Deep learning quantification of vascular pharmacokinetic parameters in mouse brain tumor models.

Arledge, Chad A; Sankepalle, Deeksha M; Crowe, William N; Liu, Yang; Wang, Lulu; Zhao, Dawen

Arledge, Chad A; Sankepalle, Deeksha M; Crowe, William N; Liu, Yang; Wang, Lulu; Zhao, Dawen.

Afiliação

Arledge CA; Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.
Sankepalle DM; Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.
Crowe WN; Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.
Liu Y; Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.
Wang L; Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.
Zhao D; Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.

Front Biosci (Landmark Ed) ; 27(3): 99, 2022 03 16.

Article em En | MEDLINE | ID: mdl-35345331

Assuntos

Neoplasias Encefálicas; Aprendizado Profundo; Animais; Neoplasias Encefálicas/patologia; Meios de Contraste; Humanos; Imageamento por Ressonância Magnética/métodos; Camundongos; Camundongos Nus

Palavras-chave

breast cancer brain metastasis; convolutional neural network; dynamic contrast enhanced MRI; glioblastoma; pharmacokinetic modeling; vascular permeability parameters

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Encefálicas / Aprendizado Profundo Limite: Animals / Humans Idioma: En Revista: Front Biosci (Landmark Ed) Ano de publicação: 2022 Tipo de documento: Article

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