Neurosurgery Simulation Using Non-linear Finite Element Modeling and Haptic Interaction.
Identifieur interne : 000C64 ( PubMed/Corpus ); précédent : 000C63; suivant : 000C65Neurosurgery Simulation Using Non-linear Finite Element Modeling and Haptic Interaction.
Auteurs : Huai-Ping Lee ; Michel Audette ; Grand Roman Joldes ; Andinet EnquobahrieSource :
- Proceedings of SPIE--the International Society for Optical Engineering [ 0277-786X ] ; 2012.
Abstract
Real-time surgical simulation is becoming an important component of surgical training. To meet the real-time requirement, however, the accuracy of the biomechancial modeling of soft tissue is often compromised due to computing resource constraints. Furthermore, haptic integration presents an additional challenge with its requirement for a high update rate. As a result, most real-time surgical simulation systems employ a linear elasticity model, simplified numerical methods such as the boundary element method or spring-particle systems, and coarse volumetric meshes. However, these systems are not clinically realistic. We present here an ongoing work aimed at developing an efficient and physically realistic neurosurgery simulator using a non-linear finite element method (FEM) with haptic interaction. Real-time finite element analysis is achieved by utilizing the total Lagrangian explicit dynamic (TLED) formulation and GPU acceleration of per-node and per-element operations. We employ a virtual coupling method for separating deformable body simulation and collision detection from haptic rendering, which needs to be updated at a much higher rate than the visual simulation. The system provides accurate biomechancial modeling of soft tissue while retaining a real-time performance with haptic interaction. However, our experiments showed that the stability of the simulator depends heavily on the material property of the tissue and the speed of colliding objects. Hence, additional efforts including dynamic relaxation are required to improve the stability of the system.
DOI: 10.1117/12.911987
PubMed: 24465116
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To meet the real-time requirement, however, the accuracy of the biomechancial modeling of soft tissue is often compromised due to computing resource constraints. Furthermore, haptic integration presents an additional challenge with its requirement for a high update rate. As a result, most real-time surgical simulation systems employ a linear elasticity model, simplified numerical methods such as the boundary element method or spring-particle systems, and coarse volumetric meshes. However, these systems are not clinically realistic. We present here an ongoing work aimed at developing an efficient and physically realistic neurosurgery simulator using a non-linear finite element method (FEM) with haptic interaction. Real-time finite element analysis is achieved by utilizing the total Lagrangian explicit dynamic (TLED) formulation and GPU acceleration of per-node and per-element operations. We employ a virtual coupling method for separating deformable body simulation and collision detection from haptic rendering, which needs to be updated at a much higher rate than the visual simulation. The system provides accurate biomechancial modeling of soft tissue while retaining a real-time performance with haptic interaction. However, our experiments showed that the stability of the simulator depends heavily on the material property of the tissue and the speed of colliding objects. Hence, additional efforts including dynamic relaxation are required to improve the stability of the system.</div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">24465116</PMID><DateCreated><Year>2014</Year><Month>1</Month><Day>27</Day></DateCreated><DateRevised><Year>2015</Year><Month>5</Month><Day>6</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Print">0277-786X</ISSN><JournalIssue CitedMedium="Print"><Volume>8316</Volume><PubDate><Year>2012</Year><Month>Feb</Month><Day>23</Day></PubDate></JournalIssue><Title>Proceedings of SPIE--the International Society for Optical Engineering</Title><ISOAbbreviation>Proc SPIE Int Soc Opt Eng</ISOAbbreviation></Journal><ArticleTitle>Neurosurgery Simulation Using Non-linear Finite Element Modeling and Haptic Interaction.</ArticleTitle><Pagination><MedlinePgn>83160H</MedlinePgn></Pagination><Abstract><AbstractText>Real-time surgical simulation is becoming an important component of surgical training. To meet the real-time requirement, however, the accuracy of the biomechancial modeling of soft tissue is often compromised due to computing resource constraints. Furthermore, haptic integration presents an additional challenge with its requirement for a high update rate. As a result, most real-time surgical simulation systems employ a linear elasticity model, simplified numerical methods such as the boundary element method or spring-particle systems, and coarse volumetric meshes. However, these systems are not clinically realistic. We present here an ongoing work aimed at developing an efficient and physically realistic neurosurgery simulator using a non-linear finite element method (FEM) with haptic interaction. Real-time finite element analysis is achieved by utilizing the total Lagrangian explicit dynamic (TLED) formulation and GPU acceleration of per-node and per-element operations. We employ a virtual coupling method for separating deformable body simulation and collision detection from haptic rendering, which needs to be updated at a much higher rate than the visual simulation. The system provides accurate biomechancial modeling of soft tissue while retaining a real-time performance with haptic interaction. However, our experiments showed that the stability of the simulator depends heavily on the material property of the tissue and the speed of colliding objects. Hence, additional efforts including dynamic relaxation are required to improve the stability of the system.</AbstractText></Abstract><AuthorList><Author><LastName>Lee</LastName><ForeName>Huai-Ping</ForeName><Initials>HP</Initials><AffiliationInfo><Affiliation>Kitware Inc., Clifton Park, NY 12065, USA ; Dept. of Computer Science, Univ. of North Carolina, Chapel Hill, NC 27599, USA.</Affiliation></AffiliationInfo></Author><Author><LastName>Audette</LastName><ForeName>Michel</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Dept. of MSVE, Old Dominion University, Norfolk, VA 23529, USA.</Affiliation></AffiliationInfo></Author><Author><LastName>Joldes</LastName><ForeName>Grand Roman</ForeName><Initials>GR</Initials><AffiliationInfo><Affiliation>School of Mechanical Engineering, The Univ. of Western Australia, Perth, Australia.</Affiliation></AffiliationInfo></Author><Author><LastName>Enquobahrie</LastName><ForeName>Andinet</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Kitware Inc., Clifton Park, NY 12065, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>ENG</Language><GrantList><Grant><GrantID>R43 NS067742</GrantID><Acronym>NS</Acronym><Agency>NINDS NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="">JOURNAL ARTICLE</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><MedlineTA>Proc SPIE Int Soc Opt Eng</MedlineTA><NlmUniqueID>101524122</NlmUniqueID><ISSNLinking>1018-4732</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">finite element method</Keyword><Keyword MajorTopicYN="N">haptic rendering</Keyword><Keyword MajorTopicYN="N">non-linear biomechanics</Keyword><Keyword MajorTopicYN="N">surgical simulation</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2014</Year><Month>1</Month><Day>28</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2012</Year><Month>2</Month><Day>23</Day><Hour>0</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2012</Year><Month>2</Month><Day>23</Day><Hour>0</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="doi">10.1117/12.911987</ArticleId><ArticleId IdType="pubmed">24465116</ArticleId><ArticleId IdType="pmc">PMC3898833</ArticleId><ArticleId IdType="mid">NIHMS395325</ArticleId></ArticleIdList><pmc-dir>nihms</pmc-dir>
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