A Systematic Review and Identification of the Challenges of Deep Learning Techniques for Undersampled Magnetic Resonance Image Reconstruction.

Hossain, Md Biddut; Shinde, Rupali Kiran; Oh, Sukhoon; Kwon, Ki-Chul; Kim, Nam

Hossain, Md Biddut; Shinde, Rupali Kiran; Oh, Sukhoon; Kwon, Ki-Chul; Kim, Nam.

Afiliação

Hossain MB; School of Information and Communication Engineering, Chungbuk National University, Cheongju-si 28644, Chungcheongbuk-do, Republic of Korea.
Shinde RK; School of Information and Communication Engineering, Chungbuk National University, Cheongju-si 28644, Chungcheongbuk-do, Republic of Korea.
Oh S; Research Equipment Operation Department, Korea Basic Science Institute, Cheongju-si 28119, Chungcheongbuk-do, Republic of Korea.
Kwon KC; School of Information and Communication Engineering, Chungbuk National University, Cheongju-si 28644, Chungcheongbuk-do, Republic of Korea.
Kim N; School of Information and Communication Engineering, Chungbuk National University, Cheongju-si 28644, Chungcheongbuk-do, Republic of Korea.

Sensors (Basel) ; 24(3)2024 Jan 24.

Article em En | MEDLINE | ID: mdl-38339469

Assuntos

Aprendizado Profundo; Processamento de Imagem Assistida por Computador; Imageamento por Ressonância Magnética; Imageamento por Ressonância Magnética/métodos; Humanos; Processamento de Imagem Assistida por Computador/métodos; Redes Neurais de Computação; Algoritmos

Palavras-chave

3D MRI; DL tools; MRI datasets; Swin transformer; deep learning; federated learning; transfer learning

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Imageamento por Ressonância Magnética / Aprendizado Profundo Tipo de estudo: Diagnostic_studies / Guideline / Prognostic_studies / Systematic_reviews Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article

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