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By using kinetic modeling, quantitative CBF and CMR\n                      <jats:sub>glu<\/jats:sub>\n                      values can be obtained. However, hardware limitations led to the development of semiquantitive calculation schemes which are still widely used. In this paper, the analysis of CMR\n                      <jats:sub>glu<\/jats:sub>\n                      and CBF scans, acquired on a current state-of-the-art PET brain scanner, is presented. In particular, the correspondence between nonlinear as well as linearized methods for the determination of CBF and CMR\n                      <jats:sub>glu<\/jats:sub>\n                      is investigated. As a further step towards widespread clinical applicability, the use of an image-derived input function (IDIF) is investigated.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Thirteen healthy male volunteers were included in this study. Each subject had one scanning session in the fasting state, consisting of a dynamic [\n                      <jats:sup>15<\/jats:sup>\n                      O]H\n                      <jats:sub>2<\/jats:sub>\n                      O scan and a dynamic [\n                      <jats:sup>18<\/jats:sup>\n                      \u2009F]FDG PET scan, acquired at a high-resolution research tomograph. Time-activity curves (TACs) were generated for automatically delineated and for manually drawn gray matter (GM) and white matter regions. Input functions were derived using on-line arterial blood sampling (blood sampler derived input function (BSIF)). Additionally, the possibility of using carotid artery IDIFs was investigated. Data were analyzed using nonlinear regression (NLR) of regional TACs and parametric methods.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      After quality control, 9 CMR\n                      <jats:sub>glu<\/jats:sub>\n                      and 11 CBF scans were available for analysis. Average GM CMR\n                      <jats:sub>glu<\/jats:sub>\n                      values were 0.33\u2009\u00b1\u20090.04 \u03bcmol\/cm\n                      <jats:sup>3<\/jats:sup>\n                      per minute, and average CBF values were 0.43\u2009\u00b1\u20090.09 mL\/cm\n                      <jats:sup>3<\/jats:sup>\n                      per minute. Good correlation between NLR and parametric CMR\n                      <jats:sub>glu<\/jats:sub>\n                      measurements was obtained as well as between NLR and parametric CBF values. For CMR\n                      <jats:sub>glu<\/jats:sub>\n                      Patlak linearization, BSIF and IDIF derived results were similar. The use of an IDIF, however, did not provide reliable CBF estimates.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      Nonlinear regression analysis, allowing for the derivation of regional CBF and CMR\n                      <jats:sub>glu<\/jats:sub>\n                      values, can be applied to data acquired with high-spatial resolution current state-of-the-art PET brain scanners. Linearized models, applied to the voxel level, resulted in comparable values. CMR\n                      <jats:sub>glu<\/jats:sub>\n                      measurements do not require invasive arterial sampling to define the input function.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Trial registration<\/jats:title>\n                    <jats:p>ClinicalTrials.gov NCT00626080<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/2191-219x-2-63","type":"journal-article","created":{"date-parts":[[2012,11,20]],"date-time":"2012-11-20T18:14:12Z","timestamp":1353435252000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["Cerebral blood flow and glucose metabolism in healthy volunteers measured using a high-resolution PET scanner"],"prefix":"10.1186","volume":"2","author":[{"given":"Marc C","family":"Huisman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Larissa W","family":"van Golen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikie J","family":"Hoetjes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henri N","family":"Greuter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick","family":"Schober","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard G","family":"Ijzerman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michaela","family":"Diamant","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adriaan A","family":"Lammertsma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2012,11,20]]},"reference":[{"key":"97_CR1","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1038\/jcbfm.1987.39","volume":"7","author":"AA Lammertsma","year":"1987","unstructured":"Lammertsma AA, Brooks DJ, Frackowiak RS, Beaney RP, Herold S, Heather JD, Palmer AJ, Jones T: Measurement of glucose utilization with [18 F]2-fluoro-2-deoxy-D-glucose: a comparison of different analytical methods. 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