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Dual‐energy CT‐based stopping power prediction for dental materials in particle therapy

Radiotherapy with protons or light ions can offer accurate and precise treatment delivery. Accurate knowledge of the stopping power ratio (SPR) distribution of the tissues in the patient is crucial for improving dose prediction in patients during planning. However, materials of uncertain stoichiomet...

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Autores principales: Longarino, Friderike K., Herpel, Christopher, Tessonnier, Thomas, Mein, Stewart, Ackermann, Benjamin, Debus, Jürgen, Schwindling, Franz Sebastian, Stiller, Wolfram, Mairani, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10402687/
https://www.ncbi.nlm.nih.gov/pubmed/37032540
http://dx.doi.org/10.1002/acm2.13977
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author Longarino, Friderike K.
Herpel, Christopher
Tessonnier, Thomas
Mein, Stewart
Ackermann, Benjamin
Debus, Jürgen
Schwindling, Franz Sebastian
Stiller, Wolfram
Mairani, Andrea
author_facet Longarino, Friderike K.
Herpel, Christopher
Tessonnier, Thomas
Mein, Stewart
Ackermann, Benjamin
Debus, Jürgen
Schwindling, Franz Sebastian
Stiller, Wolfram
Mairani, Andrea
author_sort Longarino, Friderike K.
collection PubMed
description Radiotherapy with protons or light ions can offer accurate and precise treatment delivery. Accurate knowledge of the stopping power ratio (SPR) distribution of the tissues in the patient is crucial for improving dose prediction in patients during planning. However, materials of uncertain stoichiometric composition such as dental implant and restoration materials can substantially impair particle therapy treatment planning due to related SPR prediction uncertainties. This study investigated the impact of using dual‐energy computed tomography (DECT) imaging for characterizing and compensating for commonly used dental implant and restoration materials during particle therapy treatment planning. Radiological material parameters of ten common dental materials were determined using two different DECT techniques: sequential acquisition CT (SACT) and dual‐layer spectral CT (DLCT). DECT‐based direct SPR predictions of dental materials via spectral image data were compared to conventional single‐energy CT (SECT)‐based SPR predictions obtained via indirect CT‐number‐to‐SPR conversion. DECT techniques were found overall to reduce uncertainty in SPR predictions in dental implant and restoration materials compared to SECT, although DECT methods showed limitations for materials containing elements of a high atomic number. To assess the influence on treatment planning, an anthropomorphic head phantom with a removable tooth containing lithium disilicate as a dental material was used. The results indicated that both DECT techniques predicted similar ranges for beams unobstructed by dental material in the head phantom. When ion beams passed through the lithium disilicate restoration, DLCT‐based SPR predictions using a projection‐based method showed better agreement with measured reference SPR values (range deviation: 0.2 mm) compared to SECT‐based predictions. DECT‐based SPR prediction may improve the management of certain non‐tissue dental implant and restoration materials and subsequently increase dose prediction accuracy.
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spelling pubmed-104026872023-08-05 Dual‐energy CT‐based stopping power prediction for dental materials in particle therapy Longarino, Friderike K. Herpel, Christopher Tessonnier, Thomas Mein, Stewart Ackermann, Benjamin Debus, Jürgen Schwindling, Franz Sebastian Stiller, Wolfram Mairani, Andrea J Appl Clin Med Phys Radiation Oncology Physics Radiotherapy with protons or light ions can offer accurate and precise treatment delivery. Accurate knowledge of the stopping power ratio (SPR) distribution of the tissues in the patient is crucial for improving dose prediction in patients during planning. However, materials of uncertain stoichiometric composition such as dental implant and restoration materials can substantially impair particle therapy treatment planning due to related SPR prediction uncertainties. This study investigated the impact of using dual‐energy computed tomography (DECT) imaging for characterizing and compensating for commonly used dental implant and restoration materials during particle therapy treatment planning. Radiological material parameters of ten common dental materials were determined using two different DECT techniques: sequential acquisition CT (SACT) and dual‐layer spectral CT (DLCT). DECT‐based direct SPR predictions of dental materials via spectral image data were compared to conventional single‐energy CT (SECT)‐based SPR predictions obtained via indirect CT‐number‐to‐SPR conversion. DECT techniques were found overall to reduce uncertainty in SPR predictions in dental implant and restoration materials compared to SECT, although DECT methods showed limitations for materials containing elements of a high atomic number. To assess the influence on treatment planning, an anthropomorphic head phantom with a removable tooth containing lithium disilicate as a dental material was used. The results indicated that both DECT techniques predicted similar ranges for beams unobstructed by dental material in the head phantom. When ion beams passed through the lithium disilicate restoration, DLCT‐based SPR predictions using a projection‐based method showed better agreement with measured reference SPR values (range deviation: 0.2 mm) compared to SECT‐based predictions. DECT‐based SPR prediction may improve the management of certain non‐tissue dental implant and restoration materials and subsequently increase dose prediction accuracy. John Wiley and Sons Inc. 2023-04-09 /pmc/articles/PMC10402687/ /pubmed/37032540 http://dx.doi.org/10.1002/acm2.13977 Text en © 2023 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Radiation Oncology Physics
Longarino, Friderike K.
Herpel, Christopher
Tessonnier, Thomas
Mein, Stewart
Ackermann, Benjamin
Debus, Jürgen
Schwindling, Franz Sebastian
Stiller, Wolfram
Mairani, Andrea
Dual‐energy CT‐based stopping power prediction for dental materials in particle therapy
title Dual‐energy CT‐based stopping power prediction for dental materials in particle therapy
title_full Dual‐energy CT‐based stopping power prediction for dental materials in particle therapy
title_fullStr Dual‐energy CT‐based stopping power prediction for dental materials in particle therapy
title_full_unstemmed Dual‐energy CT‐based stopping power prediction for dental materials in particle therapy
title_short Dual‐energy CT‐based stopping power prediction for dental materials in particle therapy
title_sort dual‐energy ct‐based stopping power prediction for dental materials in particle therapy
topic Radiation Oncology Physics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10402687/
https://www.ncbi.nlm.nih.gov/pubmed/37032540
http://dx.doi.org/10.1002/acm2.13977
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