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Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications.
Leuschner, Johannes; Schmidt, Maximilian; Ganguly, Poulami Somanya; Andriiashen, Vladyslav; Coban, Sophia Bethany; Denker, Alexander; Bauer, Dominik; Hadjifaradji, Amir; Batenburg, Kees Joost; Maass, Peter; van Eijnatten, Maureen.
Afiliação
  • Leuschner J; Center for Industrial Mathematics, University of Bremen, Bibliothekstr. 5, 28359 Bremen, Germany.
  • Schmidt M; Center for Industrial Mathematics, University of Bremen, Bibliothekstr. 5, 28359 Bremen, Germany.
  • Ganguly PS; Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands.
  • Andriiashen V; The Mathematical Institute, Leiden University, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands.
  • Coban SB; Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands.
  • Denker A; Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands.
  • Bauer D; Center for Industrial Mathematics, University of Bremen, Bibliothekstr. 5, 28359 Bremen, Germany.
  • Hadjifaradji A; Computer Assisted Clinical Medicine, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany.
  • Batenburg KJ; School of Biomedical Engineering, University of British Columbia, 2222 Health Sciences Mall, Vancouver, BC V6T 1Z3, Canada.
  • Maass P; Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands.
  • van Eijnatten M; Leiden Institute of Advanced Computer Science, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands.
J Imaging ; 7(3)2021 Mar 02.
Article em En | MEDLINE | ID: mdl-34460700
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: J Imaging Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Alemanha País de publicação: Suíça
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: J Imaging Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Alemanha País de publicação: Suíça