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Real-time auto-adaptive margin generation for MLC-tracked radiotherapy

In radiotherapy, abdominal and thoracic sites are candidates for performing motion tracking. With real-time control it is possible to adjust the multileaf collimator (MLC) position to the target position. However, positions are not perfectly matched and position errors arise from system delays and c...

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Autores principales: Glitzner, M, Fast, M F, de Senneville, B Denis, Nill, S, Oelfke, U, Lagendijk, J J W, Raaymakers, B W, Crijns, S P M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IOP Publishing 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5952335/
https://www.ncbi.nlm.nih.gov/pubmed/27991457
http://dx.doi.org/10.1088/1361-6560/62/1/186
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author Glitzner, M
Fast, M F
de Senneville, B Denis
Nill, S
Oelfke, U
Lagendijk, J J W
Raaymakers, B W
Crijns, S P M
author_facet Glitzner, M
Fast, M F
de Senneville, B Denis
Nill, S
Oelfke, U
Lagendijk, J J W
Raaymakers, B W
Crijns, S P M
author_sort Glitzner, M
collection PubMed
description In radiotherapy, abdominal and thoracic sites are candidates for performing motion tracking. With real-time control it is possible to adjust the multileaf collimator (MLC) position to the target position. However, positions are not perfectly matched and position errors arise from system delays and complicated response of the electromechanic MLC system. Although, it is possible to compensate parts of these errors by using predictors, residual errors remain and need to be compensated to retain target coverage. This work presents a method to statistically describe tracking errors and to automatically derive a patient-specific, per-segment margin to compensate the arising underdosage on-line, i.e. during plan delivery. The statistics of the geometric error between intended and actual machine position are derived using kernel density estimators. Subsequently a margin is calculated on-line according to a selected coverage parameter, which determines the amount of accepted underdosage. The margin is then applied onto the actual segment to accommodate the positioning errors in the enlarged segment. The proof-of-concept was tested in an on-line tracking experiment and showed the ability to recover underdosages for two test cases, increasing [Formula: see text] in the underdosed area about [Formula: see text] and [Formula: see text] , respectively. The used dose model was able to predict the loss of dose due to tracking errors and could be used to infer the necessary margins. The implementation had a running time of 23 ms which is compatible with real-time requirements of MLC tracking systems. The auto-adaptivity to machine and patient characteristics makes the technique a generic yet intuitive candidate to avoid underdosages due to MLC tracking errors.
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spelling pubmed-59523352018-05-18 Real-time auto-adaptive margin generation for MLC-tracked radiotherapy Glitzner, M Fast, M F de Senneville, B Denis Nill, S Oelfke, U Lagendijk, J J W Raaymakers, B W Crijns, S P M Phys Med Biol Paper In radiotherapy, abdominal and thoracic sites are candidates for performing motion tracking. With real-time control it is possible to adjust the multileaf collimator (MLC) position to the target position. However, positions are not perfectly matched and position errors arise from system delays and complicated response of the electromechanic MLC system. Although, it is possible to compensate parts of these errors by using predictors, residual errors remain and need to be compensated to retain target coverage. This work presents a method to statistically describe tracking errors and to automatically derive a patient-specific, per-segment margin to compensate the arising underdosage on-line, i.e. during plan delivery. The statistics of the geometric error between intended and actual machine position are derived using kernel density estimators. Subsequently a margin is calculated on-line according to a selected coverage parameter, which determines the amount of accepted underdosage. The margin is then applied onto the actual segment to accommodate the positioning errors in the enlarged segment. The proof-of-concept was tested in an on-line tracking experiment and showed the ability to recover underdosages for two test cases, increasing [Formula: see text] in the underdosed area about [Formula: see text] and [Formula: see text] , respectively. The used dose model was able to predict the loss of dose due to tracking errors and could be used to infer the necessary margins. The implementation had a running time of 23 ms which is compatible with real-time requirements of MLC tracking systems. The auto-adaptivity to machine and patient characteristics makes the technique a generic yet intuitive candidate to avoid underdosages due to MLC tracking errors. IOP Publishing 2017-01-07 2016-12-16 /pmc/articles/PMC5952335/ /pubmed/27991457 http://dx.doi.org/10.1088/1361-6560/62/1/186 Text en © 2016 Institute of Physics and Engineering in Medicine http://creativecommons.org/licenses/by/3.0/ Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence (http://creativecommons.org/licenses/by/3.0) . Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
spellingShingle Paper
Glitzner, M
Fast, M F
de Senneville, B Denis
Nill, S
Oelfke, U
Lagendijk, J J W
Raaymakers, B W
Crijns, S P M
Real-time auto-adaptive margin generation for MLC-tracked radiotherapy
title Real-time auto-adaptive margin generation for MLC-tracked radiotherapy
title_full Real-time auto-adaptive margin generation for MLC-tracked radiotherapy
title_fullStr Real-time auto-adaptive margin generation for MLC-tracked radiotherapy
title_full_unstemmed Real-time auto-adaptive margin generation for MLC-tracked radiotherapy
title_short Real-time auto-adaptive margin generation for MLC-tracked radiotherapy
title_sort real-time auto-adaptive margin generation for mlc-tracked radiotherapy
topic Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5952335/
https://www.ncbi.nlm.nih.gov/pubmed/27991457
http://dx.doi.org/10.1088/1361-6560/62/1/186
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