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Multi-material decomposition of spectral CT images via Fully Convolutional DenseNets.
Wu, Xiaochuan; He, Peng; Long, Zourong; Guo, Xiaodong; Chen, Mianyi; Ren, Xuezhi; Chen, Peijun; Deng, Luzhen; An, Kang; Li, Pengcheng; Wei, Biao; Feng, Peng.
Afiliación
  • Wu X; The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China.
  • He P; The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China.
  • Long Z; Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China.
  • Guo X; ICT NDT Engineering Research Center, Ministry of Education, Chongqing University, Chongqing, China.
  • Chen M; The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China.
  • Ren X; The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China.
  • Chen P; The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China.
  • Deng L; The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China.
  • An K; The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China.
  • Li P; The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China.
  • Wei B; ICT NDT Engineering Research Center, Ministry of Education, Chongqing University, Chongqing, China.
  • Feng P; The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China.
J Xray Sci Technol ; 27(3): 461-471, 2019.
Article en En | MEDLINE | ID: mdl-31177260
BACKGROUND: Spectral computed tomography (CT) has the capability to resolve the energy levels of incident photons, which has the potential to distinguish different material compositions. Although material decomposition methods based on x-ray attenuation characteristics have good performance in dual-energy CT imaging, there are some limitations in terms of image contrast and noise levels. OBJECTIVE: This study focused on multi-material decomposition of spectral CT images based on a deep learning approach. METHODS: To classify and quantify different materials, we proposed a multi-material decomposition method via the improved Fully Convolutional DenseNets (FC-DenseNets). A mouse specimen was first scanned by spectral CT system based on a photon-counting detector with different energy ranges. We then constructed a training set from the reconstructed CT images for deep learning to decompose different materials. RESULTS: Experimental results demonstrated that the proposed multi-material decomposition method could more effectively identify bone, lung and soft tissue than the basis material decomposition based on post-reconstruction space in high noise levels. CONCLUSIONS: The new proposed approach yielded good performance on spectral CT material decomposition, which could establish guidelines for multi-material decomposition approaches based on the deep learning algorithm.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Imagen Asistido por Computador / Tomografía Computarizada por Rayos X / Imagen Radiográfica por Emisión de Doble Fotón / Aprendizaje Profundo Tipo de estudio: Prognostic_studies Límite: Animals Idioma: En Revista: J Xray Sci Technol Asunto de la revista: RADIOLOGIA Año: 2019 Tipo del documento: Article País de afiliación: China Pais de publicación: Países Bajos
Texto completo
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XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Imagen Asistido por Computador / Tomografía Computarizada por Rayos X / Imagen Radiográfica por Emisión de Doble Fotón / Aprendizaje Profundo Tipo de estudio: Prognostic_studies Límite: Animals Idioma: En Revista: J Xray Sci Technol Asunto de la revista: RADIOLOGIA Año: 2019 Tipo del documento: Article País de afiliación: China Pais de publicación: Países Bajos