Virtual and real-world implementation of deep-learning-based image denoising model on projection domain in digital tomosynthesis and cone-beam computed tomography data.

Jin, David Shih-Chun; Chang, Li-Sheng; Wang, Yu-Hong; Chen, Jyh-Cheng; Tseng, Snow H; Liu, Tse-Ying

Jin, David Shih-Chun; Chang, Li-Sheng; Wang, Yu-Hong; Chen, Jyh-Cheng; Tseng, Snow H; Liu, Tse-Ying.

Afiliación

Jin DS; Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, 112304 Taipei, Taiwan, ROC.
Chang LS; Department of Electro-Optical Engineering, National Taipei University of Technology, 106344, Taipei, Taiwan, ROC.
Wang YH; Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, 112304 Taipei, Taiwan, ROC.
Chen JC; Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, 112304 Taipei, Taiwan, ROC.
Tseng SH; Institute of Biophotonics, National Yang Ming Chiao Tung University, 112304 Taipei, Taiwan, ROC.
Liu TY; Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, 112304 Taipei, Taiwan, ROC.

Biomed Phys Eng Express ; 8(6)2022 10 21.

Article en En | MEDLINE | ID: mdl-36223710

Asunto(s)

Aprendizaje Profundo; Tomografía Computarizada de Haz Cónico; Tomografía Computarizada por Rayos X/métodos; Artefactos; Simulación por Computador

Palabras clave

3D concatenating convolutional encoder-decoder (CCE-3D); denoising model; low-dose computed tomography (LDCT); structural sensitive loss (SSL); transfer learning (TL)

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo Idioma: En Revista: Biomed Phys Eng Express Año: 2022 Tipo del documento: Article Pais de publicación: Reino Unido

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