Sparse regularization-based reconstruction for bioluminescence tomography using a multilevel adaptive finite element method

  • Authors:

  • Xiaowei He;Yanbin Hou;Duofang Chen;Yuchuan Jiang;Man Shen;Junting Liu;Qitan Zhang;Jie Tian

  • Affiliations:

  • Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, China;Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, China and School of Information Sciences and Technology, Northwest University, Xi'an, Shaanxi, Chin ...;Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, China;Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, China;Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, China;Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, China;Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, China;Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, China and Institute of Automation, Chinese Academy of Sciences, Beijing, China

  • Venue:
  • Journal of Biomedical Imaging - Special issue on modern mathematics in biomedical imaging
  • Year:
  • 2011

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Abstract

Bioluminescence tomography (BLT) is a promising tool for studying physiological and pathological processes at cellular and molecular levels. In most clinical or preclinical practices, fine discretization is needed for recovering sources with acceptable resolution when solving BLT with finite element method (FEM). Nevertheless, uniformly fine meshes would cause large dataset and overfine meshes might aggravate the ill-posedness of BLT. Additionally, accurately quantitative information of density and power has not been simultaneously obtained so far. In this paper, we present a novel multilevel sparse reconstruction method based on adaptive FEM framework. In this method, permissible source region gradually reduces with adaptive local mesh refinement. By using sparse reconstruction with l1 regularization on multilevel adaptive meshes, simultaneous recovery of density and power as well as accurate source location can be achieved. Experimental results for heterogeneous phantom and mouse atlas model demonstrate its effectiveness and potentiality in the application of quantitative BLT.