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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Patient-Specific Non-Linear Finite Element Modelling for Predicting Soft Organ Deformation in Real-Time; Application to Non-Rigid Neuroimage Registration</title><author><name sortKey="Wittek, Adam" sort="Wittek, Adam" uniqKey="Wittek A" first="Adam" last="Wittek">Adam Wittek</name><affiliation><nlm:aff id="A1">Intelligent Systems for Medicine Laboratory, School of Mechanical Engineering, The University of Western Australia, 35 Stirling Highway, 6009 Crawley, WA, Australia</nlm:aff></affiliation></author><author><name sortKey="Joldes, Grand" sort="Joldes, Grand" uniqKey="Joldes G" first="Grand" last="Joldes">Grand Joldes</name><affiliation><nlm:aff id="A1">Intelligent Systems for Medicine Laboratory, School of Mechanical Engineering, The University of Western Australia, 35 Stirling Highway, 6009 Crawley, WA, Australia</nlm:aff></affiliation></author><author><name sortKey="Couton, Mathieu" sort="Couton, Mathieu" uniqKey="Couton M" first="Mathieu" last="Couton">Mathieu Couton</name><affiliation><nlm:aff id="A1">Intelligent Systems for Medicine Laboratory, School of Mechanical Engineering, The University of Western Australia, 35 Stirling Highway, 6009 Crawley, WA, Australia</nlm:aff></affiliation><affiliation><nlm:aff id="A3">Institut Francais de Mecanique Avancee IFMA Clermont Ferrand, 63175 Aubiere Cedex, France</nlm:aff></affiliation></author><author><name sortKey="Warfield, Simon K" sort="Warfield, Simon K" uniqKey="Warfield S" first="Simon K." last="Warfield">Simon K. Warfield</name><affiliation><nlm:aff id="A2">Radiology, Children’s Hospital 300 Longwood Avenue, Boston, MA02115, USA</nlm:aff></affiliation></author><author><name sortKey="Miller, Karol" sort="Miller, Karol" uniqKey="Miller K" first="Karol" last="Miller">Karol Miller</name><affiliation><nlm:aff id="A1">Intelligent Systems for Medicine Laboratory, School of Mechanical Engineering, The University of Western Australia, 35 Stirling Highway, 6009 Crawley, WA, Australia</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">20868706</idno><idno type="pmc">3107968</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3107968</idno><idno type="RBID">PMC:3107968</idno><idno type="doi">10.1016/j.pbiomolbio.2010.09.001</idno><date when="2010">2010</date><idno type="wicri:Area/Pmc/Corpus">001530</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">001530</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Patient-Specific Non-Linear Finite Element Modelling for Predicting Soft Organ Deformation in Real-Time; Application to Non-Rigid Neuroimage Registration</title><author><name sortKey="Wittek, Adam" sort="Wittek, Adam" uniqKey="Wittek A" first="Adam" last="Wittek">Adam Wittek</name><affiliation><nlm:aff id="A1">Intelligent Systems for Medicine Laboratory, School of Mechanical Engineering, The University of Western Australia, 35 Stirling Highway, 6009 Crawley, WA, Australia</nlm:aff></affiliation></author><author><name sortKey="Joldes, Grand" sort="Joldes, Grand" uniqKey="Joldes G" first="Grand" last="Joldes">Grand Joldes</name><affiliation><nlm:aff id="A1">Intelligent Systems for Medicine Laboratory, School of Mechanical Engineering, The University of Western Australia, 35 Stirling Highway, 6009 Crawley, WA, Australia</nlm:aff></affiliation></author><author><name sortKey="Couton, Mathieu" sort="Couton, Mathieu" uniqKey="Couton M" first="Mathieu" last="Couton">Mathieu Couton</name><affiliation><nlm:aff id="A1">Intelligent Systems for Medicine Laboratory, School of Mechanical Engineering, The University of Western Australia, 35 Stirling Highway, 6009 Crawley, WA, Australia</nlm:aff></affiliation><affiliation><nlm:aff id="A3">Institut Francais de Mecanique Avancee IFMA Clermont Ferrand, 63175 Aubiere Cedex, France</nlm:aff></affiliation></author><author><name sortKey="Warfield, Simon K" sort="Warfield, Simon K" uniqKey="Warfield S" first="Simon K." last="Warfield">Simon K. Warfield</name><affiliation><nlm:aff id="A2">Radiology, Children’s Hospital 300 Longwood Avenue, Boston, MA02115, USA</nlm:aff></affiliation></author><author><name sortKey="Miller, Karol" sort="Miller, Karol" uniqKey="Miller K" first="Karol" last="Miller">Karol Miller</name><affiliation><nlm:aff id="A1">Intelligent Systems for Medicine Laboratory, School of Mechanical Engineering, The University of Western Australia, 35 Stirling Highway, 6009 Crawley, WA, Australia</nlm:aff></affiliation></author></analytic><series><title level="j">Progress in biophysics and molecular biology</title><idno type="ISSN">0079-6107</idno><idno type="eISSN">1873-1732</idno><imprint><date when="2010">2010</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="P2">Long computation times of non-linear (i.e. accounting for geometric and material non-linearity) biomechanical models have been regarded as one of the key factors preventing application of such models in predicting organ deformation for image-guided surgery. This contribution presents real-time patient-specific computation of the deformation field within the brain for six cases of brain shift induced by craniotomy (i.e. surgical opening of the skull) using specialised non-linear finite element procedures implemented on a graphics processing unit (GPU). In contrast to commercial finite element codes that rely on an updated Lagrangian formulation and implicit integration in time domain for steady state solutions, our procedures utilise the total Lagrangian formulation with explicit time stepping and dynamic relaxation. We used patient-specific finite element meshes consisting of hexahedral and non-locking tetrahedral elements, together with realistic material properties for the brain tissue and appropriate contact conditions at the boundaries. The loading was defined by prescribing deformations on the brain surface under the craniotomy. Application of the computed deformation fields to register (i.e. align) the preoperative and intraoperative images indicated that the models very accurately predict the intraoperative deformations within the brain. For each case, computing the brain deformation field took less than 4 s using a NVIDIA Tesla C870 GPU, which is two orders of magnitude reduction in computation time in comparison to our previous study in which the brain deformation was predicted using a commercial finite element solver executed on a personal computer.</p></div></front></TEI><pmc article-type="research-article" xml:lang="en"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<pmc-dir>properties manuscript</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-journal-id">0401233</journal-id><journal-id journal-id-type="pubmed-jr-id">6688</journal-id><journal-id journal-id-type="nlm-ta">Prog Biophys Mol Biol</journal-id><journal-title-group><journal-title>Progress in biophysics and molecular biology</journal-title></journal-title-group><issn pub-type="ppub">0079-6107</issn><issn pub-type="epub">1873-1732</issn></journal-meta><article-meta><article-id pub-id-type="pmid">20868706</article-id><article-id pub-id-type="pmc">3107968</article-id><article-id pub-id-type="doi">10.1016/j.pbiomolbio.2010.09.001</article-id><article-id pub-id-type="manuscript">NIHMS251271</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Patient-Specific Non-Linear Finite Element Modelling for Predicting Soft Organ Deformation in Real-Time; Application to Non-Rigid Neuroimage Registration</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Wittek</surname><given-names>Adam</given-names></name><degrees>Ph.D.</degrees><xref ref-type="aff" rid="A1">1</xref><xref ref-type="corresp" rid="CR1">*</xref></contrib><contrib contrib-type="author"><name><surname>Joldes</surname><given-names>Grand</given-names></name><degrees>Ph.D.</degrees><xref ref-type="aff" rid="A1">1</xref></contrib><contrib contrib-type="author"><name><surname>Couton</surname><given-names>Mathieu</given-names></name><xref ref-type="aff" rid="A1">1</xref><xref ref-type="aff" rid="A3">3</xref><xref ref-type="author-notes" rid="FN1">a</xref></contrib><contrib contrib-type="author"><name><surname>Warfield</surname><given-names>Simon K.</given-names></name><degrees>Ph.D.</degrees><xref ref-type="aff" rid="A2">2</xref></contrib><contrib contrib-type="author"><name><surname>Miller</surname><given-names>Karol</given-names></name><degrees>D.Sc.</degrees><xref ref-type="aff" rid="A1">1</xref></contrib></contrib-group><aff id="A1"><label>1</label>Intelligent Systems for Medicine Laboratory, School of Mechanical Engineering, The University of Western Australia, 35 Stirling Highway, 6009 Crawley, WA, Australia</aff><aff id="A2"><label>2</label>Radiology, Children’s Hospital 300 Longwood Avenue, Boston, MA02115, USA</aff><aff id="A3"><label>3</label>Institut Francais de Mecanique Avancee IFMA Clermont Ferrand, 63175 Aubiere Cedex, France</aff><author-notes><corresp id="CR1"><label>*</label>Corresponding author: <email>adwit@mech.uwa.edu.au</email> tel. +61 8 6488 7362, fax. +61 8 6488 1024
</corresp><fn id="FN1"><label>a</label><p id="P1">Study conducted during the internship at the Intelligent Systems for Medicine Laboratory, The University of Western Australia</p></fn></author-notes><pub-date pub-type="nihms-submitted"><day>3</day><month>12</month><year>2010</year></pub-date><pub-date pub-type="epub"><day>22</day><month>9</month><year>2010</year></pub-date><pub-date pub-type="ppub"><month>12</month><year>2010</year></pub-date><pub-date pub-type="pmc-release"><day>1</day><month>12</month><year>2011</year></pub-date><volume>103</volume><issue>2-3</issue><fpage>292</fpage><lpage>303</lpage><permissions><copyright-statement>© 2010 Elsevier Ltd. All rights reserved.</copyright-statement><copyright-year>2010</copyright-year></permissions><abstract><p id="P2">Long computation times of non-linear (i.e. accounting for geometric and material non-linearity) biomechanical models have been regarded as one of the key factors preventing application of such models in predicting organ deformation for image-guided surgery. This contribution presents real-time patient-specific computation of the deformation field within the brain for six cases of brain shift induced by craniotomy (i.e. surgical opening of the skull) using specialised non-linear finite element procedures implemented on a graphics processing unit (GPU). In contrast to commercial finite element codes that rely on an updated Lagrangian formulation and implicit integration in time domain for steady state solutions, our procedures utilise the total Lagrangian formulation with explicit time stepping and dynamic relaxation. We used patient-specific finite element meshes consisting of hexahedral and non-locking tetrahedral elements, together with realistic material properties for the brain tissue and appropriate contact conditions at the boundaries. The loading was defined by prescribing deformations on the brain surface under the craniotomy. Application of the computed deformation fields to register (i.e. align) the preoperative and intraoperative images indicated that the models very accurately predict the intraoperative deformations within the brain. For each case, computing the brain deformation field took less than 4 s using a NVIDIA Tesla C870 GPU, which is two orders of magnitude reduction in computation time in comparison to our previous study in which the brain deformation was predicted using a commercial finite element solver executed on a personal computer.</p></abstract><kwd-group><kwd>brain shift</kwd><kwd>tissue deformation</kwd><kwd>non-linear biomechanical models</kwd><kwd>total Lagrangian formulation</kwd><kwd>explicit time integration</kwd><kwd>real-time computation</kwd><kwd>graphics processing unit</kwd></kwd-group><funding-group><award-group><funding-source country="United States">National Institute of Biomedical Imaging and Bioengineering : NIBIB</funding-source><award-id>R03 EB008680-01A1 || EB</award-id></award-group><award-group><funding-source country="United States">National Cancer Institute : NCI</funding-source><award-id>R03 CA126466-02 || CA</award-id></award-group><award-group><funding-source country="United States">National Cancer Institute : NCI</funding-source><award-id>R03 CA126466-01A1 || CA</award-id></award-group><award-group><funding-source country="United States">National Center for Research Resources : NCRR</funding-source><award-id>R01 RR021885-04S1 || RR</award-id></award-group><award-group><funding-source country="United States">National Center for Research Resources : NCRR</funding-source><award-id>R01 RR021885-04 || RR</award-id></award-group><award-group><funding-source country="United States">National Center for Research Resources : NCRR</funding-source><award-id>R01 RR021885-03 || RR</award-id></award-group><award-group><funding-source country="United States">National Center for Research Resources : NCRR</funding-source><award-id>R01 RR021885-02 || RR</award-id></award-group><award-group><funding-source country="United States">National Center for Research Resources : NCRR</funding-source><award-id>R01 RR021885-01A1 || RR</award-id></award-group><award-group><funding-source country="United States">National Library of Medicine : NLM</funding-source><award-id>R01 LM010033-02 || LM</award-id></award-group><award-group><funding-source country="United States">National Library of Medicine : NLM</funding-source><award-id>R01 LM010033-01A1 || LM</award-id></award-group><award-group><funding-source country="United States">National Institute of Biomedical Imaging and Bioengineering : NIBIB</funding-source><award-id>R01 EB008015-04 || EB</award-id></award-group><award-group><funding-source country="United States">National Institute of Biomedical Imaging and Bioengineering : NIBIB</funding-source><award-id>R01 EB008015-03 || EB</award-id></award-group><award-group><funding-source country="United States">National Institute of Biomedical Imaging and Bioengineering : NIBIB</funding-source><award-id>R01 EB008015-02S1 || EB</award-id></award-group><award-group><funding-source country="United States">National Institute of Biomedical Imaging and Bioengineering : NIBIB</funding-source><award-id>R01 EB008015-02 || EB</award-id></award-group><award-group><funding-source country="United States">National Institute of Biomedical Imaging and Bioengineering : NIBIB</funding-source><award-id>R01 EB008015-01 || EB</award-id></award-group></funding-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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