View in EDS

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

Saved in:
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
FullText Text:
  Availability: 0
Header DbId: mdl
DbLabel: MEDLINE Ultimate
An: 41604704
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Generative deep learning synthesizes high signal-to-noise ratio sensitivity maps for PET from low count direct normalization data.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AU" term="%22Jafaritadi+M%22">Jafaritadi M</searchLink>; Department of Radiology, Stanford University, Stanford, CA, United States of America.<br /><searchLink fieldCode="AU" term="%22Groll+A%22">Groll A</searchLink>; Department of Radiology, Stanford University, Stanford, CA, United States of America.<br /><searchLink fieldCode="AU" term="%22Chin+M%22">Chin M</searchLink>; Department of Radiology, Stanford University, Stanford, CA, United States of America.; Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America.<br /><searchLink fieldCode="AU" term="%22Chinn+G%22">Chinn G</searchLink>; Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America.<br /><searchLink fieldCode="AU" term="%22Fisher+J%22">Fisher J</searchLink>; Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America.<br /><searchLink fieldCode="AU" term="%22Innes+D%22">Innes D</searchLink>; Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America.<br /><searchLink fieldCode="AU" term="%22Levin+CS%22">Levin CS</searchLink>; 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.
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%220401220%22">Physics in medicine and biology</searchLink> [Phys Med Biol] 2026 Feb 12; Vol. 71 (3). <i>Date of Electronic Publication: </i>2026 Feb 12.
– Name: TypePub
  Label: Publication Type
  Group: TypPub
  Data: Journal Article
– Name: TitleSource
  Label: Journal Info
  Group: Src
  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22IOP+Publishing%22">IOP Publishing </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>0401220 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1361-6560 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2200319155%22">00319155 </searchLink><i>NLM ISO Abbreviation: </i>Phys Med Biol <i>Subsets: </i>MEDLINE
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=41604704
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1088/1361-6560/ae3ec6
    Languages:
      – Code: eng
        Text: English
    Titles:
      – TitleFull: Generative deep learning synthesizes high signal-to-noise ratio sensitivity maps for PET from low count direct normalization data.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Jafaritadi M
      – PersonEntity:
          Name:
            NameFull: Groll A
      – PersonEntity:
          Name:
            NameFull: Chin M
      – PersonEntity:
          Name:
            NameFull: Chinn G
      – PersonEntity:
          Name:
            NameFull: Fisher J
      – PersonEntity:
          Name:
            NameFull: Innes D
      – PersonEntity:
          Name:
            NameFull: Levin CS
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 12
              M: 02
              Text: 2026 Feb 12
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-electronic
              Value: 1361-6560
          Numbering:
            – Type: volume
              Value: 71
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: Physics in medicine and biology
              Type: main
ResultId 1