Multi-atlas attenuation correction supports full quantification of static and dynamic brain PET data in PET-MR
In simultaneous PET-MR, attenuation maps are not directly available. Essential for absolute radioactivity quantification, they need to be derived from MR or PET data to correct for gamma photon attenuation by the imaged object. We evaluate a multi-atlas attenuation correction method for brain imagin...
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description | In simultaneous PET-MR, attenuation maps are not directly available. Essential for absolute radioactivity quantification, they need to be derived from MR or PET data to correct for gamma photon attenuation by the imaged object. We evaluate a multi-atlas attenuation correction method for brain imaging (MaxProb) on static [18F]FDG PET and, for the first time, on dynamic PET, using the serotoninergic tracer [18F]MPPF. A database of 40 MR/CT image pairs (atlases) was used. The MaxProb method synthesises subject-specific pseudo-CTs by registering each atlas to the target subject space. Atlas CT intensities are then fused via label propagation and majority voting. Here, we compared these pseudo-CTs with the real CTs in a leave-one-out design, contrasting the MaxProb approach with a simplified single-atlas method (SingleAtlas). We evaluated the impact of pseudo-CT accuracy on reconstructed PET images, compared to PET data reconstructed with real CT, at the regional and voxel levels for the following: radioactivity images; time-activity curves; and kinetic parameters (non-displaceable binding potential, BPND). On static [18F]FDG, the mean bias for MaxProb ranged between 0 and 1% for 73 out of 84 regions assessed, and exceptionally peaked at 2.5% for only one region. Statistical parametric map analysis of MaxProb-corrected PET data showed significant differences in less than 0.02% of the brain volume, whereas SingleAtlas-corrected data showed significant differences in 20% of the brain volume. On dynamic [18F]MPPF, most regional errors on BPND ranged from -1 to +3% (maximum bias 5%) for the MaxProb method. With SingleAtlas, errors were larger and had higher variability in most regions. PET quantification bias increased over the duration of the dynamic scan for SingleAtlas, but not for MaxProb. We show that this effect is due to the interaction of the spatial tracer-distribution heterogeneity variation over time with the degree of accuracy of the attenuation maps. This work demonstrates that inaccuracies in attenuation maps can induce bias in dynamic brain PET studies. Multi-atlas attenuation correction with MaxProb enables quantification on hybrid PET-MR scanners, eschewing the need for CT. |
doi_str_mv | 10.1088/1361-6560/aa5f6c |
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Essential for absolute radioactivity quantification, they need to be derived from MR or PET data to correct for gamma photon attenuation by the imaged object. We evaluate a multi-atlas attenuation correction method for brain imaging (MaxProb) on static [18F]FDG PET and, for the first time, on dynamic PET, using the serotoninergic tracer [18F]MPPF. A database of 40 MR/CT image pairs (atlases) was used. The MaxProb method synthesises subject-specific pseudo-CTs by registering each atlas to the target subject space. Atlas CT intensities are then fused via label propagation and majority voting. Here, we compared these pseudo-CTs with the real CTs in a leave-one-out design, contrasting the MaxProb approach with a simplified single-atlas method (SingleAtlas). We evaluated the impact of pseudo-CT accuracy on reconstructed PET images, compared to PET data reconstructed with real CT, at the regional and voxel levels for the following: radioactivity images; time-activity curves; and kinetic parameters (non-displaceable binding potential, BPND). On static [18F]FDG, the mean bias for MaxProb ranged between 0 and 1% for 73 out of 84 