{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T20:53:51Z","timestamp":1789419231497,"version":"build-2803163510"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T00:00:00Z","timestamp":1710288000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T00:00:00Z","timestamp":1710288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EJNMMI Phys"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate image-derived input function (IDIF) from highly sensitive large axial field of view (LAFOV) PET\/CT scanners could avoid the need of invasive blood sampling for kinetic modelling. The aim is to validate the use of IDIF for two kinds of tracers, 3 different IDIF locations and 9 different reconstruction settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Eight [\n                      <jats:sup>18<\/jats:sup>\n                      F]FDG and 10 [\n                      <jats:sup>18<\/jats:sup>\n                      F]DPA-714 scans were acquired respectively during 70 and 60\u00a0min on the Vision Quadra PET\/CT system. PET images were reconstructed using various reconstruction settings. IDIFs were taken from ascending aorta (AA), descending aorta (DA), and left ventricular cavity (LV). The calibration factor (CF) extracted from the comparison between the IDIFs and the manual blood samples as reference was used for IDIFs accuracy and precision assessment. To illustrate the effect of various calibrated-IDIFs on Patlak linearization for [\n                      <jats:sup>18<\/jats:sup>\n                      F]FDG and Logan linearization for [\n                      <jats:sup>18<\/jats:sup>\n                      F]DPA-714, the same target time-activity curves were applied for each calibrated-IDIF.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      For [\n                      <jats:sup>18<\/jats:sup>\n                      F]FDG, the accuracy and precision of the IDIFs were high (mean CF\u2009\u2265\u20090.82, SD\u2009\u2264\u20090.06). Compared to the striatum influx (\n                      <jats:italic>K<\/jats:italic>\n                      <jats:sub>\n                        <jats:italic>i<\/jats:italic>\n                      <\/jats:sub>\n                      ) extracted using calibrated AA IDIF with the updated European Association of Nuclear Medicine Research Ltd. standard reconstruction (EARL2),\n                      <jats:italic>K<\/jats:italic>\n                      <jats:sub>\n                        <jats:italic>i<\/jats:italic>\n                      <\/jats:sub>\n                      mean differences were &lt;\u20092% using the other calibrated IDIFs. For [\n                      <jats:sup>18<\/jats:sup>\n                      F]DPA714, high accuracy of the IDIFs was observed (mean CF\u2009\u2265\u20090.86) except using absolute scatter correction, DA and LV (respectively mean CF\u2009=\u20090.68, 0.47 and 0.44). However, the precision of the AA IDIFs was low (SD\u2009\u2265\u20090.10). Compared to the distribution volume (\n                      <jats:italic>V<\/jats:italic>\n                      <jats:sub>\n                        <jats:italic>T<\/jats:italic>\n                      <\/jats:sub>\n                      ) in a frontal region obtained using calibrated continuous arterial sampler input function as reference,\n                      <jats:italic>V<\/jats:italic>\n                      <jats:sub>\n                        <jats:italic>T<\/jats:italic>\n                      <\/jats:sub>\n                      mean differences were small using calibrated AA IDIFs (for example\n                      <jats:italic>V<\/jats:italic>\n                      <jats:sub>\n                        <jats:italic>T<\/jats:italic>\n                      <\/jats:sub>\n                      mean difference = -5.3% using EARL2), but higher using calibrated DA and LV IDIFs (respectively\u2009+\u200912.5% and +\u200919.1%).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      For [\n                      <jats:sup>18<\/jats:sup>\n                      F]FDG, IDIF do not need calibration against manual blood samples. For [\n                      <jats:sup>18<\/jats:sup>\n                      F]DPA-714, AA IDIF can replace continuous arterial sampling for simplified kinetic quantification but only with calibration against arterial blood samples. The accuracy and precision of IDIF from LAFOV PET\/CT system depend on tracer, reconstruction settings and IDIF VOI locations, warranting careful optimization.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40658-024-00628-0","type":"journal-article","created":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T23:01:55Z","timestamp":1710284515000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Validation of image-derived input function using a long axial field of view PET\/CT scanner for two different tracers"],"prefix":"10.1186","volume":"11","author":[{"given":"Xavier","family":"Palard-Novello","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Denise","family":"Visser","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nelleke","family":"Tolboom","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charlotte L. 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Competing interests: Frederik Barkhof is a steering committee or data safety monitoring board member for Biogen, Merck, ATRI\/ACTC and Prothena, is a consultant for Roche, Celltrion, Rewind Therapeutics, Merck, IXICO, Jansen, Combinostics, has research agreements with Merck, Biogen, GE Healthcare, Roche and is the co-founder and shareholder of Queen Square Analytics LTD. No other potential conflict of interest relevant to this article was reported.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"25"}}