Your browser doesn't support javascript.
loading
Application of deep learning-based super-resolution to T1-weighted postcontrast gradient echo imaging of the chest.
Maennlin, Simon; Wessling, Daniel; Herrmann, Judith; Almansour, Haidara; Nickel, Dominik; Kannengiesser, Stephan; Afat, Saif; Gassenmaier, Sebastian.
Afiliación
  • Maennlin S; Diagnostic and Interventional Radiology, University Hospital Tuebingen, Hoppe- Seyler- Str. 3, 72076, Tübingen, Germany. Simon.Maennlin@med.uni-tuebingen.de.
  • Wessling D; Diagnostic and Interventional Radiology, University Hospital Tuebingen, Hoppe- Seyler- Str. 3, 72076, Tübingen, Germany.
  • Herrmann J; Diagnostic and Interventional Radiology, University Hospital Tuebingen, Hoppe- Seyler- Str. 3, 72076, Tübingen, Germany.
  • Almansour H; Diagnostic and Interventional Radiology, University Hospital Tuebingen, Hoppe- Seyler- Str. 3, 72076, Tübingen, Germany.
  • Nickel D; MR Applications Predevelopment, Siemens Healthcare GmbH, Allee Am Roethelheimpark 2, 91052, Erlangen, Germany.
  • Kannengiesser S; MR Applications Predevelopment, Siemens Healthcare GmbH, Allee Am Roethelheimpark 2, 91052, Erlangen, Germany.
  • Afat S; Diagnostic and Interventional Radiology, University Hospital Tuebingen, Hoppe- Seyler- Str. 3, 72076, Tübingen, Germany.
  • Gassenmaier S; Diagnostic and Interventional Radiology, University Hospital Tuebingen, Hoppe- Seyler- Str. 3, 72076, Tübingen, Germany.
Radiol Med ; 128(2): 184-190, 2023 Feb.
Article en En | MEDLINE | ID: mdl-36609662
Asunto(s)
Palabras clave
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Imagen por Resonancia Magnética / Aprendizaje Profundo Tipo de estudio: Observational_studies Límite: Humans Idioma: En Revista: Radiol Med Año: 2023 Tipo del documento: Article País de afiliación: Alemania
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Imagen por Resonancia Magnética / Aprendizaje Profundo Tipo de estudio: Observational_studies Límite: Humans Idioma: En Revista: Radiol Med Año: 2023 Tipo del documento: Article País de afiliación: Alemania