Research Papers: General

Laplacian manifold regularization method for fluorescence molecular tomography

[+] Author Affiliations
Xuelei He, Xiaodong Wang, Huangjian Yi, Yanrong Chen, Xu Zhang, Xiaowei He

Northwest University, School of Information Sciences and Technology, Xi’an, China

Jingjing Yu

Shaanxi Normal University, School of Physics and Information Technology, Xi’an, China

J. Biomed. Opt. 22(4), 045009 (Apr 21, 2017). doi:10.1117/1.JBO.22.4.045009
History: Received December 7, 2016; Accepted April 7, 2017
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Abstract.  Sparse regularization methods have been widely used in fluorescence molecular tomography (FMT) for stable three-dimensional reconstruction. Generally, 1-regularization-based methods allow for utilizing the sparsity nature of the target distribution. However, in addition to sparsity, the spatial structure information should be exploited as well. A joint 1 and Laplacian manifold regularization model is proposed to improve the reconstruction performance, and two algorithms (with and without Barzilai–Borwein strategy) are presented to solve the regularization model. Numerical studies and in vivo experiment demonstrate that the proposed Gradient projection-resolved Laplacian manifold regularization method for the joint model performed better than the comparative algorithm for 1 minimization method in both spatial aggregation and location accuracy.

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© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Xuelei He ; Xiaodong Wang ; Huangjian Yi ; Yanrong Chen ; Xu Zhang, et al.
"Laplacian manifold regularization method for fluorescence molecular tomography", J. Biomed. Opt. 22(4), 045009 (Apr 21, 2017). ; http://dx.doi.org/10.1117/1.JBO.22.4.045009


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