A Method for Registering Diffusion Weighted Magnetic Resonance Images
Diffusion weighted magnetic resonance (DWMR or DW) imaging is a fast evolving technique to investigate the connectivity of brain white matter by measuring the self-diffusion of the water molecules in the tissue. Registration is a key step in group analysis of the DW images that may lead to understan...
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description | Diffusion weighted magnetic resonance (DWMR or DW) imaging is a fast evolving technique to investigate the connectivity of brain white matter by measuring the self-diffusion of the water molecules in the tissue. Registration is a key step in group analysis of the DW images that may lead to understanding of functional and structural variability of the normal brain, understanding disease process, and improving neurosurgical planning. In this paper, we present a new method for registering DW images. The method works directly on the diffusion weighted images without using tensor reconstruction, fiber tracking, and fiber clustering. Therefore, the performance of the method does not rely on the accuracy and robustness of these steps. Moreover, since all the information in the original diffusion weighted images is used for registration, the results of the method is robust to imaging noise. We demonstrate the method on intra-subject registration with an affine transform using DW images acquired on the same scanner with the same imaging protocol. Extension to deformable registration for images acquired on different scanners and/or with different imaging protocols is also discussed. |
doi_str_mv | 10.1007/11866763_73 |
format | Book Chapter |
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Extension to deformable registration for images acquired on different scanners and/or with different imaging protocols is also discussed.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 354044727X</identifier><identifier>ISBN: 9783540447276</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 3540447288</identifier><identifier>EISBN: 9783540447283</identifier><identifier>DOI: 10.1007/11866763_73</identifier><identifier>PMID: 17354821</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Artificial Intelligence ; Brain - anatomy & histology ; Brain White Matter ; Deformable Registration ; Diffusion Magnetic Resonance Imaging - methods ; Diffusion Weight Magnetic Resonance Image ; Diffusion Weighted Image ; Fractional Anisotropy ; Humans ; Image Enhancement - methods ; Image Interpretation, Computer-Assisted - methods ; Pattern Recognition, Automated - methods ; Reproducibility of Results ; Sensitivity and Specificity ; Subtraction Technique</subject><ispartof>Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006, 2006, Vol.9 (Pt 2), p.594-602</ispartof><rights>Springer-Verlag Berlin Heidelberg 2006</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><relation>Lecture Notes in Computer Science</relation></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/11866763_73$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/11866763_73$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>775,776,780,789,27902,38232,41418,42487</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/17354821$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Larsen, Rasmus</contributor><contributor>Sporring, Jon</contributor><contributor>Nielsen, Mads</contributor><creatorcontrib>Tao, Xiaodong</creatorcontrib><creatorcontrib>Miller, James V.</creatorcontrib><title>A Method for Registering Diffusion Weighted Magnetic Resonance Images</title><title>Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006</title><addtitle>Med Image Comput Comput Assist Interv</addtitle><description>Diffusion weighted magnetic resonance (DWMR or DW) imaging is a fast evolving technique to investigate the connectivity of brain white matter by measuring the self-diffusion of the water molecules in the tissue. Registration is a key step in group analysis of the DW images that may lead to understanding of functional and structural variability of the normal brain, understanding disease process, and improving neurosurgical planning. In this paper, we present a new method for registering DW images. The method works directly on the diffusion weighted images without using tensor reconstruction, fiber tracking, and fiber clustering. Therefore, the performance of the method does not rely on the accuracy and robustness of these steps. Moreover, since all the information in the original diffusion weighted images is used for registration, the results of the method is robust to imaging noise. We demonstrate the method on intra-subject registration with an affine transform using DW images acquired on the same scanner with the same imaging protocol. Extension to deformable registration for images acquired on different scanners and/or with different imaging protocols is also discussed.