaptiveradiotherapy
AT zhujian fastonlinereplanningalgorithmbasedonintensityfieldprojectionforadaptiveradiotherapy
AT yugang fastonlinereplanningalgorithmbasedonintensityfieldprojectionforadaptiveradiotherapy
AT yuyanyan fastonlinereplanningalgorithmbasedonintensityfieldprojectionforadaptiveradiotherapy
AT caoqiang fastonlinereplanningalgorithmbasedonintensityfieldprojectionforadaptiveradiotherapy
AT lixallen fastonlinereplanningalgorithmbasedonintensityfieldprojectionforadaptiveradiotherapy
AT libaosheng fastonlinereplanningalgorithmbasedonintensityfieldprojectionforadaptiveradiotherapy