Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Özbek, Yusuf, Bárdosi, Zoltán, Freysinger, Wolfgang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2020
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
_version_ 1783547971924656128
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
work_keys_str_mv AT ozbekyusuf respitrackpatientspecificrealtimerespiratorytumormotionpredictionusingmagnetictracking
AT bardosizoltan respitrackpatientspecificrealtimerespiratorytumormotionpredictionusingmagnetictracking
AT freysingerwolfgang respitrackpatientspecificrealtimerespiratorytumormotionpredictionusingmagnetictracking