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Use of GammaPlan convolution algorithm for dose calculation on CT and cone-beam CT images

PURPOSE: The aim of this study was to assess the suitability of using cone-beam computed tomography images (CBCTs) produced in a Leksell Gamma Knife (LGK) Icon system to generate electron density information for the convolution algorithm in Leksell GammaPlan (LGP) Treatment Planning System (TPS). MA...

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Autores principales: Ramachandran, Prabhakar, Perrett, Ben, Dancewicz, Orrie, Seshadri, Venkatakrishnan, Jones, Catherine, Mehta, Akash, Foote, Matthew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society for Radiation Oncology 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8497862/
https://www.ncbi.nlm.nih.gov/pubmed/34619830
http://dx.doi.org/10.3857/roj.2020.00640
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author Ramachandran, Prabhakar
Perrett, Ben
Dancewicz, Orrie
Seshadri, Venkatakrishnan
Jones, Catherine
Mehta, Akash
Foote, Matthew
author_facet Ramachandran, Prabhakar
Perrett, Ben
Dancewicz, Orrie
Seshadri, Venkatakrishnan
Jones, Catherine
Mehta, Akash
Foote, Matthew
author_sort Ramachandran, Prabhakar
collection PubMed
description PURPOSE: The aim of this study was to assess the suitability of using cone-beam computed tomography images (CBCTs) produced in a Leksell Gamma Knife (LGK) Icon system to generate electron density information for the convolution algorithm in Leksell GammaPlan (LGP) Treatment Planning System (TPS). MATERIALS AND METHODS: A retrospective set of 30 LGK treatment plans generated for patients with multiple metastases was selected in this study. Both CBCTs and fan-beam CTs were used to provide electron density data for the convolution algorithm. Plan quality metrics such as coverage, selectivity, gradient index, and beam-on time were used to assess the changes introduced by convolution using CBCT (convCBCT) and planning CT (convCT) data compared to the homogeneous TMR10 algorithm. RESULTS: The mean beam-on time for TMR10 and convCBCT was found to be 18.9 ± 5.8 minutes and 21.7 ± 6.6 minutes, respectively. The absolute mean difference between TMR10 and convCBCT for coverage, selectivity, and gradient index were 0.001, 0.02, and 0.0002, respectively. The calculated beam-on times for convCBCT were higher than the time calculated for convCT treatment plans. This is attributed to the considerable variation in Hounsfield values (HU) dependent on the position within the field of view. CONCLUSION: The artifacts from the CBCT’s limited field-of-view and considerable HU variation need to be taken into account before considering the use of convolution algorithm for dose calculation on CBCT image datasets, and electron data derived from the onboard CBCT should be used with caution.
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spelling pubmed-84978622021-10-19 Use of GammaPlan convolution algorithm for dose calculation on CT and cone-beam CT images Ramachandran, Prabhakar Perrett, Ben Dancewicz, Orrie Seshadri, Venkatakrishnan Jones, Catherine Mehta, Akash Foote, Matthew Radiat Oncol J Original Article PURPOSE: The aim of this study was to assess the suitability of using cone-beam computed tomography images (CBCTs) produced in a Leksell Gamma Knife (LGK) Icon system to generate electron density information for the convolution algorithm in Leksell GammaPlan (LGP) Treatment Planning System (TPS). MATERIALS AND METHODS: A retrospective set of 30 LGK treatment plans generated for patients with multiple metastases was selected in this study. Both CBCTs and fan-beam CTs were used to provide electron density data for the convolution algorithm. Plan quality metrics such as coverage, selectivity, gradient index, and beam-on time were used to assess the changes introduced by convolution using CBCT (convCBCT) and planning CT (convCT) data compared to the homogeneous TMR10 algorithm. RESULTS: The mean beam-on time for TMR10 and convCBCT was found to be 18.9 ± 5.8 minutes and 21.7 ± 6.6 minutes, respectively. The absolute mean difference between TMR10 and convCBCT for coverage, selectivity, and gradient index were 0.001, 0.02, and 0.0002, respectively. The calculated beam-on times for convCBCT were higher than the time calculated for convCT treatment plans. This is attributed to the considerable variation in Hounsfield values (HU) dependent on the position within the field of view. CONCLUSION: The artifacts from the CBCT’s limited field-of-view and considerable HU variation need to be taken into account before considering the use of convolution algorithm for dose calculation on CBCT image datasets, and electron data derived from the onboard CBCT should be used with caution. The Korean Society for Radiation Oncology 2021-06 2021-06-16 /pmc/articles/PMC8497862/ /pubmed/34619830 http://dx.doi.org/10.3857/roj.2020.00640 Text en Copyright © 2021 The Korean Society for Radiation Oncology https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Ramachandran, Prabhakar
Perrett, Ben
Dancewicz, Orrie
Seshadri, Venkatakrishnan
Jones, Catherine
Mehta, Akash
Foote, Matthew
Use of GammaPlan convolution algorithm for dose calculation on CT and cone-beam CT images
title Use of GammaPlan convolution algorithm for dose calculation on CT and cone-beam CT images
title_full Use of GammaPlan convolution algorithm for dose calculation on CT and cone-beam CT images
title_fullStr Use of GammaPlan convolution algorithm for dose calculation on CT and cone-beam CT images
title_full_unstemmed Use of GammaPlan convolution algorithm for dose calculation on CT and cone-beam CT images
title_short Use of GammaPlan convolution algorithm for dose calculation on CT and cone-beam CT images
title_sort use of gammaplan convolution algorithm for dose calculation on ct and cone-beam ct images
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8497862/
https://www.ncbi.nlm.nih.gov/pubmed/34619830
http://dx.doi.org/10.3857/roj.2020.00640
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