{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T15:42:22Z","timestamp":1791474142868,"version":"4.3.4"},"reference-count":54,"publisher":"SAGE Publications","issue":"9","license":[{"start":{"date-parts":[[2021,2,8]],"date-time":"2021-02-08T00:00:00Z","timestamp":1612742400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Cereb Blood Flow Metab"],"published-print":{"date-parts":[[2021,9]]},"abstract":"<jats:p>\n                    Cerebral blood flow (CBF) can be measured with dynamic positron emission tomography (PET) of\n                    <jats:sup>15<\/jats:sup>\n                    O-labeled water by using tracer kinetic modelling. However, for quantification of regional CBF, an arterial input function (AIF), obtained from arterial blood sampling, is required. In this work we evaluated a novel, non-invasive approach for input function prediction based on machine learning (MLIF), against AIF for CBF PET measurements in human subjects.\n                  <\/jats:p>\n                  <jats:p>\n                    Twenty-five subjects underwent two 10\u2009min dynamic\n                    <jats:sup>15<\/jats:sup>\n                    O-water brain PET scans with continuous arterial blood sampling, before (baseline) and following acetazolamide medication. Three different image-derived time-activity curves were automatically segmented from the carotid arteries and used as input into a Gaussian process-based AIF prediction model, considering both baseline and acetazolamide scans as training data. The MLIF approach was evaluated by comparing AIF and MLIF curves, as well as whole-brain grey matter CBF values estimated by kinetic modelling derived with either AIF or MLIF.\n                  <\/jats:p>\n                  <jats:p>\n                    The results showed that AIF and MLIF curves were similar and that corresponding CBF values were highly correlated and successfully differentiated before and after acetazolamide medication. In conclusion, our non-invasive MLIF method shows potential to replace the AIF obtained from blood sampling for CBF measurements using\n                    <jats:sup>15<\/jats:sup>\n                    O-water PET and kinetic modelling.\n                  <\/jats:p>","DOI":"10.1177\/0271678x21991393","type":"journal-article","created":{"date-parts":[[2021,2,12]],"date-time":"2021-02-12T06:14:49Z","timestamp":1613110489000},"page":"2229-2241","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":33,"title":["Cerebral blood flow measurements with\n                    <sup>15<\/sup>\n                    O-water PET using a non-invasive machine-learning-derived arterial input function"],"prefix":"10.1177","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7747-9003","authenticated-orcid":false,"given":"Samuel","family":"Kuttner","sequence":"first","affiliation":[{"name":"Department of Clinical Medicine, UiT The Arctic University of Norway, Troms\u00f8, Norway"},{"name":"Department of Physics and Technology, UiT The Arctic University of Norway, Troms\u00f8, Norway"},{"name":"The PET Imaging Center, University Hospital of North Norway, Troms\u00f8, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristoffer Knutsen","family":"Wickstr\u00f8m","sequence":"additional","affiliation":[{"name":"Department of Physics and Technology, UiT The Arctic University of Norway, Troms\u00f8, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Lubberink","sequence":"additional","affiliation":[{"name":"Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Tolf","sequence":"additional","affiliation":[{"name":"Department of Neuroscience, Uppsala University, Uppsala, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joachim","family":"Burman","sequence":"additional","affiliation":[{"name":"Department of Neuroscience, Uppsala University, Uppsala, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rune","family":"Sundset","sequence":"additional","affiliation":[{"name":"Department of Clinical Medicine, UiT The Arctic University of Norway, Troms\u00f8, Norway"},{"name":"The PET Imaging Center, University Hospital of North Norway, Troms\u00f8, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Jenssen","sequence":"additional","affiliation":[{"name":"Department of Physics and Technology, UiT The Arctic University of Norway, Troms\u00f8, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lieuwe","family":"Appel","sequence":"additional","affiliation":[{"name":"Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3731-3612","authenticated-orcid":false,"given":"Jan","family":"Axelsson","sequence":"additional","affiliation":[{"name":"Department of Radiation Sciences, Ume\u00e5 University, Ume\u00e5, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2021,2,8]]},"reference":[{"key":"bibr1-0271678X21991393","doi-asserted-by":"publisher","DOI":"10.1111\/j.1600-0404.1980.tb03046.x"},{"key":"bibr2-0271678X21991393","doi-asserted-by":"publisher","DOI":"10.1001\/archneur.1963.00460120065007"},{"key":"bibr3-0271678X21991393","first-page":"782","volume":"24","author":"Herscovitch P","year":"1983","journal-title":"J Nucl Med"},{"key":"bibr4-0271678X21991393","first-page":"790","volume":"24","author":"Raichle ME","year":"1983","journal-title":"J Nucl Med"},{"key":"bibr5-0271678X21991393","first-page":"177","volume":"25","author":"Mintun 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