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A adaptive parameter selection strategy based on maximizing the probability of data for robust fluorescence molecular tomography reconstruction
  • +7
  • Hongbo Guo,
  • Jintao Li,
  • Lizhi Zhang,
  • |Diya Zhang,
  • Dizhen Kang,
  • Beilei Wang,
  • Xiaowei He,
  • Heng Zhang,
  • Yizhe Zhao,
  • Yuqing Hou
Hongbo Guo
Northwest University School of Information Science and Technology

Corresponding Author:guohb@nwu.edu.cn

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Jintao Li
Northwest University School of Information Science and Technology
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Lizhi Zhang
Northwest University School of Information Science and Technology
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|Diya Zhang
Northwest University School of Information Science and Technology
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Dizhen Kang
Northwest University School of Information Science and Technology
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Beilei Wang
Northwest University School of Information Science and Technology
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Xiaowei He
Northwest University School of Information Science and Technology
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Heng Zhang
Northwest University School of Information Science and Technology
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Yizhe Zhao
Northwest University School of Information Science and Technology
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Yuqing Hou
Northwest University School of Information Science and Technology
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Abstract

To alleviate the ill-posed of the inverse problem in Fluorescent molecular tomography (FMT), many regularization methods based on L 2 or L 1 norm have been proposed. Whereas, the quality of regularization parameters affect the performance of the reconstruction algorithm. Some classical parameter selection strategies usually need initialization of parameter range and high computing costs, which is not universal in the practical application of FMT. In this paper, an universally applicable adaptive parameter selection method based on maximizing the probability of data (MPD) strategy was proposed. This strategy used maximum a posteriori (MAP) estimation and maximum likelihood (ML) estimation to establish a regularization parameters model. The stable optimal regularization parameters can be determined by multiple iterative estimates. Numerical simulations and in vivo experiments show that MPD strategy can obtain stable regularization parameters for both regularization algorithms based on L 2 or L 1 norm and achieve good reconstruction performance.
31 Jan 2023Submitted to Journal of Biophotonics
31 Jan 2023Submission Checks Completed
31 Jan 2023Assigned to Editor
31 Jan 2023Review(s) Completed, Editorial Evaluation Pending
31 Jan 2023Reviewer(s) Assigned
24 Feb 2023Editorial Decision: Revise Major
21 Mar 20231st Revision Received
21 Mar 2023Submission Checks Completed
21 Mar 2023Assigned to Editor
21 Mar 2023Reviewer(s) Assigned
21 Mar 2023Review(s) Completed, Editorial Evaluation Pending
14 Apr 2023Editorial Decision: Accept