regions assessed, and exceptionally peaked at 2.5% for only one region. Statistical parametric map analysis of MaxProb-corrected PET data showed significant differences in less than 0.02% of the brain volume, whereas SingleAtlas-corrected data showed significant differences in 20% of the brain volume. On dynamic [18F]MPPF, most regional errors on BPND ranged from -1 to +3% (maximum bias 5%) for the MaxProb method. With SingleAtlas, errors were larger and had higher variability in most regions. PET quantification bias increased over the duration of the dynamic scan for SingleAtlas, but not for MaxProb. We show that this effect is due to the interaction of the spatial tracer-distribution heterogeneity variation over time with the degree of accuracy of the attenuation maps. This work demonstrates that inaccuracies in attenuation maps can induce bias in dynamic brain PET studies. Multi-atlas attenuation correction with MaxProb enables quantification on hybrid PET-MR scanners, eschewing the need for CT.</description><identifier>ISSN: 0031-9155</identifier><identifier>ISSN: 1361-6560</identifier><identifier>EISSN: 1361-6560</identifier><identifier>DOI: 10.1088/1361-6560/aa5f6c</identifier><identifier>PMID: 28181479</identifier><identifier>CODEN: PHMBA7</identifier><language>eng</language><publisher>England: IOP Publishing</publisher><subject>Adolescent ; Adult ; Algorithms ; attenuation map ; Brain - diagnostic imaging ; Brain - physiology ; Brain Mapping - methods ; Female ; Fluorodeoxyglucose F18 - metabolism ; Humans ; Image Processing, Computer-Assisted - methods ; kinetic modelling ; Kinetics ; magnetic resonance imaging ; Magnetic Resonance Imaging - methods ; Male ; Middle Aged ; Multimodal Imaging ; Neuroimaging - methods ; Neurosciences ; Neurovetenskaper ; positron emission tomography ; Positron-Emission Tomography - methods ; pseudo-CT ; Radiologi och bildbehandling ; Radiology, Nuclear Medicine and Medical Imaging ; Tomography, X-Ray Computed - methods ; Young Adult</subject><ispartof>Physics in medicine & biology, 2017-04, Vol.62 (7), p.2834-2858</ispartof><rights>2017 Institute of Physics and Engineering in Medicine</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c512t-5cbdbc71eb7886bd54366ffbf2f73f140054ac26e43e75a5a06d2570e65674b53</citedby><cites>FETCH-LOGICAL-c512t-5cbdbc71eb7886bd54366ffbf2f73f140054ac26e43e75a5a06d2570e65674b53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://iopscience.iop.org/article/10.1088/1361-6560/aa5f6c/pdf$$EPDF$$P50$$Giop$$Hfree_for_read</linktopdf><link.rule.ids>230,314,780,784,885,27923,27924,53845,53892</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28181479$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://gup.ub.gu.se/publication/252755$$DView record from Swedish Publication Index$$Hfree_for_read</backlink></links><search><creatorcontrib>Mérida, Inés</creatorcontrib><creatorcontrib>Reilhac, Anthonin</creatorcontrib><creatorcontrib>Redouté, Jérôme</creatorcontrib><creatorcontrib>Heckemann, Rolf A</creatorcontrib><creatorcontrib>Costes, Nicolas</creatorcontrib><creatorcontrib>Hammers, Alexander</creatorcontrib><creatorcontrib>Institutionen för neurovetenskap och fysiologi, sektionen för klinisk neurovetenskap</creatorcontrib><creatorcontrib>Sahlgrenska akademin</creatorcontrib><creatorcontrib>Institute of Neuroscience and Physiology, Department of Clinical Neuroscience</creatorcontrib><creatorcontrib>Göteborgs universitet</creatorcontrib><creatorcontrib>Gothenburg University</creatorcontrib><creatorcontrib>Sahlgrenska Academy</creatorcontrib><title>Multi-atlas attenuation correction supports full quantification of static and dynamic brain PET data in PET-MR</title><title>Physics in medicine & biology</title><addtitle>PMB</addtitle><addtitle>Phys. Med. Biol</addtitle><description>In simultaneous PET-MR, attenuation maps are not directly available. Essential for absolute radioactivity quantification, they