Evaluation of deep-learning image reconstruction for chest CT examinations at two different dose levels.

Svalkvist, Angelica; Fagman, Erika; Vikgren, Jenny; Ku, Sara; Diniz, Micael Oliveira; Norrlund, Rauni Rossi; Johnsson, Åse A

Svalkvist, Angelica; Fagman, Erika; Vikgren, Jenny; Ku, Sara; Diniz, Micael Oliveira; Norrlund, Rauni Rossi; Johnsson, Åse A.

Afiliação

Svalkvist A; Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden.
Fagman E; Department of Medical Radiation Sciences, Institute of Clinical Sciences, The Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden.
Vikgren J; Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
Ku S; Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Diniz MO; Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
Norrlund RR; Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Johnsson ÅA; Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden.

J Appl Clin Med Phys ; 24(3): e13871, 2023 Mar.

Article em En | MEDLINE | ID: mdl-36583696

Assuntos

Aprendizado Profundo; Humanos; Doses de Radiação; Interpretação de Imagem Radiográfica Assistida por Computador/métodos; Tomografia Computadorizada por Raios X/métodos; Processamento de Imagem Assistida por Computador/métodos; Algoritmos

Palavras-chave

chest; computed tomography; deep-learning image reconstruction; post-processing; ultra-low dose

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Guideline Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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