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Artificial Intelligence and Deep Learning for Advancing PET Image Reconstruction: State-of-the-Art and Future Directions.

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Bibliographic Details
Title: Artificial Intelligence and Deep Learning for Advancing PET Image Reconstruction: State-of-the-Art and Future Directions.
Transliterated Title: Künstliche Intelligenz und Deep Learning für die Weiterentwicklung der PET-Bildrekonstruktion: Stand der Technik und zukünftige Perspektiven.
Authors: Hellwig D; Department of Nuclear Medicine, University Hospital Regensburg, Regensburg, Germany.; Partner Site Regensburg, Bavarian Center for Cancer Research (BZKF), Regensburg, Germany.; Medical Data Integration Center (MEDIZUKR), University Hospital Regensburg, Regensburg, Germany., Hellwig NC; Department of Nuclear Medicine, University Hospital Regensburg, Regensburg, Germany.; Medical Data Integration Center (MEDIZUKR), University Hospital Regensburg, Regensburg, Germany., Boehner S; Department of Nuclear Medicine, University Hospital Regensburg, Regensburg, Germany.; Partner Site Regensburg, Bavarian Center for Cancer Research (BZKF), Regensburg, Germany.; Medical Data Integration Center (MEDIZUKR), University Hospital Regensburg, Regensburg, Germany., Fuchs T; Department of Nuclear Medicine, University Hospital Regensburg, Regensburg, Germany.; Partner Site Regensburg, Bavarian Center for Cancer Research (BZKF), Regensburg, Germany.; Medical Data Integration Center (MEDIZUKR), University Hospital Regensburg, Regensburg, Germany., Fischer R; Department of Nuclear Medicine, University Hospital Regensburg, Regensburg, Germany.; Partner Site Regensburg, Bavarian Center for Cancer Research (BZKF), Regensburg, Germany.; Medical Data Integration Center (MEDIZUKR), University Hospital Regensburg, Regensburg, Germany., Schmidt D; Department of Nuclear Medicine, University Hospital Regensburg, Regensburg, Germany.
Source: Nuklearmedizin. Nuclear medicine [Nuklearmedizin] 2023 Dec; Vol. 62 (6), pp. 334-342. Date of Electronic Publication: 2023 Nov 23.
Publication Type: Review; Journal Article
Journal Info: Publisher: Thieme Country of Publication: Germany NLM ID: 7609387 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2567-6407 (Electronic) Linking ISSN: 00295566 NLM ISO Abbreviation: Nuklearmedizin Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2567-6407
DOI:10.1055/a-2198-0358