Fiber direction estimation using constrained spherical deconvolution based on multi-model response function
Constrained spherical deconvolution can quantify white matter fiber orientation distribution information from diffusion magnetic resonance imaging data. But this method is only applicable to single shell diffusion magnetic resonance imaging data and will provide wrong fiber orientation information i...
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| Veröffentlicht in: | Sheng wu yi xue gong cheng xue za zhi 2022-12, Vol.39 (6), p.1117-1126 |
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| container_title | Sheng wu yi xue gong cheng xue za zhi |
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| creator | Pan, Yingyu Wang, Yuanjun |
| description | Constrained spherical deconvolution can quantify white matter fiber orientation distribution information from diffusion magnetic resonance imaging data. But this method is only applicable to single shell diffusion magnetic resonance imaging data and will provide wrong fiber orientation information in white matter tissue which contains isotropic diffusion signals. To solve these problems, this paper proposes a constrained spherical deconvolution method based on multi-model response function. Multi-shell data can improve the stability of fiber orientation, and multi-model response function can attenuate isotropic diffusion signals in white matter, providing more accurate fiber orientation information. Synthetic data and real brain data from public database were used to verify the effectiveness of this algorithm. The results demonstrate that the proposed algorithm can attenuate isotropic diffusion signals in white matter and overcome the influence of partial volume effect on fiber direction estimation, thus esti |
| doi_str_mv | 10.7507/1001-5515.202202034 |
| format | Article |
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But this method is only applicable to single shell diffusion magnetic resonance imaging data and will provide wrong fiber orientation information in white matter tissue which contains isotropic diffusion signals. To solve these problems, this paper proposes a constrained spherical deconvolution method based on multi-model response function. Multi-shell data can improve the stability of fiber orientation, and multi-model response function can attenuate isotropic diffusion signals in white matter, providing more accurate fiber orientation information. Synthetic data and real brain data from public database were used to verify the effectiveness of this algorithm. The results demonstrate that the proposed algorithm can attenuate isotropic diffusion signals in white matter and overcome the influence of partial volume effect on fiber direction estimation, thus esti</description><identifier>ISSN: 1001-5515</identifier><identifier>DOI: 10.7507/1001-5515.202202034</identifier><identifier>PMID: 36575080</identifier><language>chi</language><publisher>China: Sichuan Society for Biomedical Engineering</publisher><subject>Algorithms ; Brain ; Databases, Factual ; Deconvolution ; Diffusion ; Diffusion Magnetic Resonance Imaging - methods ; Fiber orientation ; Image Processing, Computer-Assisted - methods ; Magnetic resonance imaging ; Medical imaging ; Neuroimaging ; Resonance ; Response functions ; Substantia alba ; White Matter - diagnostic imaging</subject><ispartof>Sheng wu yi xue gong cheng xue za zhi, 2022-12, Vol.39 (6), p.1117-1126</ispartof><rights>Copyright Sichuan Society for Biomedical Engineering 2022</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36575080$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Pan, Yingyu</creatorcontrib><creatorcontrib>Wang, Yuanjun</creatorcontrib><title>Fiber direction estimation using constrained spherical deconvolution based on multi-model response function</title><title>Sheng wu yi xue gong cheng xue za zhi</title><addtitle>Sheng Wu Yi Xue Gong Cheng Xue Za Zhi</addtitle><description>Constrained spherical deconvolution can quantify white matter fiber orientation distribution information from diffusion magnetic resonance imaging data. But this method is only applicable to single shell diffusion magnetic resonance imaging data and will provide wrong fiber orientation information in white matter tissue which contains isotropic diffusion signals. To solve these problems, this paper proposes a constrained spherical deconvolution method based on multi-model response function. Multi-shell data can improve the stability of fiber orientation, and multi-model response function can attenuate isotropic diffusion signals in white matter, providing more accurate fiber orientation information. Synthetic data and real brain data from public database were used to verify the effectiveness of this algorithm. The results