Patient-Specific Biomechanical Model for the Prediction of Lung Motion From 4-D CT Images
This paper presents an approach to predict the deformation of the lungs and surrounding organs during respiration. The framework incorporates a computational model of the respiratory system, which comprises an anatomical model extracted from computed tomography (CT) images at end-expiration (EE), an...
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| Veröffentlicht in: | IEEE transactions on medical imaging 2015-02, Vol.34 (2), p.599-607 |
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| container_title | IEEE transactions on medical imaging |
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| creator | Fuerst, Bernhard Mansi, Tommaso Carnis, Francois Salzle, Martin Jingdan Zhang Declerck, Jerome Boettger, Thomas Bayouth, John Navab, Nassir Kamen, Ali |
| description | This paper presents an approach to predict the deformation of the lungs and surrounding organs during respiration. The framework incorporates a computational model of the respiratory system, which comprises an anatomical model extracted from computed tomography (CT) images at end-expiration (EE), and a biomechanical model of the respiratory physiology, including the material behavior and interactions between organs. A personalization step is performed to automatically estimate patient-specific thoracic pressure, which drives the biomechanical model. The zone-wise pressure values are obtained by using a trust-region optimizer, where the estimated motion is compared to CT images at end-inspiration (EI). A detailed convergence analysis in terms of mesh resolution, time stepping and number of pressure zones on the surface of the thoracic cavity is carried out. The method is then tested on five public datasets. Results show that the model is able to predict the respiratory motion with an average landmark error of 3.40 ±1.0 mm over the entire respiratory cycle. The estimated 3-D lung motion may constitute as an advanced 3-D surrogate for more accurate medical image reconstruction and patient respiratory analysis. |
| doi_str_mv | 10.1109/TMI.2014.2363611 |
| format | Article |
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The framework incorporates a computational model of the respiratory system, which comprises an anatomical model extracted from computed tomography (CT) images at end-expiration (EE), and a biomechanical model of the respiratory physiology, including the material behavior and interactions between organs. A personalization step is performed to automatically estimate patient-specific thoracic pressure, which drives the biomechanical model. The zone-wise pressure values are obtained by using a trust-region optimizer, where the estimated motion is compared to CT images at end-inspiration (EI). A detailed convergence analysis in terms of mesh resolution, time stepping and number of pressure zones on the surface of the thoracic cavity is carried out. The method is then tested on five public datasets. Results show that the model is able to predict the respiratory motion with an average landmark error of 3.40 ±1.0 mm over the entire respiratory cycle. The estimated 3-D lung motion may constitute as an advanced 3-D surrogate for more accurate medical image reconstruction and patient respiratory analysis.</description><identifier>ISSN: 0278-0062</identifier><identifier>EISSN: 1558-254X</identifier><identifier>DOI: 10.1109/TMI.2014.2363611</identifier><identifier>PMID: 25343757</identifier><identifier>CODEN: ITMID4</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Biological system modeling ; Biomechanical modeling ; Biomechanical Phenomena - physiology ; Biomechanics ; Computational modeling ; Computed tomography ; Computer Simulation ; Deformable models ; Four-Dimensional Computed Tomography - methods ; Humans ; Image Processing, Computer-Assisted - methods ; lung ; Lung - anatomy & histology ; Lung - physiology ; Lungs ; Models, Biological ; motion prediction ; Movement ; personalization ; Precision Medicine - methods ; Respiration ; respiratory motion ; Thorax</subject><ispartof>IEEE transactions on medical imaging, 2015-02, Vol.34 (2), p.599-607</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c385t-b95340b346bb168d4665daedf0b395ddaf326f65d314a8fe1083e9d9e65bee0d3</citedby><cites>FETCH-LOGICAL-c385t-b95340b346bb168d4665daedf0b395ddaf326f65d314a8fe1083e9d9e65bee0d3</cites><orcidid>0000-0001-7439-8312</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6926856$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27923,27924,54757</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6926856$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25343757$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Fuerst, Bernhard</creatorcontrib><creatorcontrib>Mansi, Tommaso</creatorcontrib><creatorcontrib>Carnis, Francois</creatorcontrib><creatorcontrib>Salzle, Martin</creatorcontrib><creatorcontrib>Jingdan Zhang</creatorcontrib><creatorcontrib>Declerck, Jerome</creatorcontrib><creatorcontrib>Boettger, Thomas</creatorcontrib><creatorcontrib>Bayouth, John</creatorcontrib><creatorcontrib>Navab, Nassir</creatorcontrib><creatorcontrib>Kamen, Ali</creatorcontrib><title>Patient-Specific Biomechanical Model for the Prediction of Lung Motion From 4-D CT Images</title><title>IEEE transactions on medical imaging</title><addtitle>TMI</addtitle><addtitle>IEEE Trans Med Imaging</addtitle><description>This paper presents an approach to predict the deformation of the lungs and surrounding organs during respiration. The framework incorporates a computational model of the respiratory system, which comprises an anatomical model extracted from computed tomography (CT) images at end-expiration (EE), and a biomechanical model of the respiratory physiology, including the material behavior and interactions between organs. A personalization step is performed to automatically estimate patient-specific thoracic pressure, which drives the biomechanical model. The zone-wise pressure values are obtained by using a trust-region optimizer, where the estimated motion is compared to CT images at end-inspiration (EI). A detailed convergence analysis in terms of mesh resolution, time stepping and number of pressure zones on the surface of the thoracic cavity is carried out. The method is then tested on five public datasets. Results show that the model is able to predict the respiratory motion with an average landmark error of 3.40 ±1.0 mm over the entire respiratory cycle. The estimated 3-D lung motion may constitute as an advanced 3-D surrogate for more accurate medical image reconstruction and patient respiratory analysis.</description><subject>Biological system modeling</subject><subject>Biomechanical modeling</subject><subject>Biomechanical Phenomena - physiology</subject><subject>Biomechanics</subject><subject>Computational modeling</subject><subject>Computed tomography</subject><subject>Computer Simulation</subject><subject>Deformable models</subject><subject>Four-Dimensional Computed Tomography - methods</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>lung</subject><subject>Lung - anatomy & histology</subject><subject>Lung - physiology</subject><subject>Lungs</subject><subject>Models, Biological</subject><subject>motion prediction</subject><subject>Movement</subject><subject>personalization</subject><subject>Precision Medicine - methods</subject><subject>Respiration</subject><subject>respiratory motion</subject><subject>Thorax</subject><issn>0278-0062</issn><issn>1558-254X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNo9kE1Lw0AQhhdRbK3eBUH26CV1P7Kb5KjVaqHFghX0FDbZ2XYlydZscvDfu7XV0zAzzzsMD0KXlIwpJdntajEbM0LjMeOSS0qP0JAKkUZMxO_HaEhYkkaESDZAZ95_kkAKkp2iARM85olIhuhjqToLTRe9bqG0xpb43roayo1qbKkqvHAaKmxci7sN4GUL2paddQ12Bs_7Zh2A33bauhrH0QOerPCsVmvw5-jEqMrDxaGO0Nv0cTV5juYvT7PJ3TwqeSq6qMjCL6TgsSwKKlMdSym0Am3CLBNaK8OZNGHGaaxSA5SkHDKdgRQFANF8hG72d7et--rBd3ltfQlVpRpwvc-pFCwWMiEsoGSPlq3zvgWTb1tbq_Y7pyTfCc2D0HwnND8IDZHrw_W-qEH_B_4MBuBqD1gA-F_LjMlUSP4DZ8h4oQ</recordid><startdate>201502</startdate><enddate>201502</enddate><creator>Fuerst, Bernhard</creator><creator>Mansi, Tommaso</creator><creator>Carnis, Francois</creator><creator>Salzle, Martin</creator><creator>Jingdan Zhang</creator><creator>Declerck, Jerome</creator><creator>Boettger, Thomas</creator><creator>Bayouth, John</creator><creator>Navab, Nassir</creator><creator>Kamen, Ali</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-7439-8312</orcidid></search><sort><creationdate>201502</creationdate><title>Patient-Specific Biomechanical Model for the Prediction of Lung Motion From 4-D CT Images</title><author>Fuerst, Bernhard ; Mansi, Tommaso ; Carnis, Francois ; Salzle, Martin ; Jingdan Zhang ; Declerck, Jerome ; Boettger, Thomas ; Bayouth, John ; Navab, Nassir ; Kamen, Ali</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c385t-b95340b346bb168d4665daedf0b395ddaf326f65d314a8fe1083e9d9e65bee0d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Biological system modeling</topic><topic>Biomechanical modeling</topic><topic>Biomechanical Phenomena - physiology</topic><topic>Biomechanics</topic><topic>Computational modeling</topic><topic>Computed tomography</topic><topic>Computer Simulation</topic><topic>Deformable models</topic><topic>Four-Dimensional Computed Tomography - methods</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>lung</topic><topic>Lung - anatomy & histology</topic><topic>Lung - physiology</topic><topic>Lungs</topic><topic>Models, Biological</topic><topic>motion prediction</topic><topic>Movement</topic><topic>personalization</topic><topic>Precision Medicine - methods</topic><topic>Respiration</topic><topic>respiratory motion</topic><topic>Thorax</topic><toplevel>online_resources</toplevel><creatorcontrib>Fuerst, Bernhard</creatorcontrib><creatorcontrib>Mansi, Tommaso</creatorcontrib><creatorcontrib>Carnis, Francois</creatorcontrib><creatorcontrib>Salzle, Martin</creatorcontrib><creatorcontrib>Jingdan Zhang</creatorcontrib><creatorcontrib>Declerck, Jerome</creatorcontrib><creatorcontrib>Boettger, Thomas</creatorcontrib><creatorcontrib>Bayouth, John</creatorcontrib><creatorcontrib>Navab, Nassir</creatorcontrib><creatorcontrib>Kamen, Ali</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transactions on medical imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Fuerst, Bernhard</au><au>Mansi, Tommaso</au><au>Carnis, Francois</au><au>Salzle, Martin</au><au>Jingdan Zhang</au><au>Declerck, Jerome</au><au>Boettger, Thomas</au><au>Bayouth, John</au><au>Navab, Nassir</au><au>Kamen, Ali</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Patient-Specific Biomechanical Model for the Prediction of Lung Motion From 4-D CT Images</atitle><jtitle>IEEE transactions on medical imaging</jtitle><stitle>TMI</stitle><addtitle>IEEE Trans Med Imaging</addtitle><date>2015-02</date><risdate>2015</risdate><volume>34</volume><issue>2</issue><spage>599</spage><epage>607</epage><pages>599-607</pages><issn>0278-0062</issn><eissn>1558-254X</eissn><coden>ITMID4</coden><abstract>This paper presents an approach to predict the deformation of the lungs and surrounding organs during respiration. The framework incorporates a computational model of the respiratory system, which comprises an anatomical model extracted from computed tomography (CT) images at end-expiration (EE), and a biomechanical model of the respiratory physiology, including the material behavior and interactions between organs. A personalization step is performed to automatically estimate patient-specific thoracic pressure, which drives the biomechanical model. The zone-wise pressure values are obtained by using a trust-region optimizer, where the estimated motion is compared to CT images at end-inspiration (EI). A detailed convergence analysis in terms of mesh resolution, time stepping and number of pressure zones on the surface of the thoracic cavity is carried out. The method is then tested on five public datasets. Results show that the model is able to predict the respiratory motion with an average landmark error of 3.40 ±1.0 mm over the entire respiratory cycle. 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| subjects | Biological system modeling Biomechanical modeling Biomechanical Phenomena - physiology Biomechanics Computational modeling Computed tomography Computer Simulation Deformable models Four-Dimensional Computed Tomography - methods Humans Image Processing, Computer-Assisted - methods lung Lung - anatomy & histology Lung - physiology Lungs Models, Biological motion prediction Movement personalization Precision Medicine - methods Respiration respiratory motion Thorax |
| title | Patient-Specific Biomechanical Model for the Prediction of Lung Motion From 4-D CT Images |
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