{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T08:33:21Z","timestamp":1789115601565,"version":"build-2803163510"},"reference-count":54,"publisher":"IOP Publishing","issue":"17","license":[{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"},{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"},{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"}],"funder":[{"DOI":"10.13039\/501100004489","name":"Mitacs","doi-asserted-by":"crossref","award":["2022-4041"],"award-info":[{"award-number":["2022-4041"]}],"id":[{"id":"10.13039\/501100004489","id-type":"DOI","asserted-by":"crossref"}]},{"name":"INOVAIT","award":["2022-4041"],"award-info":[{"award-number":["2022-4041"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2026,9,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Ventilation\u2013perfusion (\n                    <jats:italic>V<\/jats:italic>\n                    \/\n                    <jats:italic>Q<\/jats:italic>\n                    ) planar scintigraphy remains widely used for diagnosing pulmonary embolism, despite the reported superior diagnostic performance of\n                    <jats:italic>V<\/jats:italic>\n                    \/\n                    <jats:italic>Q<\/jats:italic>\n                    SPECT. In part, SPECT adoption is limited by the differences in clinical interpretation. Pseudo-planar from SPECT data can facilitate transition to SPECT, but existing methods, such as summed angular projections or forward reprojection, fail to reliably reproduce true planar image characteristics.\n                    <jats:italic>Approach.<\/jats:italic>\n                    We propose a novel artificial intelligence\u2013based count enhancement framework to generate high-fidelity pseudo-planar perfusion images (\n                    <jats:italic>pQ<\/jats:italic>\n                    ) from low-count inputs. Two conditional generative adversarial networks (cGANs) were trained using (i) 10% Poisson-resampled planar perfusion images and (ii) matched single-projection SPECT views as inputs, with corresponding full-count planar perfusion images as ground truth. The models were trained and evaluated on 829 paired planar and SPECT studies from a multi-scanner clinical cohort. Performance was assessed using quantitative image similarity metrics (SSIM, PSNR, MSE) and a clinically meaningful task-based evaluation using a previously validated deep learning computer-assisted diagnostic model for vascular defect detection via free-response receiver operating characteristic analysis.\n                    <jats:italic>Main Results.<\/jats:italic>\n                    The cGAN trained on resampled planar images achieved the highest image fidelity (test SSIM: 0.70 \u00b1 0.05; PSNR: 22.3 \u00b1 1.2 dB), significantly outperforming SPECT-trained models and the conventional summed angular method (\n                    <jats:italic>p<\/jats:italic>\n                    &lt; 0.001). In downstream clinical evaluation, this model also yielded superior defect detection performance (sensitivity: 0.800 \u00b1 0.035 at 1.0 false positives per projection), surpassing current clinical practice of the summed angular method.\n                    <jats:italic>Significance.<\/jats:italic>\n                    This study introduces the first AI-based framework for generating pseudo-planar lung scintigraphy from low-count data. The proposed method demonstrates improved image realism and clinically relevant defect preservation, supporting its potential for hybrid planar\u2013SPECT workflows in\n                    <jats:italic>V<\/jats:italic>\n                    \/\n                    <jats:italic>Q<\/jats:italic>\n                    imaging and for accelerating acquisition of planar images.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ae9d10","type":"journal-article","created":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T22:54:03Z","timestamp":1787352843000},"page":"175037","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial count enhancement in lung scintigraphy for acquisition acceleration and pseudo-planar generation"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5939-0109","authenticated-orcid":true,"given":"Amir","family":"Jabbarpour","sequence":"first","affiliation":[{"name":"Carleton University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5759-6712","authenticated-orcid":true,"given":"Ghassel","family":"Siraj","sequence":"additional","affiliation":[{"name":"Electrical Engineering and Computer Science, University of Ottawa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanaz","family":"Kaviani","sequence":"additional","affiliation":[{"name":"Jubilant DraxImage Inc"},{"name":"University of Ottawa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7769-9548","authenticated-orcid":true,"given":"Jochen","family":"Lang","sequence":"additional","affiliation":[{"name":"Electrical Engineering and Computer Science, University of Ottawa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric","family":"Moulton","sequence":"additional","affiliation":[{"name":"Electrical Engineering and Computer Science, University of Ottawa"},{"name":"Jubilant DraxImage Inc"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4357-3779","authenticated-orcid":true,"given":"Ran","family":"Klein","sequence":"additional","affiliation":[{"name":"Carleton University"},{"name":"Electrical Engineering and Computer Science, University of Ottawa"},{"name":"University of Ottawa"},{"name":"The Ottawa Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,9,11]]},"reference":[{"key":"pmbae9d10bib1","doi-asserted-by":"publisher","first-page":"2731","DOI":"10.1007\/s00259-019-04593-0","type":"journal-article","article-title":"Artificial intelligence and radiomics in nuclear medicine: potentials and challenges","volume":"46","author":"Aktolun","year":"2019","journal-title":"Eur. 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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":"2026-03-21","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-08-21","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-09-11","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}