demonstrate that the proposed algorithm can attenuate isotropic diffusion signals in white matter and overcome the influence of partial volume effect on fiber direction estimation, thus esti</description><subject>Algorithms</subject><subject>Brain</subject><subject>Databases, Factual</subject><subject>Deconvolution</subject><subject>Diffusion</subject><subject>Diffusion Magnetic Resonance Imaging - methods</subject><subject>Fiber orientation</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Magnetic resonance imaging</subject><subject>Medical imaging</subject><subject>Neuroimaging</subject><subject>Resonance</subject><subject>Response functions</subject><subject>Substantia alba</subject><subject>White Matter - diagnostic imaging</subject><issn>1001-5515</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdkEtLAzEQx3NQbKn9BIIsePGyNY_No0cptgoFL3peZjezGt2XyUbw2xtq9SAMzDDzm9efkAtGV1pSfcMoZbmUTK445cmoKE7I_C87I8sQXEUpN1QpI87ITCiZOg2dk_etq9Bn1nmsJzf0GYbJdXAIY3D9S1YPfZg8uB5tFsZX9K6GNrOY8p9DGw9kBSFVU9DFdnJ5N1hsM49hTL2YNbE_zD4npw20AZdHvyDP27unzX2-f9w9bG73-chkMeXArZVSMwUMuK6RNaIB1Gvk2ohKcVNDZWzDrJaisJZJWWhsgKIFi2BALMj1z9zRDx8xPVR2LtTYttDjEEPJtVwncYwWCb36h74N0ffpukQplRYKVSTq8kjFqkNbjj5J5L_KXxnFN8egdpU</recordid><startdate>20221225</startdate><enddate>20221225</enddate><creator>Pan, Yingyu</creator><creator>Wang, Yuanjun</creator><general>Sichuan Society for Biomedical Engineering</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>20221225</creationdate><title>Fiber direction estimation using constrained spherical deconvolution based on multi-model response function</title><author>Pan, Yingyu ; Wang, Yuanjun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p154t-a2dd55716a1a27ce1f3fae79e2783b628cab8df1d7534dd15547efa0edadea8a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>chi</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Brain</topic><topic>Databases, Factual</topic><topic>Deconvolution</topic><topic>Diffusion</topic><topic>Diffusion Magnetic Resonance Imaging - methods</topic><topic>Fiber orientation</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Magnetic resonance imaging</topic><topic>Medical imaging</topic><topic>Neuroimaging</topic><topic>Resonance</topic><topic>Response functions</topic><topic>Substantia alba</topic><topic>White Matter - diagnostic imaging</topic><toplevel>online_resources</toplevel><creatorcontrib>Pan, Yingyu</creatorcontrib><creatorcontrib>Wang, Yuanjun</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Sheng wu yi xue gong cheng xue za zhi</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pan, Yingyu</au><au>Wang, Yuanjun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fiber direction estimation using constrained spherical deconvolution based on multi-model response function</atitle><jtitle>Sheng wu yi xue gong cheng xue za zhi</jtitle><addtitle>Sheng Wu Yi Xue Gong Cheng Xue Za Zhi</addtitle><date>2022-12-25</date><risdate>2022</risdate><volume>39</volume><issue>6</issue><spage>1117</spage><epage>1126</epage><pages>1117-1126</pages><issn>1001-5515</issn><abstract>Constrained spherical deconvolution can quantify white matter fiber orientation distribution information from diffusion magnetic resonance imaging data. But this method is only applicable to single shell diffusion magnetic resonance imaging data and will provide wrong fiber orientation information in white matter tissue which contains isotropic diffusion signals. To solve these problems, this paper proposes a constrained spherical deconvolution method based on multi-model response function. Multi-shell data can improve the stability of fiber orientation, and multi-model response function can attenuate isotropic diffusion signals in white matter, providing more accurate fiber orientation information. Synthetic data and real brain data from public database were used to verify the effectiveness of this algorithm. The results demonstrate that the proposed algorithm can attenuate isotropic diffusion signals in white matter and overcome the influence of partial volume effect on fiber direction estimation, thus esti</abstract><cop>China</cop><pub>Sichuan Society for Biomedical Engineering</pub><pmid>36575080</pmid><doi>10.7507/1001-5515.202202034</doi><tpages>10</tpages></addata></record> |
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| subjects | Algorithms Brain Databases, Factual Deconvolution Diffusion Diffusion Magnetic Resonance Imaging - methods Fiber orientation Image Processing, Computer-Assisted - methods Magnetic resonance imaging Medical imaging Neuroimaging Resonance Response functions Substantia alba White Matter - diagnostic imaging |
| title | Fiber direction estimation using constrained spherical deconvolution based on multi-model response function |
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