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Proceedings Paper

An efficient reconstruction method for bioluminescence tomography based on two-step iterative shrinkage approach
Author(s): Wei Guo; Kebin Jia; Jie Tian; Dong Han; Xueyan Liu; Ping Wu; Jinchao Feng; Xin Yang
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Paper Abstract

Among many molecular imaging modalities, Bioluminescence tomography (BLT) is an important optical molecular imaging modality. Due to its unique advantages in specificity, sensitivity, cost-effectiveness and low background noise, BLT is widely studied for live small animal imaging. Since only the photon distribution over the surface is measurable and the photo propagation with biological tissue is highly diffusive, BLT is often an ill-posed problem and may bear multiple solutions and aberrant reconstruction in the presence of measurement noise and optical parameter mismatches. For many BLT practical applications, such as early detection of tumors, the volumes of the light sources are very small compared with the whole body. Therefore, the L1-norm sparsity regularization has been used to take advantage of the sparsity prior knowledge and alleviate the ill-posedness of the problem. Iterative shrinkage (IST) algorithm is an important research achievement in a field of compressed sensing and widely applied in sparse signal reconstruction. However, the convergence rate of IST algorithm depends heavily on the linear operator. When the problem is ill-posed, it becomes very slow. In this paper, we present a sparsity regularization reconstruction method for BLT based on the two-step iterated shrinkage approach. By employing Two-step strategy of iterative reweighted shrinkage (IRS) to improve IST, the proposed method shows faster convergence rate and better adaptability for BLT. The simulation experiments with mouse atlas were conducted to evaluate the performance of proposed method. By contrast, the proposed method can obtain the stable and comparable reconstruction solution with less number of iterations.

Paper Details

Date Published: 3 March 2012
PDF: 6 pages
Proc. SPIE 8313, Medical Imaging 2012: Physics of Medical Imaging, 83133Y (3 March 2012); doi: 10.1117/12.911158
Show Author Affiliations
Wei Guo, Beijing Univ. of Technology (China)
Kebin Jia, Beijing Univ. of Technology (China)
Jie Tian, Institute of Automation (China)
Dong Han, Institute of Automation (China)
Northeastern Univ. (China)
Xueyan Liu, Northeastern Univ. (China)
Ping Wu, Institute of Automation (China)
Jinchao Feng, Beijing Univ. of Technology (China)
Xin Yang, Institute of Automation (China)


Published in SPIE Proceedings Vol. 8313:
Medical Imaging 2012: Physics of Medical Imaging
Norbert J. Pelc; Robert M. Nishikawa; Bruce R. Whiting, Editor(s)

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