Technical performance of a dual-energy CT system with a novel deep-learning based reconstruction process: Evaluation using an abdomen protocol.

Oostveen, Luuk J; Boedeker, Kirsten L; Balta, Christiana; Shin, Daniel; de Lange, Frank; Prokop, Mathias; Sechopoulos, Ioannis

Oostveen, Luuk J; Boedeker, Kirsten L; Balta, Christiana; Shin, Daniel; de Lange, Frank; Prokop, Mathias; Sechopoulos, Ioannis.

Afiliación

Oostveen LJ; Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
Boedeker KL; Canon Medical Systems Corporation, Otawara, Japan.
Balta C; Canon Medical Systems Corporation, Otawara, Japan.
Shin D; Canon Medical Systems Europe, Zoetermeer, The Netherlands.
de Lange F; Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
Prokop M; Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
Sechopoulos I; Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.

Med Phys ; 50(3): 1378-1389, 2023 Mar.

Article en En | MEDLINE | ID: mdl-36502496

Asunto(s)

Aprendizaje Profundo; Yodo; Polietileno; Tomografía Computarizada por Rayos X/métodos; Fantasmas de Imagen; Abdomen/diagnóstico por imagen

Palabras clave

computed tomography; dual energy and spectral; technology assessment

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo / Yodo Tipo de estudio: Health_technology_assessment Idioma: En Revista: Med Phys Año: 2023 Tipo del documento: Article País de afiliación: Países Bajos Pais de publicación: Estados Unidos

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