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 thoracic or abdominal cavity. Currently, motion characterization for respiration tracking mainly focuses on markers which are placed on surface of human chest. Nevertheless, limited markers are not ca...

Full description

Saved in:
Bibliographic Details
Published in:Frontiers in neurorobotics 2020-11, Vol.14, p.582385-582385
Main Authors: Yu, Shumei, Hou, Pengcheng, Sun, Rongchuan, Kuang, Shaolong, Zhang, Fengfeng, Zhou, Mingchuan, Guo, Jing, Sun, Lining
Format: Article
Language:English
Subjects:
Abdomen
Cameras
Chest
correlation model
Experiments
Fuzzy logic
Methods
Neural networks
Neuroscience
Patients
Principal components analysis
Radiosurgery
Respiration
respiratory motion characterization
Skin
surface modeling
Thorax
tumor tracking
Tumors
voxel model
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c470t-59bc6f37b292cd29ae4374f8ea4fb42b1746095cee90fc76370e789fcbe72b0f3
cites cdi_FETCH-LOGICAL-c470t-59bc6f37b292cd29ae4374f8ea4fb42b1746095cee90fc76370e789fcbe72b0f3
container_end_page 582385
container_issue
container_start_page 582385
container_title Frontiers in neurorobotics
container_volume 14
creator Yu, Shumei
Hou, Pengcheng
Sun, Rongchuan
Kuang, Shaolong
Zhang, Fengfeng
Zhou, Mingchuan
Guo, Jing
Sun, Lining
description In robotic radiosurgery, motion tracking is crucial for accurate treatment planning of tumor inside thoracic or abdominal cavity. Currently, motion characterization for respiration tracking mainly focuses on markers which are placed on surface of human chest. Nevertheless, limited markers are not capable of expressing the comprehensive motion feature of human chest and abdomen. In this paper, we proposed a method of respiratory motion characterization based on the voxel modeling of 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.
doi_str_mv 10.3389/fnbot.2020.582385
format article
fullrecord <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_4e89df4001da4ada85f16a57624c3055</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_4e89df4001da4ada85f16a57624c3055</doaj_id><sourcerecordid>2460072161</sourcerecordid><originalsourceid>FETCH-LOGICAL-c470t-59bc6f37b292cd29ae4374f8ea4fb42b1746095cee90fc76370e789fcbe72b0f3</originalsourceid><addsrcrecordid>eNpdkktvEzEUhUcIREvhB7AbiQ2bBL8fGyQU8ahUBGrD2njs6-AwYxd7Bqn_HiepEGV1r66PPx1fn657idGaUqXfhDTkeU0QQWuuCFX8UXeOhSArTrB6_E9_1j2rdY-QIIKrp90ZpaS1Wp133ze5FBjtDL6_-RlTf7OUYB30Nvl-u0y59J_zHHNqxcMY064PbbYtYOcJ0tx_HW1Kh3G7e52bn-j6a-tjrkvZQbl73j0Jdqzw4r5edN8-vN9uPq2uvny83Ly7Wjkm0bzienAiUDkQTZwn2gKjkgUFloWBkQFLJpDmDkCj4KSgEoFUOrgBJBlQoBfd5Ynrs92b2xInW-5MttEcB7nsjC3N3AiGgdI-MISwt8x6q3jAwnIpCHMUcd5Yb0-s22WYwLv2zmLHB9CHJyn-MLv820ihFDsCXt8DSv61QJ3NFKuDse0K8lINYe1ntFCCNumr_6T7vJTUVnVQISQJFrip8EnlSq61QPhrBiNzyII5ZsEcsmBOWaB_ANfqp-U</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2460072161</pqid></control><display><type>article</type><title>Correlated Skin Surface and Tumor Motion Modeling for Treatment Planning in Robotic Radiosurgery</title><source>Publicly Available Content (ProQuest)</source><source>PubMed Central</source><creator>Yu, Shumei ; Hou, Pengcheng ; Sun, Rongchuan ; Kuang, Shaolong ; Zhang, Fengfeng ; Zhou, Mingchuan ; Guo, Jing ; Sun, Lining</creator><creatorcontrib>Yu, Shumei ; Hou, Pengcheng ; Sun, Rongchuan ; Kuang, Shaolong ; Zhang, Fengfeng ; Zhou, Mingchuan ; Guo, Jing ; Sun, Lining</creatorcontrib><description>In robotic radiosurgery, motion tracking is crucial for accurate treatment planning of tumor inside thoracic or abdominal cavity. Currently, motion characterization for respiration tracking mainly focuses on markers which are placed on surface of human chest. Nevertheless, limited markers are not capable of expressing the comprehensive motion feature of human chest and abdomen. In this paper, we proposed a method of respiratory motion characterization based on the voxel modeling of 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.