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Therefore, we introduce an open-source analysis pipeline (TurBO, Turku total-BOdy) for automated preprocessing and kinetic modelling of LAFOV [\n                      <jats:sup>15<\/jats:sup>\n                      O]H\n                      <jats:sub>2<\/jats:sub>\n                      O and [\n                      <jats:sup>18<\/jats:sup>\n                      F]FDG PET data. TurBO enables efficient, reproducible quantification of tissue perfusion and metabolism at regional- and voxel-levels through automated co-registration, motion correction, CT-based region of interest (ROI) segmentation, image-derived input function (IDIF) extraction, and region-specific kinetic modelling.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      The pipeline was validated with Biograph Vision Quadra (Siemens Healthineers) LAFOV PET\/CT data from 21 subjects scanned with [\n                      <jats:sup>15<\/jats:sup>\n                      O]H\n                      <jats:sub>2<\/jats:sub>\n                      O and 16 subjects scanned with [\n                      <jats:sup>18<\/jats:sup>\n                      F]FDG. Six CT-segmented ROIs (cortical brain gray matter, left iliopsoas muscle, right kidney cortex and medulla, pancreas, spleen and liver) were used to assess different levels of tissue perfusion and glucose metabolism.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Model fits showed high quality with consistent estimates at regional and voxel-levels (R\n                      <jats:sup>2<\/jats:sup>\n                      \u2009&gt;\u20090.83 for [\n                      <jats:sup>15<\/jats:sup>\n                      O]H\n                      <jats:sub>2<\/jats:sub>\n                      O, R\n                      <jats:sup>2<\/jats:sup>\n                      \u2009&gt;\u20090.99 for [\n                      <jats:sup>18<\/jats:sup>\n                      F]FDG). Manual and automated IDIFs were in concordance (R\n                      <jats:sup>2<\/jats:sup>\n                      \u2009&gt;\u20090.74 for [\n                      <jats:sup>15<\/jats:sup>\n                      O]H\n                      <jats:sub>2<\/jats:sub>\n                      O, and R\n                      <jats:sup>2<\/jats:sup>\n                      \u2009&gt;\u20090.78 for [\n                      <jats:sup>18<\/jats:sup>\n                      F]FDG) with minimal bias (&lt;\u20094% and\u2009&lt;\u200910%, respectively). Manual and CT-segmented ROIs showed strong agreement (R\n                      <jats:sup>2<\/jats:sup>\n                      \u2009&gt;\u20090.82 for [\n                      <jats:sup>15<\/jats:sup>\n                      O]H\n                      <jats:sub>2<\/jats:sub>\n                      O and R\n                      <jats:sup>2<\/jats:sup>\n                      \u2009&gt;\u20090.83 for [\n                      <jats:sup>18<\/jats:sup>\n                      F]FDG). Motion correction had little impact on estimates (R\n                      <jats:sup>2<\/jats:sup>\n                      \u2009&gt;\u20090.71 for [\n                      <jats:sup>15<\/jats:sup>\n                      O]H\n                      <jats:sub>2<\/jats:sub>\n                      O and R\n                      <jats:sup>2<\/jats:sup>\n                      \u2009&gt;\u20090.78 for [\n                      <jats:sup>18<\/jats:sup>\n                      F]FDG) compared with uncorrected data.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The TurBO pipeline provides fully automated and reliable quantification for LAFOV PET data. It substantially reduces manual workload and enables standardized, reproducible assessment of inter-organ perfusion and metabolism.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00259-026-07769-7","type":"journal-article","created":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T00:26:07Z","timestamp":1769819167000},"page":"4162-4174","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Automated long axial field of view PET image processing and kinetic modelling with the TurBO toolbox"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0965-9365","authenticated-orcid":false,"given":"Jouni","family":"Tuisku","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Santeri","family":"Palonen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henri","family":"K\u00e4rpijoki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aino","family":"Latva-Rasku","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nelli","family":"Tuomola","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harri","family":"Harju","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sergey V.","family":"Nesterov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vesa","family":"Oikonen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hidehiro","family":"Iida","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jarmo","family":"Teuho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunlei","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomi","family":"Karjalainen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna K.","family":"Kirjavainen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johan","family":"Rajader","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Riku","family":"Kl\u00e9n","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pirjo","family":"Nuutila","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juhani","family":"Knuuti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lauri","family":"Nummenmaa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,31]]},"reference":[{"key":"7769_CR1","doi-asserted-by":"publisher","first-page":"11S","DOI":"10.2967\/jnumed.122.264870","volume":"64","author":"J Knuuti","year":"2023","unstructured":"Knuuti J, Tuisku J, K\u00e4rpijoki H, Iida H, Maaniitty T, Latva-Rasku A, et al. 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