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Correlated Skin Surface and Tumor Motion Modeling for Treatment Planning in Robotic Radiosurgery
In robotic radiosurgery, motion tracking is crucial for accurate treatment planning of tumor inside the thoracic or abdominal cavity. Currently, motion characterization for respiration tracking mainly focuses on markers that are placed on the surface of human chest. Nevertheless, limited markers are...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7688455/ https://www.ncbi.nlm.nih.gov/pubmed/33262698 http://dx.doi.org/10.3389/fnbot.2020.582385 |
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author | Yu, Shumei Hou, Pengcheng Sun, Rongchuan Kuang, Shaolong Zhang, Fengfeng Zhou, Mingchuan Guo, Jing Sun, Lining |
author_facet | Yu, Shumei Hou, Pengcheng Sun, Rongchuan Kuang, Shaolong Zhang, Fengfeng Zhou, Mingchuan Guo, Jing Sun, Lining |
author_sort | Yu, Shumei |
collection | PubMed |
description | In robotic radiosurgery, motion tracking is crucial for accurate treatment planning of tumor inside the thoracic or abdominal cavity. Currently, motion characterization for respiration tracking mainly focuses on markers that are placed on the surface of human chest. Nevertheless, limited markers are not capable of expressing the comprehensive motion feature of the human chest and abdomen. In this paper, we proposed a method of respiratory motion characterization based on the voxel modeling of the thoracoabdominal torso. Point cloud data from depth cameras were used to achieve three-dimensional modeling of the chest and abdomen surface during respiration, and a dimensionality reduction algorithm was proposed to extract respiratory features from the established voxel model. Finally, experimental results including the accuracy of voxel model and correlation coefficient were compared to validate the feasibility of the proposed method, which provides enhanced accuracy of target motion correlation than traditional methods that utilized external markers. |
format | Online Article Text |
id | pubmed-7688455 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-76884552020-11-30 Correlated Skin Surface and Tumor Motion Modeling for Treatment Planning in Robotic Radiosurgery Yu, Shumei Hou, Pengcheng Sun, Rongchuan Kuang, Shaolong Zhang, Fengfeng Zhou, Mingchuan Guo, Jing Sun, Lining Front Neurorobot Neuroscience In robotic radiosurgery, motion tracking is crucial for accurate treatment planning of tumor inside the thoracic or abdominal cavity. Currently, motion characterization for respiration tracking mainly focuses on markers that are placed on the surface of human chest. Nevertheless, limited markers are not capable of expressing the comprehensive motion feature of the human chest and abdomen. In this paper, we proposed a method of respiratory motion characterization based on the voxel modeling of the thoracoabdominal torso. Point cloud data from depth cameras were used to achieve three-dimensional modeling of the chest and abdomen surface during respiration, and a dimensionality reduction algorithm was proposed to extract respiratory features from the established voxel model. Finally, experimental results including the accuracy of voxel model and correlation coefficient were compared to validate the feasibility of the proposed method, which provides enhanced accuracy of target motion correlation than traditional methods that utilized external markers. Frontiers Media S.A. 2020-11-12 /pmc/articles/PMC7688455/ /pubmed/33262698 http://dx.doi.org/10.3389/fnbot.2020.582385 Text en Copyright © 2020 Yu, Hou, Sun, Kuang, Zhang, Zhou, Guo and Sun. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Yu, Shumei Hou, Pengcheng Sun, Rongchuan Kuang, Shaolong Zhang, Fengfeng Zhou, Mingchuan Guo, Jing Sun, Lining Correlated Skin Surface and Tumor Motion Modeling for Treatment Planning in Robotic Radiosurgery |
title | Correlated Skin Surface and Tumor Motion Modeling for Treatment Planning in Robotic Radiosurgery |
title_full | Correlated Skin Surface and Tumor Motion Modeling for Treatment Planning in Robotic Radiosurgery |
title_fullStr | Correlated Skin Surface and Tumor Motion Modeling for Treatment Planning in Robotic Radiosurgery |
title_full_unstemmed | Correlated Skin Surface and Tumor Motion Modeling for Treatment Planning in Robotic Radiosurgery |
title_short | Correlated Skin Surface and Tumor Motion Modeling for Treatment Planning in Robotic Radiosurgery |
title_sort | correlated skin surface and tumor motion modeling for treatment planning in robotic radiosurgery |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7688455/ https://www.ncbi.nlm.nih.gov/pubmed/33262698 http://dx.doi.org/10.3389/fnbot.2020.582385 |
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