SU-C-BRA-07: Variability of Patient-Specific Motion Models Derived Using Different Deformable Image Registration Algorithms for Lung Cancer Stereotactic Body Radiotherapy (SBRT) Patients
Purpose: To study the variability of patient-specific motion models derived from 4-dimensional CT (4DCT) images using different deformable image registration (DIR) algorithms for lung cancer stereotactic body radiotherapy (SBRT) patients. Methods: Motion models are derived by 1) applying DIR between...
Gespeichert in:
Veröffentlicht in: | Medical physics (Lancaster) 2016-06, Vol.43 (6), p.3322-3322 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 3322 |
---|---|
container_issue | 6 |
container_start_page | 3322 |
container_title | Medical physics (Lancaster) |
container_volume | 43 |
creator | Dhou, S Ionascu, D Lewis, J Williams, C |
description | Purpose:
To study the variability of patient-specific motion models derived from 4-dimensional CT (4DCT) images using different deformable image registration (DIR) algorithms for lung cancer stereotactic body radiotherapy (SBRT) patients.
Methods:
Motion models are derived by 1) applying DIR between each 4DCT image and a reference image, resulting in a set of displacement vector fields (DVFs), and 2) performing principal component analysis (PCA) on the DVFs, resulting in a motion model (a set of eigenvectors capturing the variations in the DVFs). Three DIR algorithms were used: 1) Demons, 2) Horn-Schunck, and 3) iterative optical flow. The motion models derived were compared using patient 4DCT scans.
Results:
Motion models were derived and the variations were evaluated according to three criteria: 1) the average root mean square (RMS) difference which measures the absolute difference between the components of the eigenvectors, 2) the dot product between the eigenvectors which measures the angular difference between the eigenvectors in space, and 3) the Euclidean Model Norm (EMN), which is calculated by summing the dot products of an eigenvector with the first three eigenvectors from the reference motion model in quadrature. EMN measures how well an eigenvector can be reconstructed using another motion model derived using a different DIR algorithm. Results showed that comparing to a reference motion model (derived using the Demons algorithm), the eigenvectors of the motion model derived using the iterative optical flow algorithm has smaller RMS, larger dot product, and larger EMN values than those of the motion model derived using Horn-Schunck algorithm.
Conclusion:
The study showed that motion models vary depending on which DIR algorithms were used to derive them. The choice of a DIR algorithm may affect the accuracy of the resulting model, and it is important to assess the suitability of the algorithm chosen for a particular application.
This project was supported, in part, through a Master Research Agreement with Varian Medical Systems, Inc, Palo Alto, CA. |
doi_str_mv | 10.1118/1.4955568 |
format | Article |
fullrecord | <record><control><sourceid>wiley_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1118_1_4955568</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>MP5568</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1358-8ed04864cd80c3405c1abee61e6f65336b43e32b89a54f2814aeb8bdc485814a3</originalsourceid><addsrcrecordid>eNp9kc9uEzEQxi0EEqFw4A0scaFILv679XJLUmgrpaJKGq4rr3ecGG3WkW1A-2o8HU4Tju3p04x-832jGYTeM3rBGNOf2YWslVKVfoEmXF4KIjmtX6IJpbUkXFL1Gr1J6SeltBKKTtDf1ZrMyWw5JfTyC_5hojet730ecXD43mQPQyarPVjvvMV3IfswFOmgT_gKov8NHV4nP2zwlXcOYsFL34W4M20P-HZnNoCXsPEpR_M4PO03Ifq83SVcMLz4VWbnZrAQ8SoXg5CNzSVrFroRL03nQ95CNPsRf1zNlg_n_7dKb9ErZ_oE7056htbfvj7Mb8ji-_XtfLoglgmliYaOSl1J22lqRbmAZaYFqBhUrlJCVK0UIHira6Ok45pJA61uOyu1OhTiDH04-oaUfZOsz2C3NgwD2NxwXnEpeF2o8yNlY0gpgmv20e9MHBtGm8NrGtacXlNYcmT_-B7Gp8Hm7v7Efzryh_DHKz5j_g8teJyr</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>SU-C-BRA-07: Variability of Patient-Specific Motion Models Derived Using Different Deformable Image Registration Algorithms for Lung Cancer Stereotactic Body Radiotherapy (SBRT) Patients</title><source>Wiley Online Library Journals Frontfile Complete</source><source>Alma/SFX Local Collection</source><creator>Dhou, S ; Ionascu, D ; Lewis, J ; Williams, C</creator><creatorcontrib>Dhou, S ; Ionascu, D ; Lewis, J ; Williams, C</creatorcontrib><description>Purpose:
To study the variability of patient-specific motion models derived from 4-dimensional CT (4DCT) images using different deformable image registration (DIR) algorithms for lung cancer stereotactic body radiotherapy (SBRT) patients.