</description><identifier>ISSN: 1662-5218</identifier><identifier>EISSN: 1662-5218</identifier><identifier>DOI: 10.3389/fnbot.2020.582385</identifier><identifier>PMID: 33262698</identifier><language>eng</language><publisher>Lausanne: Frontiers Research Foundation</publisher><subject>Abdomen ; Cameras ; Chest ; correlation model ; Experiments ; Fuzzy logic ; Methods ; Neural networks ; Neuroscience ; Patients ; Principal components analysis ; Radiosurgery ; Respiration ; respiratory motion characterization ; Skin ; surface modeling ; Thorax ; tumor tracking ; Tumors ; voxel model</subject><ispartof>Frontiers in neurorobotics, 2020-11, Vol.14, p.582385-582385</ispartof><rights>2020. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Copyright © 2020 Yu, Hou, Sun, Kuang, Zhang, Zhou, Guo and Sun. 2020 Yu, Hou, Sun, Kuang, Zhang, Zhou, Guo and Sun</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c470t-59bc6f37b292cd29ae4374f8ea4fb42b1746095cee90fc76370e789fcbe72b0f3</citedby><cites>FETCH-LOGICAL-c470t-59bc6f37b292cd29ae4374f8ea4fb42b1746095cee90fc76370e789fcbe72b0f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2460072161/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2460072161?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids></links><search><creatorcontrib>Yu, Shumei</creatorcontrib><creatorcontrib>Hou, Pengcheng</creatorcontrib><creatorcontrib>Sun, Rongchuan</creatorcontrib><creatorcontrib>Kuang, Shaolong</creatorcontrib><creatorcontrib>Zhang, Fengfeng</creatorcontrib><creatorcontrib>Zhou, Mingchuan</creatorcontrib><creatorcontrib>Guo, Jing</creatorcontrib><creatorcontrib>Sun, Lining</creatorcontrib><title>Correlated Skin Surface and Tumor Motion Modeling for Treatment Planning in Robotic Radiosurgery</title><title>Frontiers in neurorobotics</title><description>In robotic radiosurgery, motion tracking is crucial for accurate treatment planning of tumor inside thoracic or abdominal cavity. Currently, motion characterization for respiration tracking mainly focuses on markers which are placed on surface of human chest. Nevertheless, limited markers are not capable of expressing the comprehensive motion feature of human chest and abdomen. In this paper, we proposed a method of respiratory motion characterization based on the voxel modeling of 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.</description><subject>Abdomen</subject><subject>Cameras</subject><subject>Chest</subject><subject>correlation model</subject><subject>Experiments</subject><subject>Fuzzy logic</subject><subject>Methods</subject><subject>Neural networks</subject><subject>Neuroscience</subject><subject>Patients</subject><subject>Principal components analysis</subject><subject>Radiosurgery</subject><subject>Respiration</subject><subject>respiratory motion characterization</subject><subject>Skin</subject><subject>surface modeling</subject><subject>Thorax</subject><subject>tumor tracking</subject><subject>Tumors</subject><subject>voxel model</subject><issn>1662-5218</issn><issn>1662-5218</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpdkktvEzEUhUcIREvhB7AbiQ2bBL8fGyQU8ahUBGrD2njs6-AwYxd7Bqn_HiepEGV1r66PPx1fn657idGaUqXfhDTkeU0QQWuuCFX8UXeOhSArTrB6_E9_1j2rdY-QIIKrp90ZpaS1Wp133ze5FBjtDL6_-RlTf7OUYB30Nvl-u0y59J_zHHNqxcMY064PbbYtYOcJ0tx_HW1Kh3G7e52bn-j6a-tjrkvZQbl73j0Jdqzw4r5edN8-vN9uPq2uvny83Ly7Wjkm0bzienAiUDkQTZwn2gKjkgUFloWBkQFLJpDmDkCj4KSgEoFUOrgBJBlQoBfd5Ynrs92b2xInW-5MttEcB7nsjC3N3AiGgdI-MISwt8x6q3jAwnIpCHMUcd5Yb0-s22WYwLv2zmLHB9CHJyn-MLv820ihFDsCXt8DSv61QJ3NFKuDse0K8lINYe1ntFCCNumr_6T7vJTUVnVQISQJFrip8EnlSq61QPhrBiNzyII5ZsEcsmBOWaB_ANfqp-U</recordid><startdate>20201112</startdate><enddate>20201112</enddate><creator>Yu, Shumei</creator><creator>Hou, Pengcheng</creator><creator>Sun, Rongchuan</creator><creator>Kuang, Shaolong</creator><creator>Zhang, Fengfeng</creator><creator>Zhou, Mingchuan</creator><creator>Guo, Jing</creator><creator>Sun, Lining</creator><general>Frontiers Research Foundation</general><general>Frontiers Media S.A</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7XB</scope><scope>88I</scope><scope>8FE</scope><scope>8FH</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>LK8</scope><scope>M2P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20201112</creationdate><title>Correlated Skin Surface and Tumor Motion Modeling for Treatment Planning in Robotic Radiosurgery</title><author>Yu, Shumei ; Hou, Pengcheng ; Sun, Rongchuan ; Kuang, Shaolong ; Zhang, Fengfeng ; Zhou, Mingchuan ; Guo, Jing ; Sun, Lining</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c470t-59bc6f37b292cd29ae4374f8ea4fb42b1746095cee90fc76370e789fcbe72b0f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Abdomen</topic><topic>Cameras</topic><topic>Chest</topic><topic>correlation model</topic><topic>Experiments</topic><topic>Fuzzy logic</topic><topic>Methods</topic><topic>Neural networks</topic><topic>Neuroscience</topic><topic>Patients</topic><topic>Principal components analysis</topic><topic>Radiosurgery</topic><topic>Respiration</topic><topic>respiratory motion characterization</topic><topic>Skin</topic><topic>surface modeling</topic><topic>Thorax</topic><topic>tumor tracking</topic><topic>Tumors</topic><topic>voxel model</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yu, Shumei</creatorcontrib><creatorcontrib>Hou, Pengcheng</creatorcontrib><creatorcontrib>Sun, Rongchuan</creatorcontrib><creatorcontrib>Kuang, Shaolong</creatorcontrib><creatorcontrib>Zhang, Fengfeng</creatorcontrib><creatorcontrib>Zhou, Mingchuan</creatorcontrib><creatorcontrib>Guo, Jing</creatorcontrib><creatorcontrib>Sun, Lining</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>ProQuest Science Journals</collection><collection>Biological Science Database</collection><collection>Publicly Available Content (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Frontiers in neurorobotics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yu, Shumei</au><au>Hou, Pengcheng</au><au>Sun, Rongchuan</au><au>Kuang, Shaolong</au><au>Zhang, Fengfeng</au><au>Zhou, Mingchuan</au><au>Guo, Jing</au><au>Sun, Lining</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Correlated Skin Surface and Tumor Motion Modeling for Treatment Planning in Robotic Radiosurgery</atitle><jtitle>Frontiers in neurorobotics</jtitle><date>2020-11-12</date><risdate>2020</risdate><volume>14</volume><spage>582385</spage><epage>582385</epage><pages>582385-582385</pages><issn>1662-5218</issn><eissn>1662-5218</eissn><abstract>In robotic radiosurgery, motion tracking is crucial for accurate treatment planning of tumor inside thoracic or abdominal cavity. Currently, motion characterization for respiration tracking mainly focuses on markers which are placed on surface of human chest. Nevertheless, limited markers are not capable of expressing the comprehensive motion feature of human chest and abdomen. In this paper, we proposed a method of respiratory motion characterization based on the voxel modeling of 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.</abstract><cop>Lausanne</cop><pub>Frontiers Research Foundation</pub><pmid>33262698</pmid><doi>10.3389/fnbot.2020.582385</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1662-5218
ispartof Frontiers in neurorobotics, 2020-11, Vol.14, p.582385-582385
issn 1662-5218
1662-5218
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_4e89df4001da4ada85f16a57624c3055
source Publicly Available Content (ProQuest); PubMed Central
subjects Abdomen
Cameras
Chest
correlation model
Experiments
Fuzzy logic
Methods
Neural networks
Neuroscience
Patients
Principal components analysis
Radiosurgery
Respiration
respiratory motion characterization
Skin
surface modeling
Thorax
tumor tracking
Tumors
voxel model
title Correlated Skin Surface and Tumor Motion Modeling for Treatment Planning in Robotic Radiosurgery
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T02%3A05%3A25IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Correlated%20Skin%20Surface%20and%20Tumor%20Motion%20Modeling%20for%20Treatment%20Planning%20in%20Robotic%20Radiosurgery&rft.jtitle=Frontiers%20in%20neurorobotics&rft.au=Yu,%20Shumei&rft.date=2020-11-12&rft.volume=14&rft.spage=582385&rft.epage=582385&rft.pages=582385-582385&rft.issn=1662-5218&rft.eissn=1662-5218&rft_id=info:doi/10.3389/fnbot.2020.582385&rft_dat=%3Cproquest_doaj_%3E2460072161%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c470t-59bc6f37b292cd29ae4374f8ea4fb42b1746095cee90fc76370e789fcbe72b0f3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2460072161&rft_id=info:pmid/33262698&rfr_iscdi=true