{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T07:31:00Z","timestamp":1777793460191,"version":"3.51.4"},"reference-count":32,"publisher":"SAGE Publications","issue":"7","license":[{"start":{"date-parts":[[2013,3,20]],"date-time":"2013-03-20T00:00:00Z","timestamp":1363737600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Cereb Blood Flow Metab"],"published-print":{"date-parts":[[2013,7]]},"abstract":"<jats:p>\n                    This study compared model-based and data-driven methods to assess the best methodology for generating precise and accurate parametric maps of the parameters of interest in [\n                    <jats:sup>11<\/jats:sup>\n                    C]( R)-rolipram brain positron-emission tomography studies. Parametric images were generated using (1) a two-tissue compartmental model (2TCM) solved with the hierarchical basis function method (H-BFM) linear estimator;(2) data-driven spectral-based methods: standard spectral analysis (std SA) and rank-shaping SA (RS);and (3) the Logan graphical plot. Nonphysiologic V\n                    <jats:sub>T<\/jats:sub>\n                    estimates were eliminated and the remaining ones were compared with the reference values, i.e., those obtained with a voxelwise 2TCM solved with a nonlinear estimator. With regard to voxelwise V\n                    <jats:sub>T<\/jats:sub>\n                    estimates, H-BFM showed the best agreement with weighted nonlinear least square (WNLLS) values and the lowest percentage of mean relative difference (1 \u00b1 1%). All methods showed comparable variability in the relative differences. H-BFM provided the best correlation with WNLLS (y = 1.034x \u2212 0.013; R\n                    <jats:sup>2<\/jats:sup>\n                    = 0.973). Despite a slight bias, the other three methods also showed good agreement and high correlation ( R\n                    <jats:sup>2<\/jats:sup>\n                    &gt; 0.96). H-BFM yielded the most reliable voxelwise quantification of [\n                    <jats:sup>11<\/jats:sup>\n                    C]( R)-rolipram as well as the complete description of the tracer kinetic. The Logan plot represents a valid alternative if only V\n                    <jats:sub>T<\/jats:sub>\n                    estimation is required. Its marginally higher bias was outweighed by a low computational time, ease of implementation, and robustness.\n                  <\/jats:p>","DOI":"10.1038\/jcbfm.2013.43","type":"journal-article","created":{"date-parts":[[2013,3,20]],"date-time":"2013-03-20T04:58:45Z","timestamp":1363755525000},"page":"1032-1040","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":12,"title":["Voxelwise Quantification of [\n                    <sup>11<\/sup>\n                    C](\n                    <i>R<\/i>\n                    )-Rolipram PET Data: A Comparison Between Model-Based and Data-Driven Methods"],"prefix":"10.1177","volume":"33","author":[{"given":"Gaia","family":"Rizzo","sequence":"first","affiliation":[{"name":"Department of Information Engineering, University of Padova, Padova, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mattia","family":"Veronese","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Padova, Padova, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paolo","family":"Zanotti-Fregonara","sequence":"additional","affiliation":[{"name":"Molecular Imaging Branch, National Institute of Mental Health, Bethesda, Maryland, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alessandra","family":"Bertoldo","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Padova, Padova, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2013,3,20]]},"reference":[{"key":"bibr1-jcbfm.2013.43","doi-asserted-by":"publisher","DOI":"10.1016\/0006-8993(94)91552-0"},{"key":"bibr2-jcbfm.2013.43","doi-asserted-by":"publisher","DOI":"10.1176\/appi.ajp.159.1.66"},{"key":"bibr3-jcbfm.2013.43","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-0327(99)00048-8"},{"key":"bibr4-jcbfm.2013.43","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(05)79827-5"},{"key":"bibr5-jcbfm.2013.43","doi-asserted-by":"publisher","DOI":"10.1016\/S0006-3223(99)00177-8"},{"key":"bibr6-jcbfm.2013.43","doi-asserted-by":"publisher","DOI":"10.1016\/j.biopsych.2012.04.030"},{"key":"bibr7-jcbfm.2013.43","doi-asserted-by":"publisher","DOI":"10.1006\/nimg.1996.0066"},{"key":"bibr8-jcbfm.2013.43","doi-asserted-by":"publisher","DOI":"10.1097\/01.WCB.0000085441.37552.CA"},{"key":"bibr9-jcbfm.2013.43","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.10.064"},{"key":"bibr10-jcbfm.2013.43","first-page":"2083","volume":"41","author":"Slifstein 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