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respiTrack: Patient-specific real-time respiratory tumor motion prediction using magnetic tracking
PURPOSE: An intraoperative real-time respiratory tumor motion prediction system with magnetic tracking technology is presented. Based on respiratory movements in different body regions, it provides patient and single/multiple tumor-specific prediction that facilitates the guiding of treatments. METH...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7303076/ https://www.ncbi.nlm.nih.gov/pubmed/32347464 http://dx.doi.org/10.1007/s11548-020-02174-3 |
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author | Özbek, Yusuf Bárdosi, Zoltán Freysinger, Wolfgang |
author_facet | Özbek, Yusuf Bárdosi, Zoltán Freysinger, Wolfgang |
author_sort | Özbek, Yusuf |
collection | PubMed |
description | PURPOSE: An intraoperative real-time respiratory tumor motion prediction system with magnetic tracking technology is presented. Based on respiratory movements in different body regions, it provides patient and single/multiple tumor-specific prediction that facilitates the guiding of treatments. METHODS: A custom-built phantom patient model replicates the respiratory cycles similar to a human body, while the custom-built sensor holder concept is applied on the patient’s surface to find optimum sensor number and their best possible placement locations to use in real-time surgical navigation and motion prediction of internal tumors. Automatic marker localization applied to patient’s 4D-CT data, feature selection and Gaussian process regression algorithms enable off-line prediction in the preoperative phase to increase the accuracy of real-time prediction. RESULTS: Two evaluation methods with three different registration patterns (at fully/half inhaled and fully exhaled positions) were used quantitatively at all internal target positions in phantom: The statical method evaluates the accuracy by stopping simulated breathing and dynamic with continued breathing patterns. The overall root mean square error (RMS) for both methods was between [Formula: see text] and [Formula: see text] . The overall registration RMS error was [Formula: see text] . The best prediction errors were observed by registrations at half inhaled positions with minimum [Formula: see text] , maximum [Formula: see text] . The resulting accuracy satisfies most radiotherapy treatments or surgeries, e.g., for lung, liver, prostate and spine. CONCLUSION: The built system is proposed to predict respiratory motions of internal structures in the body while the patient is breathing freely during treatment. The custom-built sensor holders are compatible with magnetic tracking. Our presented approach reduces known technological and human limitations of commonly used methods for physicians and patients. |
format | Online Article Text |
id | pubmed-7303076 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-73030762020-06-22 respiTrack: Patient-specific real-time respiratory tumor motion prediction using magnetic tracking Özbek, Yusuf Bárdosi, Zoltán Freysinger, Wolfgang Int J Comput Assist Radiol Surg Original Article PURPOSE: An intraoperative real-time respiratory tumor motion prediction system with magnetic tracking technology is presented. Based on respiratory movements in different body regions, it provides patient and single/multiple tumor-specific prediction that facilitates the guiding of treatments. METHODS: A custom-built phantom patient model replicates the respiratory cycles similar to a human body, while the custom-built sensor holder concept is applied on the patient’s surface to find optimum sensor number and their best possible placement locations to use in real-time surgical navigation and motion prediction of internal tumors. Automatic marker localization applied to patient’s 4D-CT data, feature selection and Gaussian process regression algorithms enable off-line prediction in the preoperative phase to increase the accuracy of real-time prediction. RESULTS: Two evaluation methods with three different registration patterns (at fully/half inhaled and fully exhaled positions) were used quantitatively at all internal target positions in phantom: The statical method evaluates the accuracy by stopping simulated breathing and dynamic with continued breathing patterns. The overall root mean square error (RMS) for both methods was between [Formula: see text] and [Formula: see text] . The overall registration RMS error was [Formula: see text] . The best prediction errors were observed by registrations at half inhaled positions with minimum [Formula: see text] , maximum [Formula: see text] . The resulting accuracy satisfies most radiotherapy treatments or surgeries, e.g., for lung, liver, prostate and spine. CONCLUSION: The built system is proposed to predict respiratory motions of internal structures in the body while the patient is breathing freely during treatment. The custom-built sensor holders are compatible with magnetic tracking. Our presented approach reduces known technological and human limitations of commonly used methods for physicians and patients. Springer International Publishing 2020-04-28 2020 /pmc/articles/PMC7303076/ /pubmed/32347464 http://dx.doi.org/10.1007/s11548-020-02174-3 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Original Article Özbek, Yusuf Bárdosi, Zoltán Freysinger, Wolfgang respiTrack: Patient-specific real-time respiratory tumor motion prediction using magnetic tracking |
title | respiTrack: Patient-specific real-time respiratory tumor motion prediction using magnetic tracking |
title_full | respiTrack: Patient-specific real-time respiratory tumor motion prediction using magnetic tracking |
title_fullStr | respiTrack: Patient-specific real-time respiratory tumor motion prediction using magnetic tracking |
title_full_unstemmed | respiTrack: Patient-specific real-time respiratory tumor motion prediction using magnetic tracking |
title_short | respiTrack: Patient-specific real-time respiratory tumor motion prediction using magnetic tracking |
title_sort | respitrack: patient-specific real-time respiratory tumor motion prediction using magnetic tracking |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7303076/ https://www.ncbi.nlm.nih.gov/pubmed/32347464 http://dx.doi.org/10.1007/s11548-020-02174-3 |
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