Sparse Regularization-Based Reconstruction for Bioluminescence Tomography Using a Multilevel Adaptive Finite Element Method
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...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2011-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2011/203537 |
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| _version_ | 1832565198735540224 |
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| author | Xiaowei He Yanbin Hou Duofang Chen Yuchuan Jiang Man Shen Junting Liu Qitan Zhang Jie Tian |
| author_facet | Xiaowei He Yanbin Hou Duofang Chen Yuchuan Jiang Man Shen Junting Liu Qitan Zhang Jie Tian |
| author_sort | Xiaowei He |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c242adfeeee84bb5ac2920476f97ca19 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-c242adfeeee84bb5ac2920476f97ca192025-02-03T01:09:02ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962011-01-01201110.1155/2011/203537203537Sparse Regularization-Based Reconstruction for Bioluminescence Tomography Using a Multilevel Adaptive Finite Element MethodXiaowei He0Yanbin Hou1Duofang Chen2Yuchuan Jiang3Man Shen4Junting Liu5Qitan Zhang6Jie Tian7Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an 710071, ChinaLife Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an 710071, ChinaLife Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an 710071, ChinaLife Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an 710071, ChinaLife Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an 710071, ChinaLife Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an 710071, ChinaLife Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an 710071, ChinaLife Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an 710071, ChinaBioluminescence 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.http://dx.doi.org/10.1155/2011/203537 |
| spellingShingle | Xiaowei He Yanbin Hou Duofang Chen Yuchuan Jiang Man Shen Junting Liu Qitan Zhang Jie Tian Sparse Regularization-Based Reconstruction for Bioluminescence Tomography Using a Multilevel Adaptive Finite Element Method International Journal of Biomedical Imaging |
| title | Sparse Regularization-Based Reconstruction for Bioluminescence Tomography Using a Multilevel Adaptive Finite Element Method |
| title_full | Sparse Regularization-Based Reconstruction for Bioluminescence Tomography Using a Multilevel Adaptive Finite Element Method |
| title_fullStr | Sparse Regularization-Based Reconstruction for Bioluminescence Tomography Using a Multilevel Adaptive Finite Element Method |
| title_full_unstemmed | Sparse Regularization-Based Reconstruction for Bioluminescence Tomography Using a Multilevel Adaptive Finite Element Method |
| title_short | Sparse Regularization-Based Reconstruction for Bioluminescence Tomography Using a Multilevel Adaptive Finite Element Method |
| title_sort | sparse regularization based reconstruction for bioluminescence tomography using a multilevel adaptive finite element method |
| url | http://dx.doi.org/10.1155/2011/203537 |
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