{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T15:33:15Z","timestamp":1787499195320,"version":"build-2736575974"},"reference-count":18,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T00:00:00Z","timestamp":1617753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:p>\n                    Recently, we developed a three-compartment dual-output model that incorporates spillover (SP) and partial volume (PV) corrections to simultaneously estimate the kinetic parameters and model-corrected blood input function (MCIF) from dynamic 2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography (FDG PET) images of mouse heart\n                    <jats:italic>in vivo<\/jats:italic>\n                    . In this study, we further optimized this model and utilized the estimated MCIF to compute cerebral FDG uptake rates,\n                    <jats:italic>K<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>i<\/jats:italic>\n                    <\/jats:sub>\n                    , from dynamic total-body FDG PET images of control Wistar\u2013Kyoto (WKY) rats and compared to those derived from arterial blood sampling\n                    <jats:italic>in vivo<\/jats:italic>\n                    . Dynamic FDG PET scans of WKY rats (\n                    <jats:italic>n<\/jats:italic>\n                    = 5), fasted for 6 h, were performed using the Albira Si Trimodal PET\/SPECT\/CT imager for 60 min. Arterial blood samples were collected for the entire imaging duration and then fitted to a seven-parameter function. The 60-min list mode PET data, corrected for attenuation, scatter, randoms, and decay, were reconstructed into 23 time bins. A 15-parameter dual-output model with SP and PV corrections was optimized with two cost functions to compute MCIF. A four-parameter compartment model was then used to compute cerebral Ki. The computed area under the curve (AUC) and\n                    <jats:italic>K<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>i<\/jats:italic>\n                    <\/jats:sub>\n                    were compared to that derived from arterial blood samples. Experimental and computed AUCs were 1,893.53 \u00b1 195.39 kBq min\/cc and 1,792.65 \u00b1 155.84 kBq min\/cc, respectively (\n                    <jats:italic>p<\/jats:italic>\n                    = 0.76). Bland\u2013Altman analysis of experimental vs. computed\n                    <jats:italic>K<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>i<\/jats:italic>\n                    <\/jats:sub>\n                    for 35 cerebral regions in WKY rats revealed a mean difference of 0.0029 min\n                    <jats:sup>\u22121<\/jats:sup>\n                    (~13.5%). Direct (AUC) and indirect (Ki) comparisons of model computations with arterial blood sampling were performed in WKY rats. AUC and the downstream cerebral FDG uptake rates compared well with that obtained using arterial blood samples. Experimental vs. computed cerebral\n                    <jats:italic>K<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>i<\/jats:italic>\n                    <\/jats:sub>\n                    for the four super regions including cerebellum, frontal cortex, hippocampus, and striatum indicated no significant differences.\n                  <\/jats:p>","DOI":"10.3389\/fmed.2021.618645","type":"journal-article","created":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T00:30:56Z","timestamp":1617755456000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Model Corrected Blood Input Function to Compute Cerebral FDG Uptake Rates From Dynamic Total-Body PET Images of Rats in vivo"],"prefix":"10.3389","volume":"8","author":[{"given":"James C.","family":"Massey","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vikram","family":"Seshadri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soumen","family":"Paul","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krzysztof","family":"Mi\u0144czuk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cesar","family":"Molinos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bijoy 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