Deep Learning Approaches for Automatic Quality Assurance of Magnetic Resonance Images Using ACR Phantom.

Torfeh, Tarraf; Aouadi, Souha; Yoganathan, S A; Paloor, Satheesh; Hammoud, Rabih; Al-Hammadi, Noora

Torfeh, Tarraf; Aouadi, Souha; Yoganathan, S A; Paloor, Satheesh; Hammoud, Rabih; Al-Hammadi, Noora.

Afiliación

Torfeh T; Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar. ttorfeh@hamad.qa.
Aouadi S; Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar.
Yoganathan SA; Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar.
Paloor S; Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar.
Hammoud R; Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar.
Al-Hammadi N; Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar.

BMC Med Imaging ; 23(1): 197, 2023 11 29.

Article en En | MEDLINE | ID: mdl-38031032

Asunto(s)

Inteligencia Artificial; Aprendizaje Profundo; Humanos; Fantasmas de Imagen; Aprendizaje Automático; Imagen por Resonancia Magnética

Palabras clave

Deep learning; MRI; Quality control

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Inteligencia Artificial / Aprendizaje Profundo Límite: Humans Idioma: En Revista: BMC Med Imaging Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2023 Tipo del documento: Article País de afiliación: Qatar

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