Virtual high-count PET image generation using a deep learning method.

Liu, Juan; Ren, Sijin; Wang, Rui; Mirian, Niloufarsadat; Tsai, Yu-Jung; Kulon, Michal; Pucar, Darko; Chen, Ming-Kai; Liu, Chi

Liu, Juan; Ren, Sijin; Wang, Rui; Mirian, Niloufarsadat; Tsai, Yu-Jung; Kulon, Michal; Pucar, Darko; Chen, Ming-Kai; Liu, Chi.

Afiliación

Liu J; Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA.
Ren S; Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA.
Wang R; Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA.
Mirian N; Department of Engineering Physics, Tsinghua University, Beijing, China.
Tsai YJ; Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA.
Kulon M; Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA.
Pucar D; Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA.
Chen MK; Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA.
Liu C; Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA.

Med Phys ; 49(9): 5830-5840, 2022 Sep.

Article en En | MEDLINE | ID: mdl-35880541

Asunto(s)

Aprendizaje Profundo; Humanos; Procesamiento de Imagen Asistido por Computador/métodos; Tomografía de Emisión de Positrones/métodos; Proyectos de Investigación; Relación Señal-Ruido

Palabras clave

PET; deep learning; virtual high count

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Med Phys Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos

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