Sparse reconstruction for fluorescence molecular tomography via a fast iterative algorithm
Fluorescence molecular tomography (FMT) is a fast-developing optical imaging modality that has great potential in early diagnosis of disease and drugs development. However, reconstruction algorithms have to address a highly ill-posed problem to fulfill 3D reconstruction in FMT. In this contribution,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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World Scientific Publishing
2014-05-01
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| Series: | Journal of Innovative Optical Health Sciences |
| Subjects: | |
| Online Access: | http://www.worldscientific.com/doi/pdf/10.1142/S1793545814500084 |
| _version_ | 1819138803622739968 |
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| author | Jingjing Yu Jingxing Cheng Yuqing Hou Xiaowei He |
| author_facet | Jingjing Yu Jingxing Cheng Yuqing Hou Xiaowei He |
| author_sort | Jingjing Yu |
| collection | DOAJ |
| description | Fluorescence molecular tomography (FMT) is a fast-developing optical imaging modality that has great potential in early diagnosis of disease and drugs development. However, reconstruction algorithms have to address a highly ill-posed problem to fulfill 3D reconstruction in FMT. In this contribution, we propose an efficient iterative algorithm to solve the large-scale reconstruction problem, in which the sparsity of fluorescent targets is taken as useful a priori information in designing the reconstruction algorithm. In the implementation, a fast sparse approximation scheme combined with a stage-wise learning strategy enable the algorithm to deal with the ill-posed inverse problem at reduced computational costs. We validate the proposed fast iterative method with numerical simulation on a digital mouse model. Experimental results demonstrate that our method is robust for different finite element meshes and different Poisson noise levels. |
| first_indexed | 2024-12-22T11:12:34Z |
| format | Article |
| id | doaj.art-f916f797c29744c998ee8708a7502a64 |
| institution | Directory Open Access Journal |
| issn | 1793-5458 1793-7205 |
| language | English |
| last_indexed | 2024-12-22T11:12:34Z |
| publishDate | 2014-05-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Journal of Innovative Optical Health Sciences |
| spelling | doaj.art-f916f797c29744c998ee8708a7502a642022-12-21T18:28:08ZengWorld Scientific PublishingJournal of Innovative Optical Health Sciences1793-54581793-72052014-05-01731450008-11450008-910.1142/S179354581450008410.1142/S1793545814500084Sparse reconstruction for fluorescence molecular tomography via a fast iterative algorithmJingjing Yu0Jingxing Cheng1Yuqing Hou2Xiaowei He3School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710062, P. R. ChinaSchool of Information Sciences and Technology, Northwest University, Xi'an 710069, P. R. ChinaSchool of Information Sciences and Technology, Northwest University, Xi'an 710069, P. R. ChinaSchool of Information Sciences and Technology, Northwest University, Xi'an 710069, P. R. ChinaFluorescence molecular tomography (FMT) is a fast-developing optical imaging modality that has great potential in early diagnosis of disease and drugs development. However, reconstruction algorithms have to address a highly ill-posed problem to fulfill 3D reconstruction in FMT. In this contribution, we propose an efficient iterative algorithm to solve the large-scale reconstruction problem, in which the sparsity of fluorescent targets is taken as useful a priori information in designing the reconstruction algorithm. In the implementation, a fast sparse approximation scheme combined with a stage-wise learning strategy enable the algorithm to deal with the ill-posed inverse problem at reduced computational costs. We validate the proposed fast iterative method with numerical simulation on a digital mouse model. Experimental results demonstrate that our method is robust for different finite element meshes and different Poisson noise levels.http://www.worldscientific.com/doi/pdf/10.1142/S1793545814500084Fluorescence molecular tomographysparse regularizationreconstruction algorithmleast absolute shrinkage and selection operator |
| spellingShingle | Jingjing Yu Jingxing Cheng Yuqing Hou Xiaowei He Sparse reconstruction for fluorescence molecular tomography via a fast iterative algorithm Journal of Innovative Optical Health Sciences Fluorescence molecular tomography sparse regularization reconstruction algorithm least absolute shrinkage and selection operator |
| title | Sparse reconstruction for fluorescence molecular tomography via a fast iterative algorithm |
| title_full | Sparse reconstruction for fluorescence molecular tomography via a fast iterative algorithm |
| title_fullStr | Sparse reconstruction for fluorescence molecular tomography via a fast iterative algorithm |
| title_full_unstemmed | Sparse reconstruction for fluorescence molecular tomography via a fast iterative algorithm |
| title_short | Sparse reconstruction for fluorescence molecular tomography via a fast iterative algorithm |
| title_sort | sparse reconstruction for fluorescence molecular tomography via a fast iterative algorithm |
| topic | Fluorescence molecular tomography sparse regularization reconstruction algorithm least absolute shrinkage and selection operator |
| url | http://www.worldscientific.com/doi/pdf/10.1142/S1793545814500084 |
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