Cargando…

Fast Monte Carlo simulation for total body irradiation using a [Formula: see text] teletherapy unit

Our institution delivers TBI using a modified Theratron 780 [Formula: see text] unit. Due to limitations of our treatment planning system in calculating dose for this treatment, we have developed a fast Monte Carlo code to calculate dose distributions within the patient. The algorithm is written in...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Xiaodong, Lack, Danielle, Rakowski, Joseph T., Knill, Cory, Snyder, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5714420/
https://www.ncbi.nlm.nih.gov/pubmed/23652253
http://dx.doi.org/10.1120/jacmp.v14i3.4214
_version_ 1783283584583335936
author Liu, Xiaodong
Lack, Danielle
Rakowski, Joseph T.
Knill, Cory
Snyder, Michael
author_facet Liu, Xiaodong
Lack, Danielle
Rakowski, Joseph T.
Knill, Cory
Snyder, Michael
author_sort Liu, Xiaodong
collection PubMed
description Our institution delivers TBI using a modified Theratron 780 [Formula: see text] unit. Due to limitations of our treatment planning system in calculating dose for this treatment, we have developed a fast Monte Carlo code to calculate dose distributions within the patient. The algorithm is written in C and uses voxel density information from CT images to calculate dose in heterogeneous media. To test the algorithm, film‐based dose measurements were made separately in a simple water phantom with a high‐density insert and a RANDO phantom and then compared to doses calculated by the Monte Carlo algorithm. In addition, a separate simulation in GEANT4 was run for the RANDO phantom and compared to both film and the in‐house simulation. All results were analyzed using RIT113 film analysis software. Simulations in the water phantom accurately predict the depth of maximum dose in the phantom at 0.5 cm. The measured PDD along the central axis of the beam closely matches the PDD generated from the Monte Carlo code, deviating on average by only 3% along the depth of the water phantom. Dose measured at planes inside the high‐density insert had a mean difference of 4.9% on cross‐profile measurement. In the RANDO phantom, gamma pass rates vary between 91% and 99% at 3 mm, 3%, and were [Formula: see text] at 5 mm, 5% for the four film planes measured. Profiles taken across the film and both simulations resulted in mean relative differences of [Formula: see text] for all profiles in each slice measured. The Monte Carlo algorithm presented here is potentially a viable method for calculating dose distributions delivered in TBI treatments at our center. While not yet refined enough to be the primary method of treatment planning, the algorithm at its current resolution determines the dose distribution for one patient within a few hours, and provides clinically useful information in planning TBI. With appropriate optimization, the Monte Carlo method presented here could potentially be implemented as a first‐line treatment planning option for [Formula: see text] TBI. PACS number: 87.10.Rt
format Online
Article
Text
id pubmed-5714420
institution National Center for Biotechnology Information
language English
publishDate 2013
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-57144202018-04-02 Fast Monte Carlo simulation for total body irradiation using a [Formula: see text] teletherapy unit Liu, Xiaodong Lack, Danielle Rakowski, Joseph T. Knill, Cory Snyder, Michael J Appl Clin Med Phys Radiation Oncology Physics Our institution delivers TBI using a modified Theratron 780 [Formula: see text] unit. Due to limitations of our treatment planning system in calculating dose for this treatment, we have developed a fast Monte Carlo code to calculate dose distributions within the patient. The algorithm is written in C and uses voxel density information from CT images to calculate dose in heterogeneous media. To test the algorithm, film‐based dose measurements were made separately in a simple water phantom with a high‐density insert and a RANDO phantom and then compared to doses calculated by the Monte Carlo algorithm. In addition, a separate simulation in GEANT4 was run for the RANDO phantom and compared to both film and the in‐house simulation. All results were analyzed using RIT113 film analysis software. Simulations in the water phantom accurately predict the depth of maximum dose in the phantom at 0.5 cm. The measured PDD along the central axis of the beam closely matches the PDD generated from the Monte Carlo code, deviating on average by only 3% along the depth of the water phantom. Dose measured at planes inside the high‐density insert had a mean difference of 4.9% on cross‐profile measurement. In the RANDO phantom, gamma pass rates vary between 91% and 99% at 3 mm, 3%, and were [Formula: see text] at 5 mm, 5% for the four film planes measured. Profiles taken across the film and both simulations resulted in mean relative differences of [Formula: see text] for all profiles in each slice measured. The Monte Carlo algorithm presented here is potentially a viable method for calculating dose distributions delivered in TBI treatments at our center. While not yet refined enough to be the primary method of treatment planning, the algorithm at its current resolution determines the dose distribution for one patient within a few hours, and provides clinically useful information in planning TBI. With appropriate optimization, the Monte Carlo method presented here could potentially be implemented as a first‐line treatment planning option for [Formula: see text] TBI. PACS number: 87.10.Rt John Wiley and Sons Inc. 2013-05-06 /pmc/articles/PMC5714420/ /pubmed/23652253 http://dx.doi.org/10.1120/jacmp.v14i3.4214 Text en © 2013 The Authors. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/3.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Radiation Oncology Physics
Liu, Xiaodong
Lack, Danielle
Rakowski, Joseph T.
Knill, Cory
Snyder, Michael
Fast Monte Carlo simulation for total body irradiation using a [Formula: see text] teletherapy unit
title Fast Monte Carlo simulation for total body irradiation using a [Formula: see text] teletherapy unit
title_full Fast Monte Carlo simulation for total body irradiation using a [Formula: see text] teletherapy unit
title_fullStr Fast Monte Carlo simulation for total body irradiation using a [Formula: see text] teletherapy unit
title_full_unstemmed Fast Monte Carlo simulation for total body irradiation using a [Formula: see text] teletherapy unit
title_short Fast Monte Carlo simulation for total body irradiation using a [Formula: see text] teletherapy unit
title_sort fast monte carlo simulation for total body irradiation using a [formula: see text] teletherapy unit
topic Radiation Oncology Physics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5714420/
https://www.ncbi.nlm.nih.gov/pubmed/23652253
http://dx.doi.org/10.1120/jacmp.v14i3.4214
work_keys_str_mv AT liuxiaodong fastmontecarlosimulationfortotalbodyirradiationusingaformulaseetextteletherapyunit
AT lackdanielle fastmontecarlosimulationfortotalbodyirradiationusingaformulaseetextteletherapyunit
AT rakowskijosepht fastmontecarlosimulationfortotalbodyirradiationusingaformulaseetextteletherapyunit
AT knillcory fastmontecarlosimulationfortotalbodyirradiationusingaformulaseetextteletherapyunit
AT snydermichael fastmontecarlosimulationfortotalbodyirradiationusingaformulaseetextteletherapyunit