need to be derived from MR or PET data to correct for gamma photon attenuation by the imaged object. We evaluate a multi-atlas attenuation correction method for brain imaging (MaxProb) on static [18F]FDG PET and, for the first time, on dynamic PET, using the serotoninergic tracer [18F]MPPF. A database of 40 MR/CT image pairs (atlases) was used. The MaxProb method synthesises subject-specific pseudo-CTs by registering each atlas to the target subject space. Atlas CT intensities are then fused via label propagation and majority voting. Here, we compared these pseudo-CTs with the real CTs in a leave-one-out design, contrasting the MaxProb approach with a simplified single-atlas method (SingleAtlas). We evaluated the impact of pseudo-CT accuracy on reconstructed PET images, compared to PET data reconstructed with real CT, at the regional and voxel levels for the following: radioactivity images; time-activity curves; and kinetic parameters (non-displaceable binding potential, BPND). On static [18F]FDG, the mean bias for MaxProb ranged between 0 and 1% for 73 out of 84 regions assessed, and exceptionally peaked at 2.5% for only one region. Statistical parametric map analysis of MaxProb-corrected PET data showed significant differences in less than 0.02% of the brain volume, whereas SingleAtlas-corrected data showed significant differences in 20% of the brain volume. On dynamic [18F]MPPF, most regional errors on BPND ranged from -1 to +3% (maximum bias 5%) for the MaxProb method. With SingleAtlas, errors were larger and had higher variability in most regions. PET quantification bias increased over the duration of the dynamic scan for SingleAtlas, but not for MaxProb. We show that this effect is due to the interaction of the spatial tracer-distribution heterogeneity variation over time with the degree of accuracy of the attenuation maps. This work demonstrates that inaccuracies in attenuation maps can induce bias in dynamic brain PET studies. Multi-atlas attenuation correction with MaxProb enables quantification on hybrid PET-MR scanners, eschewing the need for CT.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Algorithms</subject><subject>attenuation map</subject><subject>Brain - diagnostic imaging</subject><subject>Brain - physiology</subject><subject>Brain Mapping - methods</subject><subject>Female</subject><subject>Fluorodeoxyglucose F18 - metabolism</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>kinetic modelling</subject><subject>Kinetics</subject><subject>magnetic resonance imaging</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Multimodal Imaging</subject><subject>Neuroimaging - methods</subject><subject>Neurosciences</subject><subject>Neurovetenskaper</subject><subject>positron emission tomography</subject><subject>Positron-Emission Tomography - methods</subject><subject>pseudo-CT</subject><subject>Radiologi och bildbehandling</subject><subject>Radiology, Nuclear Medicine and Medical Imaging</subject><subject>Tomography, X-Ray Computed - methods</subject><subject>Young Adult</subject><issn>0031-9155</issn><issn>1361-6560</issn><issn>1361-6560</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>O3W</sourceid><sourceid>EIF</sourceid><recordid>eNp1kU1rFTEUhoMo9lrdu5JsBBcdm2QmH3cppVahRZG6DieZpEyZmaT5QPrvzXXqXekqL4fnvCF5EHpLyUdKlDqnvaCd4IKcA3Av7DO0O46eox0hPe32lPMT9Crne0IoVWx4iU6YoooOcr9D602dy9RBmSFjKMWtFcoUVmxDSs7-ibnGGFLJ2Nd5xg8V1jL5yW5c8DiXFi2GdcTj4wpLyybBtOLvl7d4hAJ4y93Nj9fohYc5uzdP5yn6-fny9uJLd_3t6uvFp-vOcspKx60ZjZXUGamUMCMfeiG8N5552Xs6EMIHsEy4oXeSAwciRsYlce3hcjC8P0Xd1pt_uViNjmlaID3qAJO-q1G30V3V2WnGmeQH_sPGxxQeqstFL1O2bp5hdaFmTZUQQim1Jw0lG2pTyDk5fyynRB-s6IMCfVCgNytt5d1TezWLG48LfzU04P0GTCHq-1DT2j5Hx8VowbRsXD_oOPrGnf2D---9vwEP8KSa</recordid><startdate>20170407</startdate><enddate>20170407</enddate><creator>Mérida, Inés</creator><creator>Reilhac, Anthonin</creator><creator>Redouté, Jérôme</creator><creator>Heckemann, Rolf