Reconstruction of undersampled 3D non-Cartesian image-based navigators for coronary MRA using an unrolled deep learning model.

Malavé, Mario O; Baron, Corey A; Koundinyan, Srivathsan P; Sandino, Christopher M; Ong, Frank; Cheng, Joseph Y; Nishimura, Dwight G

Malavé, Mario O; Baron, Corey A; Koundinyan, Srivathsan P; Sandino, Christopher M; Ong, Frank; Cheng, Joseph Y; Nishimura, Dwight G.

Afiliación

Malavé MO; Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA.
Baron CA; Department of Medical Biophysics, Western University, London, ON, Canada.
Koundinyan SP; Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA.
Sandino CM; Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA.
Ong F; Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA.
Cheng JY; Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA.
Nishimura DG; Department of Radiology, Stanford University, Stanford, CA.

Magn Reson Med ; 84(2): 800-812, 2020 08.

Article en En | MEDLINE | ID: mdl-32011021

Asunto(s)

Aprendizaje Profundo; Angiografía por Resonancia Magnética; Angiografía Coronaria; Corazón; Humanos; Imagenología Tridimensional

Palabras clave

3D cones trajectory; convolutional neural networks; coronary MRA; non-Cartesian

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Angiografía por Resonancia Magnética / Aprendizaje Profundo Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Magn Reson Med Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2020 Tipo del documento: Article País de afiliación: Canadá

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