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Investigation of maximum a posteriori probability expectation-maximization for image-based weighting spectral X-ray CT image reconstruction.
Zhou, Zhengdong; Xin, Runchao; Guan, Shaolin; Li, Jianbo; Tu, Jiali.
Afiliación
  • Zhou Z; State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, P. R. China.
  • Xin R; State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, P. R. China.
  • Guan S; Department of Nuclear Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, P. R. China.
  • Li J; State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, P. R. China.
  • Tu J; Department of Nuclear Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, P. R. China.
J Xray Sci Technol ; 26(5): 853-864, 2018.
Article en En | MEDLINE | ID: mdl-30124464
Development of spectral X-ray computer tomography (CT) equipped with photon counting detector has been recently attracting great research interest. This work aims to improve the quality of spectral X-ray CT image. Maximum a posteriori (MAP) expectation-maximization (EM) algorithm is applied for reconstructing image-based weighting spectral X-ray CT images. A spectral X-ray CT system based on the cadmium zinc telluride photon counting detector and a fat cylinder phantom were simulated. Comparing with the commonly used filtered back projection (FBP) method, the proposed method reduced noise in the final weighting images at 2, 4, 6 and 9 energy bins up to 85.2%, 87.5%, 86.7% and 85%, respectively. CNR improvement ranged from 6.53 to 7.77. Compared with the prior image constrained compressed sensing (PICCS) method, the proposed method could reduce noise in the final weighting images by 36.5%, 44.6%, 27.3% and 18% at 2, 4, 6 and 9 energy bins, respectively, and improve the contrast-to-noise ratio (CNR) by 1.17 to 1.81. The simulation study also showed that comparing with the FBP and PICCS algorithms, image-based weighting imaging using MAP-EM statistical algorithm yielded significant improvement of the CNR and reduced the noise of the final weighting image.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Procesamiento de Imagen Asistido por Computador / Tomografía Computarizada por Rayos X Idioma: En Revista: J Xray Sci Technol Asunto de la revista: RADIOLOGIA Año: 2018 Tipo del documento: Article Pais de publicación: Países Bajos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Procesamiento de Imagen Asistido por Computador / Tomografía Computarizada por Rayos X Idioma: En Revista: J Xray Sci Technol Asunto de la revista: RADIOLOGIA Año: 2018 Tipo del documento: Article Pais de publicación: Países Bajos