Generative deep learning synthesizes high signal-to-noise ratio sensitivity maps for PET from low count direct normalization data.

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Bibliographic Details
Title: Generative deep learning synthesizes high signal-to-noise ratio sensitivity maps for PET from low count direct normalization data.
Authors: Jafaritadi M; Department of Radiology, Stanford University, Stanford, CA, United States of America., Groll A; Department of Radiology, Stanford University, Stanford, CA, United States of America., Chin M; Department of Radiology, Stanford University, Stanford, CA, United States of America.; Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America., Chinn G; Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America., Fisher J; Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America., Innes D; Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America., Levin CS; Department of Radiology, Stanford University, Stanford, CA, United States of America.; Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America.; Department of Physics, Stanford University, Stanford, CA, United States of America.; Department of Bioengineering, Stanford University, Stanford, CA, United States of America.
Source: Physics in medicine and biology [Phys Med Biol] 2026 Feb 12; Vol. 71 (3). Date of Electronic Publication: 2026 Feb 12.
Publication Type: Journal Article
Journal Info: Publisher: IOP Publishing Country of Publication: England NLM ID: 0401220 Publication Model: Electronic Cited Medium: Internet ISSN: 1361-6560 (Electronic) Linking ISSN: 00319155 NLM ISO Abbreviation: Phys Med Biol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1361-6560
DOI:10.1088/1361-6560/ae3ec6