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Filtered maximum likelihood expectation maximization based global reconstruction for bioluminescence tomography

  • Defu Yang
  • Lin Wang
  • Dongmei Chen
  • Chenggang Yan
  • Xiaowei He
  • Jimin Liang
  • Xueli Chen
Original Article
  • 49 Downloads

Abstract

The reconstruction of bioluminescence tomography (BLT) is severely ill-posed due to the insufficient measurements and diffuses nature of the light propagation. Predefined permissible source region (PSR) combined with regularization terms is one common strategy to reduce such ill-posedness. However, the region of PSR is usually hard to determine and can be easily affected by subjective consciousness. Hence, we theoretically developed a filtered maximum likelihood expectation maximization (fMLEM) method for BLT. Our method can avoid predefining the PSR and provide a robust and accurate result for global reconstruction. In the method, the simplified spherical harmonics approximation (SPN) was applied to characterize diffuse light propagation in medium, and the statistical estimation-based MLEM algorithm combined with a filter function was used to solve the inverse problem. We systematically demonstrated the performance of our method by the regular geometry- and digital mouse-based simulations and a liver cancer-based in vivo experiment.

Graphical abstract

The filtered MLEM-based global reconstruction method for BLT.

Keywords

Image reconstruction technique Bioluminescence tomography Global reconstruction 

Notes

Funding information

This work was supported by Zhejiang Province Nature Science Foundation of China LR17F030006 and the National Natural Science Foundation of China under Grant Nos. 61671196, 81571725, 61601154, 81627807, 61327902, and 61372046.

Compliance with ethical standards

In in vivo experiments, all of the animal procedures were performed according to the guidance of the Institutional Animal Care and Use Committee at Peking University (Permit Number. 2011-0039).

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Copyright information

© International Federation for Medical and Biological Engineering 2018

Authors and Affiliations

  • Defu Yang
    • 1
  • Lin Wang
    • 2
  • Dongmei Chen
    • 3
  • Chenggang Yan
    • 1
  • Xiaowei He
    • 2
  • Jimin Liang
    • 4
  • Xueli Chen
    • 4
  1. 1.Institute of Information and ControlHangzhou Dianzi UniversityHangzhouChina
  2. 2.School of Information Sciences and TechnologyNorthwest UniversityXi’anChina
  3. 3.College of Life Information Science and Instrument EngineeringHangzhou Dianzi UniversityHangzhouChina
  4. 4.Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Science and TechnologyXidian UniversityXi’anChina

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