Comparison of parametric FBP and OS-EM reconstruction algorithm images for PET dynamic study
An ordered subsets expectation maximization (OS-EM) algorithm is used for image reconstruction to suppress image noise and to make non-negative value images. We have applied OS-EM to a digital brain phantom and to human brain 18F-FDG PET kinetic studies to generate parametric images. A 45 min dynami...
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| Veröffentlicht in: | Annals of nuclear medicine 2001-10, Vol.15 (5), p.417-423 |
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| container_title | Annals of nuclear medicine |
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| creator | Oda, K Toyama, H Uemura, K Ikoma, Y Kimura, Y Senda, M |
| description | An ordered subsets expectation maximization (OS-EM) algorithm is used for image reconstruction to suppress image noise and to make non-negative value images. We have applied OS-EM to a digital brain phantom and to human brain 18F-FDG PET kinetic studies to generate parametric images. A 45 min dynamic scan was performed starting injection of FDG with a 2D PET scanner. The images were reconstructed with OS-EM (6 iterations, 16 subsets) and with filtered backprojection (FBP), and K1, k2 and k3 images were created by the Marquardt non-linear least squares method based on the 3-parameter kinetic model. Although the OS-EM activity images correlated fairly well with those obtained by FBP, the pixel correlations were poor for the k2 and k3 parametric images, but the plots were scattered along the line of identity and the mean values for K1, k2 and k3 obtained by OS-EM were almost equal to those by FBP. The kinetic fitting error for OS-EM was no smaller than that for FBP. The results suggest that OS-EM is not necessarily superior to FBP for creating parametric images. |
| doi_str_mv | 10.1007/BF02988345 |
| format | Article |
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We have applied OS-EM to a digital brain phantom and to human brain 18F-FDG PET kinetic studies to generate parametric images. A 45 min dynamic scan was performed starting injection of FDG with a 2D PET scanner. The images were reconstructed with OS-EM (6 iterations, 16 subsets) and with filtered backprojection (FBP), and K1, k2 and k3 images were created by the Marquardt non-linear least squares method based on the 3-parameter kinetic model. Although the OS-EM activity images correlated fairly well with those obtained by FBP, the pixel correlations were poor for the k2 and k3 parametric images, but the plots were scattered along the line of identity and the mean values for K1, k2 and k3 obtained by OS-EM were almost equal to those by FBP. The kinetic fitting error for OS-EM was no smaller than that for FBP. The results suggest that OS-EM is not necessarily superior to FBP for creating parametric images.</description><identifier>ISSN: 0914-7187</identifier><identifier>EISSN: 1864-6433</identifier><identifier>DOI: 10.1007/BF02988345</identifier><identifier>PMID: 11758946</identifier><language>eng</language><publisher>Japan: Springer Nature B.V</publisher><subject>Algorithms ; Brain - diagnostic imaging ; Digital imaging ; Fluorodeoxyglucose F18 ; Humans ; Image Enhancement - methods ; Image processing ; Image reconstruction ; Kinetics ; Least squares method ; Phantoms, Imaging ; Positron emission ; Positron emission tomography ; Radiopharmaceuticals ; Studies ; Tomography, Emission-Computed - methods</subject><ispartof>Annals of nuclear medicine, 2001-10, Vol.15 (5), p.417-423</ispartof><rights>Springer 2001</rights><rights>Springer 2001.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c498t-48790802efa7bfe884b70c4691f6a9396af753d0df62b0ec78afa59fa20cba0b3</citedby><cites>FETCH-LOGICAL-c498t-48790802efa7bfe884b70c4691f6a9396af753d0df62b0ec78afa59fa20cba0b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/11758946$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Oda, K</creatorcontrib><creatorcontrib>Toyama, H</creatorcontrib><creatorcontrib>Uemura, K</creatorcontrib><creatorcontrib>Ikoma, Y</creatorcontrib><creatorcontrib>Kimura, Y</creatorcontrib><creatorcontrib>Senda, M</creatorcontrib><title>Comparison of parametric FBP and OS-EM reconstruction algorithm images for PET dynamic study</title><title>Annals of nuclear medicine</title><addtitle>Ann Nucl Med</addtitle><description>An ordered subsets expectation maximization (OS-EM) algorithm is used for image reconstruction to suppress image noise and to make non-negative value images. We have applied OS-EM to a digital brain phantom and to human brain 18F-FDG PET kinetic studies to generate parametric images. A 45 min dynamic scan was performed starting injection of FDG with a 2D PET scanner. The images were reconstructed with OS-EM (6 iterations, 16 subsets) and with filtered backprojection (FBP), and K1, k2 and k3 images were created by the Marquardt non-linear least squares method based on the 3-parameter kinetic model. Although the OS-EM activity images correlated fairly well with those obtained by FBP, the pixel correlations were poor for the k2 and k3 parametric images, but the plots were scattered along the line of identity and the mean values for K1, k2 and k3 obtained by OS-EM were almost equal to those by FBP. The kinetic fitting error for OS-EM was no smaller than that for FBP. The results suggest that OS-EM is not necessarily superior to FBP for creating parametric images.