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An EM-like reconstruction method for diffuse optical tomography
Diffuse optical tomography (DOT) is an optical imaging modality which provides the spatial distributions of optical parameters inside an object. The forward model of DOT is described by the diffusion approximation of radiative transfer equation, while the DOT is to reconstruct the optical parameters...
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Published in: | International journal for numerical methods in biomedical engineering 2010-09, Vol.26 (9), p.1099-1116 |
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description | Diffuse optical tomography (DOT) is an optical imaging modality which provides the spatial distributions of optical parameters inside an object. The forward model of DOT is described by the diffusion approximation of radiative transfer equation, while the DOT is to reconstruct the optical parameters from boundary measurements. In this paper, an EM‐like iterative reconstruction method specifically for the steady state DOT problem is developed. Previous iterative reconstruction methods are mostly based on the assumption that the measurement noise is Gaussian, and are of least‐squares type. In this paper, with the assumption that the boundary measurements have independent and identical Poisson distributions, the inverse problem of DOT is solved by maximizing a log‐likelihood functional with inequality constraints, and then an EM‐like reconstruction algorithm is developed according to the Kuhn–Tucker condition. The proposed algorithm is a variant of the well‐known EM algorithm. The performance of the proposed algorithm is tested with three‐dimensional numerical simulation. Copyright © 2010 John Wiley & Sons, Ltd. |
doi_str_mv | 10.1002/cnm.1387 |
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The forward model of DOT is described by the diffusion approximation of radiative transfer equation, while the DOT is to reconstruct the optical parameters from boundary measurements. In this paper, an EM‐like iterative reconstruction method specifically for the steady state DOT problem is developed. Previous iterative reconstruction methods are mostly based on the assumption that the measurement noise is Gaussian, and are of least‐squares type. In this paper, with the assumption that the boundary measurements have independent and identical Poisson distributions, the inverse problem of DOT is solved by maximizing a log‐likelihood functional with inequality constraints, and then an EM‐like reconstruction algorithm is developed according to the Kuhn–Tucker condition. The proposed algorithm is a variant of the well‐known EM algorithm. The performance of the proposed algorithm is tested with three‐dimensional numerical simulation. Copyright © 2010 John Wiley & Sons, Ltd.</description><identifier>ISSN: 2040-7939</identifier><identifier>ISSN: 2040-7947</identifier><identifier>EISSN: 2040-7947</identifier><identifier>DOI: 10.1002/cnm.1387</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>absorption coefficient ; Algorithms ; Applied sciences ; Artificial intelligence ; Biological and medical sciences ; Boundaries ; Computer science; control theory; systems ; diffuse optical tomography (DOT) ; Diffusion ; diffusion coefficient ; Exact sciences and technology ; Investigative techniques, diagnostic techniques (general aspects) ; Iterative methods ; Kuhn-Tucker condition ; Mathematical analysis ; Mathematical models ; Medical sciences ; Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques ; Pattern recognition. Digital image processing. Computational geometry ; Poissonian noise ; Reconstruction ; Tomography</subject><ispartof>International journal for numerical methods in biomedical engineering, 2010-09, Vol.26 (9), p.1099-1116</ispartof><rights>Copyright © 2010 John Wiley & Sons, Ltd.</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3997-cdb36d75334a93ea242e5e4910e1584015ecdf3543c8376b914385dce0d991793</citedby><cites>FETCH-LOGICAL-c3997-cdb36d75334a93ea242e5e4910e1584015ecdf3543c8376b914385dce0d991793</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=23180114$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Caifang</creatorcontrib><title>An EM-like reconstruction method for diffuse optical tomography</title><title>International journal for numerical methods in biomedical engineering</title><addtitle>Int. J. Numer. Meth. Biomed. Engng</addtitle><description>Diffuse optical tomography (DOT) is an optical imaging modality which provides the spatial distributions of optical parameters inside an object. The forward model of DOT is described by the diffusion approximation of radiative transfer equation, while the DOT is to reconstruct the optical parameters from boundary measurements. In this paper, an EM‐like iterative reconstruction method specifically for the steady state DOT problem is developed. Previous iterative reconstruction methods are mostly based on the assumption that the measurement noise is Gaussian, and are of least‐squares type. In this paper, with the assumption that the boundary measurements have independent and identical Poisson distributions, the inverse problem of DOT is solved by maximizing a log‐likelihood functional with inequality constraints, and then an EM‐like reconstruction algorithm is developed according to the Kuhn–Tucker condition. The proposed algorithm is a variant of the well‐known EM algorithm. The performance of the proposed algorithm is tested with three‐dimensional numerical simulation. Copyright © 2010 John Wiley & Sons, Ltd.