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Deep learning-based filling of incomplete sinograms from low-cost, long axial field-of-view PET scanners with inter-detector gaps
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.ORCID iD: 0000-0002-5130-1941
Department Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway.
University Hospital of North Norway, Tromsø, Norway; Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway; Department of Clinical Medicine, UiT The Arctic University of Norway, Tromsø, Norway..
Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. (Radiation Physics)ORCID iD: 0000-0002-3731-3612
2023 (English)In: The international networking symposiumon artificial intelligence and informatics in nuclear medicine: Program book, University Medical Center Groningen , 2023, p. 59-59Conference paper, Oral presentation with published abstract (Refereed)
Place, publisher, year, edition, pages
University Medical Center Groningen , 2023. p. 59-59
Keywords [en]
positron emission tomography (PET), sparse PET, deep learning - artificial intelligence, residual U-net, gap filling, long axial field of view PET, total body PET
National Category
Medical Image Processing Computational Mathematics Computer Vision and Robotics (Autonomous Systems)
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:umu:diva-224909OAI: oai:DiVA.org:umu-224909DiVA, id: diva2:1860574
Conference
International Symposium on Artificial Intelligence and Informatics in Nuclear Medicine, Groningen, Netherlands, October 9-11, 2023.
Part of project
Statistical modelling and intelligent data sampling in MRI and PET measurements for cancer therapy assessment, Swedish Research Council
Funder
Swedish Research Council, 340-2013-5342Available from: 2024-05-24 Created: 2024-05-24 Last updated: 2024-05-29Bibliographically approved

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Leffler, KlaraAxelsson, Jan

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CiteExportLink to record
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