Machine learning-based 3-D geometry reconstruction and modeling of aortic valve deformation using 3-D computed tomography images.

Liang, Liang; Kong, Fanwei; Martin, Caitlin; Pham, Thuy; Wang, Qian; Duncan, James; Sun, Wei

Liang, Liang; Kong, Fanwei; Martin, Caitlin; Pham, Thuy; Wang, Qian; Duncan, James; Sun, Wei.

Afiliación

Liang L; Tissue Mechanics Laboratory The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA.
Kong F; Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA.
Martin C; Tissue Mechanics Laboratory The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA.
Pham T; Tissue Mechanics Laboratory The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA.
Wang Q; Tissue Mechanics Laboratory The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA.
Duncan J; Tissue Mechanics Laboratory The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA.
Sun W; Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA.

Int J Numer Method Biomed Eng ; 33(5)2017 05.

Article en En | MEDLINE | ID: mdl-27557429

Asunto(s)

Válvula Aórtica/diagnóstico por imagen; Válvula Aórtica/patología; Imagenología Tridimensional/métodos; Aprendizaje Automático; Simulación por Computador; Análisis de Elementos Finitos; Humanos; Tomografía Computarizada por Rayos X

Palabras clave

aortic valve finite element model; aortic valve geometry reconstruction; cardiac image analysis; machine learning

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Válvula Aórtica / Imagenología Tridimensional / Aprendizaje Automático Tipo de estudio: Guideline Límite: Humans Idioma: En Revista: Int J Numer Method Biomed Eng Año: 2017 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido

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