{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T10:43:37Z","timestamp":1781347417283,"version":"3.54.1"},"reference-count":40,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:00:00Z","timestamp":1768521600000},"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":[[2026,5]]},"abstract":"<jats:p>\n                    PET absolute quantification requires the metabolite-corrected plasma input function (\n                    <jats:italic toggle=\"yes\">C<\/jats:italic>\n                    <jats:sub>P<\/jats:sub>\n                    ), especially when an ideal reference region is unavailable. We propose \u201cpseudo-Reference-based Input Function Shape\u201d (pRef-IFS), a novel non-invasive method to estimate\n                    <jats:italic toggle=\"yes\">C<\/jats:italic>\n                    <jats:sub>P<\/jats:sub>\n                    from PET data in two steps: (1) generating the unscaled\n                    <jats:italic toggle=\"yes\">C<\/jats:italic>\n                    <jats:sub>P<\/jats:sub>\n                    <jats:italic toggle=\"yes\">shape<\/jats:italic>\n                    from a pseudo-reference region (pRef) described by a one-tissue (1T) compartment model, with the pRef clearance rate\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                        <mml:mrow>\n                          <mml:mrow>\n                            <mml:msubsup>\n                              <mml:mi>k<\/mml:mi>\n                              <mml:mn>2<\/mml:mn>\n                              <mml:mo>\u2032<\/mml:mo>\n                            <\/mml:msubsup>\n                          <\/mml:mrow>\n                        <\/mml:mrow>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    either estimated with simplified reference tissue models (SRTM) or assumed\n                    <jats:italic toggle=\"yes\">a priori<\/jats:italic>\n                    ; (2)\n                    <jats:italic toggle=\"yes\">scaling C<\/jats:italic>\n                    <jats:sub>P<\/jats:sub>\n                    with an early image-derived blood time-activity curve (ID-BTAC). We here explored the first step (assuming accurate step-2 scaling), evaluating the impact of violating 1T assumptions and inaccurate\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                        <mml:mrow>\n                          <mml:mrow>\n                            <mml:msubsup>\n                              <mml:mi>k<\/mml:mi>\n                              <mml:mn>2<\/mml:mn>\n                              <mml:mo>\u2032<\/mml:mo>\n                            <\/mml:msubsup>\n                          <\/mml:mrow>\n                        <\/mml:mrow>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    estimates on pRef-IFS recovery with both synthetic and human data. The distribution volumes (\n                    <jats:italic toggle=\"yes\">V<\/jats:italic>\n                    <jats:sub>T<\/jats:sub>\n                    ) were the main outcome measure. With 1T kinetics, SRTM\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                        <mml:mrow>\n                          <mml:mrow>\n                            <mml:msubsup>\n                              <mml:mi>k<\/mml:mi>\n                              <mml:mn>2<\/mml:mn>\n                              <mml:mo>\u2032<\/mml:mo>\n                            <\/mml:msubsup>\n                          <\/mml:mrow>\n                        <\/mml:mrow>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    was unbiased, irrespective of pRef specific binding; pRef-IFS had small\n                    <jats:italic toggle=\"yes\">V<\/jats:italic>\n                    <jats:sub>T<\/jats:sub>\n                    bias in real data:\n                    <jats:sup>11<\/jats:sup>\n                    C-LSN3172176 (cerebellum pRef: \u22122\u2009\u00b1\u20095%; thalamus: 11\u00b18%);\n                    <jats:sup>18<\/jats:sup>\n                    F-SynVesT-1 (centrum semiovale: \u22121\u2009\u00b1\u200914%; cerebellum: 6\u2009\u00b1\u20094%). With two-tissue kinetics, pRef-IFS performed poorly, especially with\n                    <jats:sup>18<\/jats:sup>\n                    F-FPEB, while\n                    <jats:sup>18<\/jats:sup>\n                    F-ASEM produced small biases (cerebellum: \u22124\u2009\u00b1\u200910%). If good scaling with ID-BTAC can be achieved, pRef-IFS promises to be fully non-invasive for radiotracers reasonably described by 1T kinetics, even with modest specific binding in the pRef.\n                  <\/jats:p>","DOI":"10.1177\/0271678x251411712","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T12:48:48Z","timestamp":1768567728000},"page":"1238-1252","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["\u201cPseudo-reference-based Input Function Shape\u201d (pRef-IFS): Towards a True image-derived input function for PET kinetic modeling"],"prefix":"10.1177","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5451-6710","authenticated-orcid":false,"given":"Tommaso","family":"Volpi","sequence":"first","affiliation":[{"name":"Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA"},{"name":"Yale Positron Emission Tomography 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