A</creator><creator>Costes, Nicolas</creator><creator>Hammers, Alexander</creator><general>IOP Publishing</general><scope>O3W</scope><scope>TSCCA</scope><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>7X8</scope><scope>ADTPV</scope><scope>AOWAS</scope><scope>F1U</scope></search><sort><creationdate>20170407</creationdate><title>Multi-atlas attenuation correction supports full quantification of static and dynamic brain PET data in PET-MR</title><author>Mérida, Inés ; 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Med. Biol</addtitle><date>2017-04-07</date><risdate>2017</risdate><volume>62</volume><issue>7</issue><spage>2834</spage><epage>2858</epage><pages>2834-2858</pages><issn>0031-9155</issn><issn>1361-6560</issn><eissn>1361-6560</eissn><coden>PHMBA7</coden><abstract>In simultaneous PET-MR, attenuation maps are not directly available. Essential for absolute radioactivity quantification, they need to be derived from MR or PET data to correct for gamma photon attenuation by the imaged object. We evaluate a multi-atlas attenuation correction method for brain imaging (MaxProb) on static [18F]FDG PET and, for the first time, on dynamic PET, using the serotoninergic tracer [18F]MPPF. A database of 40 MR/CT image pairs (atlases) was used. The MaxProb method synthesises subject-specific pseudo-CTs by registering each atlas to the target subject space. Atlas CT intensities are then fused via label propagation and majority voting. Here, we compared these pseudo-CTs with the real CTs in a leave-one-out design, contrasting the MaxProb approach with a simplified single-atlas method (SingleAtlas). We evaluated the impact of pseudo-CT accuracy on reconstructed PET images, compared to PET data reconstructed with real CT, at the regional and voxel levels for the following: radioactivity images; time-activity curves; and kinetic parameters (non-displaceable binding potential, BPND). On static [18F]FDG, the mean bias for MaxProb ranged between 0 and 1% for 73 out of 84 regions assessed, and exceptionally peaked at 2.5% for only one region. Statistical parametric map analysis of MaxProb-corrected PET data showed significant differences in less than 0.02% of the brain volume, whereas SingleAtlas-corrected data showed significant differences in 20% of the brain volume. On dynamic [18F]MPPF, most regional errors on BPND ranged from -1 to +3% (maximum bias 5%) for the MaxProb method. With SingleAtlas, errors were larger and had higher variability in most regions. PET quantification bias increased over the duration of the dynamic scan for SingleAtlas, but not for MaxProb. We show that this effect is due to the interaction of the spatial tracer-distribution heterogeneity variation over time with the degree of accuracy of the attenuation maps. This work demonstrates that inaccuracies in attenuation maps can induce bias in dynamic brain PET studies. Multi-atlas attenuation correction with MaxProb enables quantification on hybrid PET-MR scanners, eschewing the need for CT.</abstract><cop>England</cop><pub>IOP Publishing</pub><pmid>28181479</pmid><doi>10.1088/1361-6560/aa5f6c</doi><tpages>25</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Adolescent Adult Algorithms attenuation map Brain - diagnostic imaging Brain - physiology Brain Mapping - methods Female Fluorodeoxyglucose F18 - metabolism Humans Image Processing, Computer-Assisted - methods kinetic modelling Kinetics magnetic resonance imaging Magnetic Resonance Imaging - methods Male Middle Aged Multimodal Imaging Neuroimaging - methods Neurosciences Neurovetenskaper positron emission tomography Positron-Emission Tomography - methods pseudo-CT Radiologi och bildbehandling Radiology, Nuclear Medicine and Medical Imaging Tomography, X-Ray Computed - methods Young Adult |
title | Multi-atlas attenuation correction supports full quantification of static and dynamic brain PET data in PET-MR |
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