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Convolutional neural networks for automatic image quality control and EARL compliance of PET images.
Pfaehler, Elisabeth; Euba, Daniela; Rinscheid, Andreas; Hoekstra, Otto S; Zijlstra, Josee; van Sluis, Joyce; Brouwers, Adrienne H; Lapa, Constantin; Boellaard, Ronald.
Afiliação
  • Pfaehler E; Nuclear Medicine, Medical Faculty, University of Augsburg, Augsburg, Germany. Elisabeth.pfaehler@uk-augsburg.de.
  • Euba D; Nuclear Medicine, Medical Faculty, University of Augsburg, Augsburg, Germany.
  • Rinscheid A; Medical Physics and Radiation Protection, University Hospital Augsburg, Augsburg, Germany.
  • Hoekstra OS; Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands.
  • Zijlstra J; Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands.
  • van Sluis J; Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
  • Brouwers AH; Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
  • Lapa C; Nuclear Medicine, Medical Faculty, University of Augsburg, Augsburg, Germany.
  • Boellaard R; Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands.
EJNMMI Phys ; 9(1): 53, 2022 Aug 09.
Article em En | MEDLINE | ID: mdl-35943622

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Clinical_trials / Guideline / Prognostic_studies Idioma: En Revista: EJNMMI Phys Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Alemanha

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Clinical_trials / Guideline / Prognostic_studies Idioma: En Revista: EJNMMI Phys Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Alemanha