</description><subject>absorption coefficient</subject><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Biological and medical sciences</subject><subject>Boundaries</subject><subject>Computer science; control theory; systems</subject><subject>diffuse optical tomography (DOT)</subject><subject>Diffusion</subject><subject>diffusion coefficient</subject><subject>Exact sciences and technology</subject><subject>Investigative techniques, diagnostic techniques (general aspects)</subject><subject>Iterative methods</subject><subject>Kuhn-Tucker condition</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Medical sciences</subject><subject>Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Poissonian noise</subject><subject>Reconstruction</subject><subject>Tomography</subject><issn>2040-7939</issn><issn>2040-7947</issn><issn>2040-7947</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><recordid>eNqNkEFLwzAUx4MoOObAj9CL4KWaNGnTnGQONx3bRJh4DFn66uLaZiYdum9vxsY8Cb7Le4cf__fnh9AlwTcE4-RWN_UNoTk_QZ0EMxxzwfjp8abiHPW8_8BhEiEEpx1012-ih2lcmRVEDrRtfOs2ujW2iWpol7aISuuiwpTlxkNk163RqopaW9t3p9bL7QU6K1XloXfYXfQ6fJgPHuPJ8-hp0J_EmoZHsS4WNCt4SilTgoJKWAIpMEEwkDRnmKSgi5KmjOqc8mwhCKN5WmjAhRAkVO-i633u2tnPDfhW1sZrqCrVgN14STjPMsaY-AeKE5wHSWn-i2pnvXdQyrUztXLbAMmdURmMyp3RgF4dUpUPCkqnGm38kU8oyTEJrbso3nNfpoLtn3lyMJsecg-88S18H3nlVjLjlKfybTaSL-P5cHyfC8npD1ETkN8</recordid><startdate>201009</startdate><enddate>201009</enddate><creator>Wang, Caifang</creator><general>John Wiley & Sons, Ltd</general><general>Wiley</general><scope>BSCLL</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7QO</scope><scope>P64</scope></search><sort><creationdate>201009</creationdate><title>An EM-like reconstruction method for diffuse optical tomography</title><author>Wang, Caifang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3997-cdb36d75334a93ea242e5e4910e1584015ecdf3543c8376b914385dce0d991793</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>absorption coefficient</topic><topic>Algorithms</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Biological and medical sciences</topic><topic>Boundaries</topic><topic>Computer science; control theory; systems</topic><topic>diffuse optical tomography (DOT)</topic><topic>Diffusion</topic><topic>diffusion coefficient</topic><topic>Exact sciences and technology</topic><topic>Investigative techniques, diagnostic techniques (general aspects)</topic><topic>Iterative methods</topic><topic>Kuhn-Tucker condition</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Medical sciences</topic><topic>Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Poissonian noise</topic><topic>Reconstruction</topic><topic>Tomography</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Caifang</creatorcontrib><collection>Istex</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology Research Abstracts</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>International journal for numerical methods in biomedical engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Caifang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An EM-like reconstruction method for diffuse optical tomography</atitle><jtitle>International journal for numerical methods in biomedical engineering</jtitle><addtitle>Int. J. Numer. Meth. Biomed. Engng</addtitle><date>2010-09</date><risdate>2010</risdate><volume>26</volume><issue>9</issue><spage>1099</spage><epage>1116</epage><pages>1099-1116</pages><issn>2040-7939</issn><issn>2040-7947</issn><eissn>2040-7947</eissn><abstract>Diffuse optical tomography (DOT) is an optical imaging modality which provides the spatial distributions of optical parameters inside an object. The forward model of DOT is described by the diffusion approximation of radiative transfer equation, while the DOT is to reconstruct the optical parameters from boundary measurements. In this paper, an EM‐like iterative reconstruction method specifically for the steady state DOT problem is developed. Previous iterative reconstruction methods are mostly based on the assumption that the measurement noise is Gaussian, and are of least‐squares type. In this paper, with the assumption that the boundary measurements have independent and identical Poisson distributions, the inverse problem of DOT is solved by maximizing a log‐likelihood functional with inequality constraints, and then an EM‐like reconstruction algorithm is developed according to the Kuhn–Tucker condition. The proposed algorithm is a variant of the well‐known EM algorithm. The performance of the proposed algorithm is tested with three‐dimensional numerical simulation. Copyright © 2010 John Wiley & Sons, Ltd.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><doi>10.1002/cnm.1387</doi><tpages>18</tpages></addata></record> |
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subjects | absorption coefficient Algorithms Applied sciences Artificial intelligence Biological and medical sciences Boundaries Computer science control theory systems diffuse optical tomography (DOT) Diffusion diffusion coefficient Exact sciences and technology Investigative techniques, diagnostic techniques (general aspects) Iterative methods Kuhn-Tucker condition Mathematical analysis Mathematical models Medical sciences Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques Pattern recognition. Digital image processing. Computational geometry Poissonian noise Reconstruction Tomography |
title | An EM-like reconstruction method for diffuse optical tomography |
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