</description><subject>Algorithms</subject><subject>Brain - diagnostic imaging</subject><subject>Digital imaging</subject><subject>Fluorodeoxyglucose F18</subject><subject>Humans</subject><subject>Image Enhancement - methods</subject><subject>Image processing</subject><subject>Image reconstruction</subject><subject>Kinetics</subject><subject>Least squares method</subject><subject>Phantoms, Imaging</subject><subject>Positron emission</subject><subject>Positron emission tomography</subject><subject>Radiopharmaceuticals</subject><subject>Studies</subject><subject>Tomography, Emission-Computed - methods</subject><issn>0914-7187</issn><issn>1864-6433</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2001</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqF0d9LHDEQB_BQKvU8fekfUEILFYTVye_ksR53VVAU1LfCks0mduV2cya7D_ffN-KBUGh9mnn4zMDMF6HPBE4JgDo7XwE1WjMuPqAZ0ZJXkjP2Ec3AEF4potU-Osj5CYBqoekntE-IEtpwOUO_FrHf2NTlOOAYcGlt78fUObw6v8V2aPHNXbW8xsm7OOQxTW7sCrXrx5i68XePu94--oxDTPh2eY_b7WD7Mp3Hqd0eor1g19kf7eocPayW94uL6urm5-Xix1XluNFjxbUyoIH6YFUTvNa8UeC4NCRIa5iRNijBWmiDpA14p7QNVphgKbjGQsPm6Ph17ybF58nnse677Px6bQcfp1wb4FwqUr4yR9__KxVljBGl3oWUEColMQV--ws-xSkN5dyaKq0oF5K-qK__VESAAC1IQSevyKWYc_Kh3qTy3rStCdQvSddvSRf8ZbdxanrfvtFdtOwP0KOguQ</recordid><startdate>20011001</startdate><enddate>20011001</enddate><creator>Oda, K</creator><creator>Toyama, H</creator><creator>Uemura, K</creator><creator>Ikoma, Y</creator><creator>Kimura, Y</creator><creator>Senda, M</creator><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QP</scope><scope>7RV</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M7P</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>20011001</creationdate><title>Comparison of parametric FBP and OS-EM reconstruction algorithm images for PET dynamic study</title><author>Oda, K ; Toyama, H ; Uemura, K ; Ikoma, Y ; Kimura, Y ; Senda, M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c498t-48790802efa7bfe884b70c4691f6a9396af753d0df62b0ec78afa59fa20cba0b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2001</creationdate><topic>Algorithms</topic><topic>Brain - diagnostic imaging</topic><topic>Digital imaging</topic><topic>Fluorodeoxyglucose F18</topic><topic>Humans</topic><topic>Image Enhancement - methods</topic><topic>Image processing</topic><topic>Image reconstruction</topic><topic>Kinetics</topic><topic>Least squares method</topic><topic>Phantoms, Imaging</topic><topic>Positron emission</topic><topic>Positron emission tomography</topic><topic>Radiopharmaceuticals</topic><topic>Studies</topic><topic>Tomography, Emission-Computed - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Oda, K</creatorcontrib><creatorcontrib>Toyama, H</creatorcontrib><creatorcontrib>Uemura, K</creatorcontrib><creatorcontrib>Ikoma, Y</creatorcontrib><creatorcontrib>Kimura, Y</creatorcontrib><creatorcontrib>Senda, M</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Neurosciences Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Biological Science Database</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</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>Annals of nuclear medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Oda, K</au><au>Toyama, H</au><au>Uemura, K</au><au>Ikoma, Y</au><au>Kimura, Y</au><au>Senda, M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparison of parametric FBP and OS-EM reconstruction algorithm images for PET dynamic study</atitle><jtitle>Annals of nuclear medicine</jtitle><addtitle>Ann Nucl Med</addtitle><date>2001-10-01</date><risdate>2001</risdate><volume>15</volume><issue>5</issue><spage>417</spage><epage>423</epage><pages>417-423</pages><issn>0914-7187</issn><eissn>1864-6433</eissn><abstract>An ordered subsets expectation maximization (OS-EM) algorithm is used for image reconstruction to suppress image noise and to make non-negative value images. We have applied OS-EM to a digital brain phantom and to human brain 18F-FDG PET kinetic studies to generate parametric images. A 45 min dynamic scan was performed starting injection of FDG with a 2D PET scanner. The images were reconstructed with OS-EM (6 iterations, 16 subsets) and with filtered backprojection (FBP), and K1, k2 and k3 images were created by the Marquardt non-linear least squares method based on the 3-parameter kinetic model. Although the OS-EM activity images correlated fairly well with those obtained by FBP, the pixel correlations were poor for the k2 and k3 parametric images, but the plots were scattered along the line of identity and the mean values for K1, k2 and k3 obtained by OS-EM were almost equal to those by FBP. The kinetic fitting error for OS-EM was no smaller than that for FBP. The results suggest that OS-EM is not necessarily superior to FBP for creating parametric images.</abstract><cop>Japan</cop><pub>Springer Nature B.V</pub><pmid>11758946</pmid><doi>10.1007/BF02988345</doi><tpages>7</tpages></addata></record> |
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| ispartof | Annals of nuclear medicine, 2001-10, Vol.15 (5), p.417-423 |
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| source | MEDLINE; Springer Nature - Complete Springer Journals; Alma/SFX Local Collection |
| subjects | Algorithms Brain - diagnostic imaging Digital imaging Fluorodeoxyglucose F18 Humans Image Enhancement - methods Image processing Image reconstruction Kinetics Least squares method Phantoms, Imaging Positron emission Positron emission tomography Radiopharmaceuticals Studies Tomography, Emission-Computed - methods |
| title | Comparison of parametric FBP and OS-EM reconstruction algorithm images for PET dynamic study |
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