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Brain - anatomy & histology</subject><subject>Brain White Matter</subject><subject>Deformable Registration</subject><subject>Diffusion Magnetic Resonance Imaging - methods</subject><subject>Diffusion Weight Magnetic Resonance Image</subject><subject>Diffusion Weighted Image</subject><subject>Fractional Anisotropy</subject><subject>Humans</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>Subtraction Technique</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>354044727X</isbn><isbn>9783540447276</isbn><isbn>3540447288</isbn><isbn>9783540447283</isbn><fulltext>true</fulltext><rsrctype>book_chapter</rsrctype><creationdate>2006</creationdate><recordtype>book_chapter</recordtype><sourceid>EIF</sourceid><recordid>eNpNkEtPwzAQhM1LNJSeuCNfOQS8ceLHsSoFKrVCQiC4RY6zSQ00ieL0wL_HVUGwl5F2vl1phpALYNfAmLwBUEJIwXPJD8gZz1KWpjJR6pBEIABizlN99GfIt2MSMc6SWMuUj8jE-3cWJlNaaXFKRiADqhKIyHxKVzis25JWbU-fsHZ-wN41Nb11VbX1rm3oK7p6PWBJV6ZucHA2cL5tTGORLjamRn9OTirz6XHyo2Pycjd_nj3Ey8f7xWy6jDsOeogxAbTcMJUUyqpCCMjKKkGmFVirRJEaIyRjFrUJyxKSCo0NFyJ4mUo1H5PL_d9uW2ywzLvebUz_lf_mCcDVHvDdLgT2edG2Hz4Hlu-KzP8Vyb8Bg11dig</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Tao, Xiaodong</creator><creator>Miller, James V.</creator><general>Springer Berlin Heidelberg</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope></search><sort><creationdate>2006</creationdate><title>A Method for Registering Diffusion Weighted Magnetic Resonance Images</title><author>Tao, Xiaodong ; Miller, James V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p319t-e21ec3a082b8c8b6615df2e0981cc86b4aa6700ce9ae09d12feacec3686b58493</frbrgroupid><rsrctype>book_chapters</rsrctype><prefilter>book_chapters</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Brain - anatomy & histology</topic><topic>Brain White Matter</topic><topic>Deformable Registration</topic><topic>Diffusion Magnetic Resonance Imaging - methods</topic><topic>Diffusion Weight Magnetic Resonance Image</topic><topic>Diffusion Weighted Image</topic><topic>Fractional Anisotropy</topic><topic>Humans</topic><topic>Image Enhancement - methods</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Pattern Recognition, Automated - methods</topic><topic>Reproducibility of Results</topic><topic>Sensitivity and Specificity</topic><topic>Subtraction Technique</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tao, Xiaodong</creatorcontrib><creatorcontrib>Miller, James V.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tao, Xiaodong</au><au>Miller, James V.</au><au>Larsen, Rasmus</au><au>Sporring, Jon</au><au>Nielsen, Mads</au><format>book</format><genre>bookitem</genre><ristype>CHAP</ristype><atitle>A Method for Registering Diffusion Weighted Magnetic Resonance Images</atitle><btitle>Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006</btitle><addtitle>Med Image Comput Comput Assist Interv</addtitle><seriestitle>Lecture Notes in Computer Science</seriestitle><date>2006</date><risdate>2006</risdate><volume>9</volume><issue>Pt 2</issue><spage>594</spage><epage>602</epage><pages>594-602</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>354044727X</isbn><isbn>9783540447276</isbn><eisbn>3540447288</eisbn><eisbn>9783540447283</eisbn><abstract>Diffusion weighted magnetic resonance (DWMR or DW) imaging is a fast evolving technique to investigate the connectivity of brain white matter by measuring the self-diffusion of the water molecules in the tissue. Registration is a key step in group analysis of the DW images that may lead to understanding of functional and structural variability of the normal brain, understanding disease process, and improving neurosurgical planning. In this paper, we present a new method for registering DW images. The method works directly on the diffusion weighted images without using tensor reconstruction, fiber tracking, and fiber clustering. Therefore, the performance of the method does not rely on the accuracy and robustness of these steps. Moreover, since all the information in the original diffusion weighted images is used for registration, the results of the method is robust to imaging noise. We demonstrate the method on intra-subject registration with an affine transform using DW images acquired on the same scanner with the same imaging protocol. Extension to deformable registration for images acquired on different scanners and/or with different imaging protocols is also discussed.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>17354821</pmid><doi>10.1007/11866763_73</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
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ispartof | Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006, 2006, Vol.9 (Pt 2), p.594-602 |
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language | eng |
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source | MEDLINE; Springer Books |
subjects | Algorithms Artificial Intelligence Brain - anatomy & histology Brain White Matter Deformable Registration Diffusion Magnetic Resonance Imaging - methods Diffusion Weight Magnetic Resonance Image Diffusion Weighted Image Fractional Anisotropy Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Pattern Recognition, Automated - methods Reproducibility of Results Sensitivity and Specificity Subtraction Technique |
title | A Method for Registering Diffusion Weighted Magnetic Resonance Images |
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