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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Adaptive Real-Time Bioheat Transfer Models for Computer Driven MR-guided Laser Induced Thermal Therapy</title><author><name sortKey="Fuentes, D" sort="Fuentes, D" uniqKey="Fuentes D" first="D." last="Fuentes">D. Fuentes</name></author><author><name sortKey="Feng, Y" sort="Feng, Y" uniqKey="Feng Y" first="Y." last="Feng">Y. Feng</name></author><author><name sortKey="Elliott, A" sort="Elliott, A" uniqKey="Elliott A" first="A." last="Elliott">A. Elliott</name></author><author><name sortKey="Shetty, A" sort="Shetty, A" uniqKey="Shetty A" first="A." last="Shetty">A. Shetty</name></author><author><name sortKey="Mcnichols, R J" sort="Mcnichols, R J" uniqKey="Mcnichols R" first="R. J." last="Mcnichols">R. J. Mcnichols</name></author><author><name sortKey="Oden, J T" sort="Oden, J T" uniqKey="Oden J" first="J. T." last="Oden">J. T. Oden</name></author><author><name sortKey="Stafford, R J" sort="Stafford, R J" uniqKey="Stafford R" first="R. J." last="Stafford">R. J. Stafford</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">20142153</idno><idno type="pmc">3857613</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3857613</idno><idno type="RBID">PMC:3857613</idno><idno type="doi">10.1109/TBME.2009.2037733</idno><date when="2010">2010</date><idno type="wicri:Area/Pmc/Corpus">000418</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Adaptive Real-Time Bioheat Transfer Models for Computer Driven MR-guided Laser Induced Thermal Therapy</title><author><name sortKey="Fuentes, D" sort="Fuentes, D" uniqKey="Fuentes D" first="D." last="Fuentes">D. Fuentes</name></author><author><name sortKey="Feng, Y" sort="Feng, Y" uniqKey="Feng Y" first="Y." last="Feng">Y. Feng</name></author><author><name sortKey="Elliott, A" sort="Elliott, A" uniqKey="Elliott A" first="A." last="Elliott">A. Elliott</name></author><author><name sortKey="Shetty, A" sort="Shetty, A" uniqKey="Shetty A" first="A." last="Shetty">A. Shetty</name></author><author><name sortKey="Mcnichols, R J" sort="Mcnichols, R J" uniqKey="Mcnichols R" first="R. J." last="Mcnichols">R. J. Mcnichols</name></author><author><name sortKey="Oden, J T" sort="Oden, J T" uniqKey="Oden J" first="J. T." last="Oden">J. T. Oden</name></author><author><name sortKey="Stafford, R J" sort="Stafford, R J" uniqKey="Stafford R" first="R. J." last="Stafford">R. J. Stafford</name></author></analytic><series><title level="j">IEEE transactions on bio-medical engineering</title><idno type="ISSN">0018-9294</idno><idno type="eISSN">1558-2531</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="P1">The treatment times of laser induced thermal therapies (LITT) guided by computational prediction are determined by the convergence behavior of PDE constrained optimization problems. In this work, we investigate the convergence behavior of a bioheat transfer constrained calibration problem to assess the feasibility of applying to real-time patient specific data. The calibration techniques utilize multi-planar thermal images obtained from the non-destructive <italic>in vivo</italic> heating of canine prostate. The calibration techniques attempt to adaptively recover the bio-thermal heterogeneities within the tissue on a patient specific level and results in a formidable PDE constrained optimization problem to be solved in real time. A comprehensive calibration study is performed with both homogeneous and spatially heterogeneous bio-thermal model parameters with and without constitutive nonlinearities. Initial results presented here indicate that the calibration problems involving the inverse solution of thousands of model parameters can converge to a solution within three minutes and decrease the
<inline-formula><mml:math id="M1" overflow="scroll"><mml:msubsup><mml:mrow><mml:mo stretchy="false">‖</mml:mo><mml:mo>·</mml:mo><mml:mo stretchy="false">‖</mml:mo></mml:mrow><mml:mrow><mml:msub><mml:mi>L</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo stretchy="false">(</mml:mo><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:mi>T</mml:mi><mml:mo>;</mml:mo><mml:msub><mml:mi>L</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo stretchy="false">(</mml:mo><mml:mi mathvariant="normal">Ω</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msubsup></mml:math></inline-formula> norm of the difference between computational prediction and the measured temperature values to a patient specific regime.</p></div></front></TEI><pmc article-type="research-article"><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">0012737</journal-id><journal-id journal-id-type="pubmed-jr-id">4157</journal-id><journal-id journal-id-type="nlm-ta">IEEE Trans Biomed Eng</journal-id><journal-id journal-id-type="iso-abbrev">IEEE Trans Biomed Eng</journal-id><journal-title-group><journal-title>IEEE transactions on bio-medical engineering</journal-title></journal-title-group><issn pub-type="ppub">0018-9294</issn><issn pub-type="epub">1558-2531</issn></journal-meta><article-meta><article-id pub-id-type="pmid">20142153</article-id><article-id pub-id-type="pmc">3857613</article-id><article-id pub-id-type="doi">10.1109/TBME.2009.2037733</article-id><article-id pub-id-type="manuscript">NIHMS526379</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Adaptive Real-Time Bioheat Transfer Models for Computer Driven MR-guided Laser Induced Thermal Therapy</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Fuentes</surname><given-names>D.