Methods:
Motion models are derived by 1) applying DIR between each 4DCT image and a reference image, resulting in a set of displacement vector fields (DVFs), and 2) performing principal component analysis (PCA) on the DVFs, resulting in a motion model (a set of eigenvectors capturing the variations in the DVFs). Three DIR algorithms were used: 1) Demons, 2) Horn-Schunck, and 3) iterative optical flow. The motion models derived were compared using patient 4DCT scans.
Results:
Motion models were derived and the variations were evaluated according to three criteria: 1) the average root mean square (RMS) difference which measures the absolute difference between the components of the eigenvectors, 2) the dot product between the eigenvectors which measures the angular difference between the eigenvectors in space, and 3) the Euclidean Model Norm (EMN), which is calculated by summing the dot products of an eigenvector with the first three eigenvectors from the reference motion model in quadrature. EMN measures how well an eigenvector can be reconstructed using another motion model derived using a different DIR algorithm. Results showed that comparing to a reference motion model (derived using the Demons algorithm), the eigenvectors of the motion model derived using the iterative optical flow algorithm has smaller RMS, larger dot product, and larger EMN values than those of the motion model derived using Horn-Schunck algorithm.
Conclusion:
The study showed that motion models vary depending on which DIR algorithms were used to derive them. The choice of a DIR algorithm may affect the accuracy of the resulting model, and it is important to assess the suitability of the algorithm chosen for a particular application.
This project was supported, in part, through a Master Research Agreement with Varian Medical Systems, Inc, Palo Alto, CA.</description><identifier>ISSN: 0094-2405</identifier><identifier>EISSN: 2473-4209</identifier><identifier>DOI: 10.1118/1.4955568</identifier><identifier>CODEN: MPHYA6</identifier><language>eng</language><publisher>United States: American Association of Physicists in Medicine</publisher><subject>60 APPLIED LIFE SCIENCES ; ACCURACY ; ALGORITHMS ; Biomedical modeling ; Cancer ; Computed tomography ; COMPUTERIZED TOMOGRAPHY ; Eigenvalues ; EIGENVECTORS ; Flow visualization ; Image registration ; ITERATIVE METHODS ; LUNGS ; Medical imaging ; NEOPLASMS ; Optical flow ; PATIENTS ; QUADRATURES ; RADIATION PROTECTION AND DOSIMETRY ; Radiation therapy ; RADIOTHERAPY</subject><ispartof>Medical physics (Lancaster), 2016-06, Vol.43 (6), p.3322-3322</ispartof><rights>American Association of Physicists in Medicine</rights><rights>2016 American Association of Physicists in Medicine</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1118%2F1.4955568$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,780,784,885,1416,27923,27924,45574</link.rule.ids><backlink>$$Uhttps://www.osti.gov/biblio/22624329$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Dhou, S</creatorcontrib><creatorcontrib>Ionascu, D</creatorcontrib><creatorcontrib>Lewis, J</creatorcontrib><creatorcontrib>Williams, C</creatorcontrib><title>SU-C-BRA-07: Variability of Patient-Specific Motion Models Derived Using Different Deformable Image Registration Algorithms for Lung Cancer Stereotactic Body Radiotherapy (SBRT) Patients</title><title>Medical physics (Lancaster)</title><description>Purpose:
To study the variability of patient-specific motion models derived from 4-dimensional CT (4DCT) images using different deformable image registration (DIR) algorithms for lung cancer stereotactic body radiotherapy (SBRT) patients.
Methods:
Motion models are derived by 1) applying DIR between each 4DCT image and a reference image, resulting in a set of displacement vector fields (DVFs), and 2) performing principal component analysis (PCA) on the DVFs, resulting in a motion model (a set of eigenvectors capturing the variations in the DVFs). Three DIR algorithms were used: 1) Demons, 2) Horn-Schunck, and 3) iterative optical flow. The motion models derived were compared using patient 4DCT scans.
