{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T21:35:42Z","timestamp":1788816942704,"version":"build-2803163510"},"reference-count":43,"publisher":"IOP Publishing","issue":"6","license":[{"start":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:00:00Z","timestamp":1774396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:00:00Z","timestamp":1774396800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:00:00Z","timestamp":1774396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"},{"start":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:00:00Z","timestamp":1774396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"}],"funder":[{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"crossref","award":["R01NS094253"],"award-info":[{"award-number":["R01NS094253"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2026,3,28]]},"abstract":"Abstract<\/jats:title>\n \n Objective.<\/jats:italic>\n Direct reconstruction (DR) of parametric images from dynamic positron emission tomography data has been shown to provide substantial noise reduction compared to the conventional indirect reconstruction (IR) approach where frames are first reconstructed and then voxel time-activity curves are fitted to a kinetic model. The main goal was to compare DR and IR, on both\n within-subject<\/jats:italic>\n and\n between-subject<\/jats:italic>\n variability.\n Approach.<\/jats:italic>\n This work evaluated the Parametric motion-compensation OSEM List-mode algorithm for resolution-recovery-1T DR method, using multiple scans of Parkinson\u2019s disease patients with [\n 11<\/jats:sup>\n C]UCB-J, a radioligand for synaptic vesicle glycoprotein 2A (SV2A), a marker for synaptic density. This was achieved by comparing\n K<\/jats:italic>\n 1<\/jats:sub>\n ,\n k<\/jats:italic>\n 2<\/jats:sub>\n , and\n V<\/jats:italic>\n T<\/jats:sub>\n parametric images estimated, at full- and lower-count levels (20%, 10%, and 5%), between DR and IR.\n Main Results.<\/jats:italic>\n DR delivered considerable improvement, compared to IR, by substantially reducing variability for both\n within-subject<\/jats:italic>\n and\n between-subject<\/jats:italic>\n analyses, and dramatically reducing noise-induced bias for\n K<\/jats:italic>\n 1<\/jats:sub>\n and\n V<\/jats:italic>\n T<\/jats:sub>\n . Conversely, IR increased the\n within-subject<\/jats:italic>\n variability for\n K<\/jats:italic>\n 1<\/jats:sub>\n by 79%\u2013353% and for\n V<\/jats:italic>\n T<\/jats:sub>\n by 62%\u201379% across the lower count levels (averaged over regions at matched iterations). The\n between-subject<\/jats:italic>\n variability was also increased with IR over DR with an increase of 20%\u2013221% for\n K<\/jats:italic>\n 1<\/jats:sub>\n and 45%\u201348% for\n V<\/jats:italic>\n T<\/jats:sub>\n . Even at the full-count level, the\n between-subject<\/jats:italic>\n variability was slightly increased for\n K<\/jats:italic>\n 1<\/jats:sub>\n by 4%, but by 24% for\n V<\/jats:italic>\n T<\/jats:sub>\n . Furthermore, at 5% count level, DR delivered comparable variability to IR at 20% counts. The noise-induced %bias, relative to the full-count level, for IR was 3%\u201328% (from 20% to 5% count levels respectively) for\n K<\/jats:italic>\n 1<\/jats:sub>\n and 12%\u201331% for\n V<\/jats:italic>\n T<\/jats:sub>\n , whilst for DR the %bias was only 1% for\n K<\/jats:italic>\n 1<\/jats:sub>\n across count levels, and 7%\u201318% for\n V<\/jats:italic>\n T<\/jats:sub>\n .\n Significance.<\/jats:italic>\n To the best of our knowledge, this is the first demonstration that direct-4D reconstruction delivers lower variability and bias not only for\n within-subject<\/jats:italic>\n analysis, but also for\n between-subject<\/jats:italic>\n analysis.\n <\/jats:p>","DOI":"10.1088\/1361-6560\/ae520a","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T22:56:34Z","timestamp":1773442594000},"page":"065007","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Variance reduction with synaptic density imaging in Parkinson\u2019s disease using direct-4D PET image reconstruction"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3702-7314","authenticated-orcid":true,"given":"Paul","family":"Gravel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Dominique","family":"Gallezot","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kathryn","family":"Fontaine","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Matuskey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard E","family":"Carson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"pmbae520abib1","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.ejmp.2014.12.008","type":"journal-article","article-title":"Full field spatially-variant image-based resolution modelling reconstruction for the HRRT","volume":"31","author":"Angelis","year":"2015","journal-title":"Phys. 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Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2025-10-17","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-03-13","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-03-25","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}