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作者:

Feng, Jinchao (Feng, Jinchao.) (学者:冯金超) | Chang, Di (Chang, Di.) | Li, Zhe (Li, Zhe.) | Sun, Zhonghua (Sun, Zhonghua.) | Jia, Kebin (Jia, Kebin.) (学者:贾克斌)

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摘要:

As a new molecular imaging technology, Cherenkov-excited luminescence scanned imaging (CELSI) has merits of high spatial resolution and large imaging depth, therefore showing a potential for monitoring the physiological changes of tumors during radiotherapy. In our previous work, we developed a tomographic technique for CELSI based on Tikhonov method, which is problematic to reconstruct accurate fluorescent targets with position depth larger than 3 cm or with low contrast. To overcome this problem, we develop a sparse reconstruction method for tomographic CELSI based on approximate message passing. To demonstrate the merits of the proposed algorithm, we compare it with traditional Tikhonov regularization and three sparse based reconstruction algorithms. Our results show that the proposed method can achieve best performance in terms of mean-square error and contrast noise ratio. © 2020, Chinese Lasers Press. All right reserved.

关键词:

Image reconstruction Light Mean square error Molecular imaging Tomography Message passing Luminescence

作者机构:

  • [ 1 ] [Feng, Jinchao]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Feng, Jinchao]Beijing Laboratory of Advanced Information Networks, Beijing; 100124, China
  • [ 3 ] [Chang, Di]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Chang, Di]Beijing Laboratory of Advanced Information Networks, Beijing; 100124, China
  • [ 5 ] [Li, Zhe]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 6 ] [Li, Zhe]Beijing Laboratory of Advanced Information Networks, Beijing; 100124, China
  • [ 7 ] [Sun, Zhonghua]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 8 ] [Sun, Zhonghua]Beijing Laboratory of Advanced Information Networks, Beijing; 100124, China
  • [ 9 ] [Jia, Kebin]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 10 ] [Jia, Kebin]Beijing Laboratory of Advanced Information Networks, Beijing; 100124, China

通讯作者信息:

  • 贾克斌

    [jia, kebin]beijing key laboratory of computational intelligence and intelligent system, faculty of information technology, beijing university of technology, beijing; 100124, china;;[jia, kebin]beijing laboratory of advanced information networks, beijing; 100124, china

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来源 :

Chinese Journal of Lasers

ISSN: 0258-7025

年份: 2020

期: 2

卷: 47

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 2

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