Results:
Motion models were derived and the variations were evaluated according to three criteria: 1) the average root mean square (RMS) difference which measures the absolute difference between the components of the eigenvectors, 2) the dot product between the eigenvectors which measures the angular difference between the eigenvectors in space, and 3) the Euclidean Model Norm (EMN), which is calculated by summing the dot products of an eigenvector with the first three eigenvectors from the reference motion model in quadrature. EMN measures how well an eigenvector can be reconstructed using another motion model derived using a different DIR algorithm. Results showed that comparing to a reference motion model (derived using the Demons algorithm), the eigenvectors of the motion model derived using the iterative optical flow algorithm has smaller RMS, larger dot product, and larger EMN values than those of the motion model derived using Horn-Schunck algorithm.
Conclusion:
The study showed that motion models vary depending on which DIR algorithms were used to derive them. The choice of a DIR algorithm may affect the accuracy of the resulting model, and it is important to assess the suitability of the algorithm chosen for a particular application.
This project was supported, in part, through a Master Research Agreement with Varian Medical Systems, Inc, Palo Alto, CA.</description><subject>60 APPLIED LIFE SCIENCES</subject><subject>ACCURACY</subject><subject>ALGORITHMS</subject><subject>Biomedical modeling</subject><subject>Cancer</subject><subject>Computed tomography</subject><subject>COMPUTERIZED TOMOGRAPHY</subject><subject>Eigenvalues</subject><subject>EIGENVECTORS</subject><subject>Flow visualization</subject><subject>Image registration</subject><subject>ITERATIVE METHODS</subject><subject>LUNGS</subject><subject>Medical imaging</subject><subject>NEOPLASMS</subject><subject>Optical flow</subject><subject>PATIENTS</subject><subject>QUADRATURES</subject><subject>RADIATION PROTECTION AND DOSIMETRY</subject><subject>Radiation therapy</subject><subject>RADIOTHERAPY</subject><issn>0094-2405</issn><issn>2473-4209</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNp9kc9uEzEQxi0EEqFw4A0scaFILv679XJLUmgrpaJKGq4rr3ecGG3WkW1A-2o8HU4Tju3p04x-832jGYTeM3rBGNOf2YWslVKVfoEmXF4KIjmtX6IJpbUkXFL1Gr1J6SeltBKKTtDf1ZrMyWw5JfTyC_5hojet730ecXD43mQPQyarPVjvvMV3IfswFOmgT_gKov8NHV4nP2zwlXcOYsFL34W4M20P-HZnNoCXsPEpR_M4PO03Ifq83SVcMLz4VWbnZrAQ8SoXg5CNzSVrFroRL03nQ95CNPsRf1zNlg_n_7dKb9ErZ_oE7056htbfvj7Mb8ji-_XtfLoglgmliYaOSl1J22lqRbmAZaYFqBhUrlJCVK0UIHira6Ok45pJA61uOyu1OhTiDH04-oaUfZOsz2C3NgwD2NxwXnEpeF2o8yNlY0gpgmv20e9MHBtGm8NrGtacXlNYcmT_-B7Gp8Hm7v7Efzryh_DHKz5j_g8teJyr</recordid><startdate>201606</startdate><enddate>201606</enddate><creator>Dhou, S</creator><creator>Ionascu, D</creator><creator>Lewis, J</creator><creator>Williams, C</creator><general>American Association of Physicists in Medicine</general><scope>AAYXX</scope><scope>CITATION</scope><scope>OTOTI</scope></search><sort><creationdate>201606</creationdate><title>SU-C-BRA-07: Variability of Patient-Specific Motion Models Derived Using Different Deformable Image Registration Algorithms for Lung Cancer Stereotactic Body Radiotherapy (SBRT) Patients</title><author>Dhou, S ; Ionascu, D ; Lewis, J ; Williams, C</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1358-8ed04864cd80c3405c1abee61e6f65336b43e32b89a54f2814aeb8bdc485814a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>60 APPLIED LIFE SCIENCES</topic><topic>ACCURACY</topic><topic>ALGORITHMS</topic><topic>Biomedical modeling</topic><topic>Cancer</topic><topic>Computed tomography</topic><topic>COMPUTERIZED TOMOGRAPHY</topic><topic>Eigenvalues</topic><topic>EIGENVECTORS</topic><topic>Flow visualization</topic><topic>Image registration</topic><topic>ITERATIVE METHODS</topic><topic>LUNGS</topic><topic>Medical imaging</topic><topic>NEOPLASMS</topic><topic>Optical flow</topic><topic>PATIENTS</topic><topic>QUADRATURES</topic><topic>RADIATION PROTECTION AND DOSIMETRY</topic><topic>Radiation therapy</topic><topic>RADIOTHERAPY</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dhou, S</creatorcontrib><creatorcontrib>Ionascu, D</creatorcontrib><creatorcontrib>Lewis, J</creatorcontrib><creatorcontrib>Williams, C</creatorcontrib><collection>CrossRef</collection><collection>OSTI.GOV</collection><jtitle>Medical physics (Lancaster)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dhou, S</au><au>Ionascu, D</au><au>Lewis, J</au><au>Williams, C</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>SU-C-BRA-07: Variability of Patient-Specific Motion Models Derived Using Different Deformable Image Registration Algorithms for Lung Cancer Stereotactic Body Radiotherapy (SBRT) Patients</atitle><jtitle>Medical physics (Lancaster)</jtitle><date>2016-06</date><risdate>2016</risdate><volume>43</volume><issue>6</issue><spage>3322</spage><epage>3322</epage><pages>3322-3322</pages><issn>0094-2405</issn><eissn>2473-4209</eissn><coden>MPHYA6</coden><abstract>Purpose:
To study the variability of patient-specific motion models derived from 4-dimensional CT (4DCT) images using different deformable image registration (DIR) algorithms for lung cancer stereotactic body radiotherapy (SBRT) patients.
