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International Journal of Biomedical Imaging
Volume 2011 (2011), Article ID 203537, 11 pages
http://dx.doi.org/10.1155/2011/203537
Research Article

Sparse Regularization-Based Reconstruction for Bioluminescence Tomography Using a Multilevel Adaptive Finite Element Method

1Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an 710071, China
2School of Information Sciences and Technology, Northwest University, Xi'an, Shaanxi 710069, China
3Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

Received 30 April 2010; Accepted 13 August 2010

Academic Editor: Robert J. Plemmons

Copyright © 2011 Xiaowei He et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.