</given-names></name><aff id="A1">The University of Texas M.D. Anderson Cancer Center, Department of Imaging Physics, Houston TX 77030, USA</aff><email>dtfuentes@mdanderson.org</email></contrib><contrib contrib-type="author"><name><surname>Feng</surname><given-names>Y.</given-names></name><aff id="A2">Computational Bioengineering and Nanotechnology Lab, The University of Texas at San Antonio, San Antonio, TX 78749, USA</aff><email>yusheng.feng@utsa.edu</email></contrib><contrib contrib-type="author"><name><surname>Elliott</surname><given-names>A.</given-names></name><aff id="A3">The University of Texas M.D. Anderson Cancer Center, Department of Imaging Physics, Houston TX 77030, USA</aff><email>andrew.elliott@mdanderson.org</email></contrib><contrib contrib-type="author"><name><surname>Shetty</surname><given-names>A.</given-names></name><aff id="A4">The University of Texas M.D. Anderson Cancer Center, Department of Imaging Physics, Houston TX 77030, USA</aff><email>anil.shetty@mdanderson.org, </email></contrib><contrib contrib-type="author"><name><surname>McNichols</surname><given-names>R. J.</given-names></name><aff id="A5">BioTex, Inc., Houston TX 77054, USA</aff><email>roger@biotexmedical.com</email></contrib><contrib contrib-type="author"><name><surname>Oden</surname><given-names>J. T.</given-names></name><aff id="A6">Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin TX 78712, USA</aff><email>oden@ices.utexas.edu</email></contrib><contrib contrib-type="author"><name><surname>Stafford</surname><given-names>R. J.</given-names></name><aff id="A7">The University of Texas M.D. Anderson Cancer Center, Department of Imaging Physics, Houston TX 77030, USA</aff><xref rid="FN1" ref-type="author-notes">*</xref></contrib></contrib-group><author-notes><corresp id="FN1"><label>*</label>Corresponding Author: R. J. Stafford <email>jstafford@mdanderson.org</email></corresp></author-notes><pub-date pub-type="nihms-submitted"><day>9</day><month>11</month><year>2013</year></pub-date><pub-date pub-type="epub"><day>05</day><month>2</month><year>2010</year></pub-date><pub-date pub-type="ppub"><month>5</month><year>2010</year></pub-date><pub-date pub-type="pmc-release"><day>10</day><month>12</month><year>2013</year></pub-date><volume>57</volume><issue>5</issue><elocation-id>10.1109/TBME.2009.2037733</elocation-id><abstract><p id="P1">The treatment times of laser induced thermal therapies (LITT) guided by computational prediction are determined by the convergence behavior of PDE constrained optimization problems. In this work, we investigate the convergence behavior of a bioheat transfer constrained calibration problem to assess the feasibility of applying to real-time patient specific data. The calibration techniques utilize multi-planar thermal images obtained from the non-destructive <italic>in vivo</italic> heating of canine prostate. The calibration techniques attempt to adaptively recover the bio-thermal heterogeneities within the tissue on a patient specific level and results in a formidable PDE constrained optimization problem to be solved in real time. A comprehensive calibration study is performed with both homogeneous and spatially heterogeneous bio-thermal model parameters with and without constitutive nonlinearities. Initial results presented here indicate that the calibration problems involving the inverse solution of thousands of model parameters can converge to a solution within three minutes and decrease the
<inline-formula><mml:math id="M1" overflow="scroll"><mml:msubsup><mml:mrow><mml:mo stretchy="false">‖</mml:mo><mml:mo>·</mml:mo><mml:mo stretchy="false">‖</mml:mo></mml:mrow><mml:mrow><mml:msub><mml:mi>L</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo stretchy="false">(</mml:mo><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:mi>T</mml:mi><mml:mo>;</mml:mo><mml:msub><mml:mi>L</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo stretchy="false">(</mml:mo><mml:mi mathvariant="normal">Ω</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msubsup></mml:math></inline-formula> norm of the difference between computational prediction and the measured temperature values to a patient specific regime.</p></abstract><kwd-group><title>Index Terms</title><kwd>PDE contrained optimization</kwd><kwd>real-time computing</kwd><kwd>MR temperature imaging</kwd></kwd-group><funding-group><award-group><funding-source country="United States">National Cancer Institute : NCI</funding-source><award-id>T32 CA119930 || CA</award-id></award-group><award-group><funding-source country="United States">National Cancer Institute : NCI</funding-source><award-id>K25 CA116291 || CA</award-id></award-group></funding-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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