Methods:
Motion models are derived by 1) applying DIR between each 4DCT image and a reference image, resulting in a set of displacement vector fields (DVFs), and 2) performing principal component analysis (PCA) on the DVFs, resulting in a motion model (a set of eigenvectors capturing the variations in the DVFs). Three DIR algorithms were used: 1) Demons, 2) Horn-Schunck, and 3) iterative optical flow. The motion models derived were compared using patient 4DCT scans.
Results:
Motion models were derived and the variations were evaluated according to three criteria: 1) the average root mean square (RMS) difference which measures the absolute difference between the components of the eigenvectors, 2) the dot product between the eigenvectors which measures the angular difference between the eigenvectors in space, and 3) the Euclidean Model Norm (EMN), which is calculated by summing the dot products of an eigenvector with the first three eigenvectors from the reference motion model in quadrature. EMN measures how well an eigenvector can be reconstructed using another motion model derived using a different DIR algorithm. Results showed that comparing to a reference motion model (derived using the Demons algorithm), the eigenvectors of the motion model derived using the iterative optical flow algorithm has smaller RMS, larger dot product, and larger EMN values than those of the motion model derived using Horn-Schunck algorithm.
Conclusion:
The study showed that motion models vary depending on which DIR algorithms were used to derive them. The choice of a DIR algorithm may affect the accuracy of the resulting model, and it is important to assess the suitability of the algorithm chosen for a particular application.
This project was supported, in part, through a Master Research Agreement with Varian Medical Systems, Inc, Palo Alto, CA.</abstract><cop>United States</cop><pub>American Association of Physicists in Medicine</pub><doi>10.1118/1.4955568</doi><tpages>1</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0094-2405 |
ispartof | Medical physics (Lancaster), 2016-06, Vol.43 (6), p.3322-3322 |
issn | 0094-2405 2473-4209 |
language | eng |
recordid | cdi_crossref_primary_10_1118_1_4955568 |
source | Wiley Online Library Journals Frontfile Complete; Alma/SFX Local Collection |
subjects | 60 APPLIED LIFE SCIENCES ACCURACY ALGORITHMS Biomedical modeling Cancer Computed tomography COMPUTERIZED TOMOGRAPHY Eigenvalues EIGENVECTORS Flow visualization Image registration ITERATIVE METHODS LUNGS Medical imaging NEOPLASMS Optical flow PATIENTS QUADRATURES RADIATION PROTECTION AND DOSIMETRY Radiation therapy RADIOTHERAPY |
title | SU-C-BRA-07: Variability of Patient-Specific Motion Models Derived Using Different Deformable Image Registration Algorithms for Lung Cancer Stereotactic Body Radiotherapy (SBRT) Patients |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-13T04%3A31%3A11IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-wiley_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=SU-C-BRA-07:%20Variability%20of%20Patient-Specific%20Motion%20Models%20Derived%20Using%20Different%20Deformable%20Image%20Registration%20Algorithms%20for%20Lung%20Cancer%20Stereotactic%20Body%20Radiotherapy%20(SBRT)%20Patients&rft.jtitle=Medical%20physics%20(Lancaster)&rft.au=Dhou,%20S&rft.date=2016-06&rft.volume=43&rft.issue=6&rft.spage=3322&rft.epage=3322&rft.pages=3322-3322&rft.issn=0094-2405&rft.eissn=2473-4209&rft.coden=MPHYA6&rft_id=info:doi/10.1118/1.4955568&rft_dat=%3Cwiley_cross%3EMP5568%3C/wiley_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |