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Morphological reconstruction of fluorescence molecular tomography based on nonlocal total variation regularization for tracer distribution in glioma
Author(s): Hui Meng; Yuan Gao; Kun Wang; Jie Tian
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Paper Abstract

The high sensitivity and low cost of fluorescence imaging enables fluorescence molecular tomography (FMT) as a powerful noninvasive technique in applications of tracer distribution visualization. With the development of targeted fluorescence tracer, FMT has been widely used to localize the tumor. However, the visualization of probe distribution in tumor and surrounding region is still a challenge for FMT reconstruction. In this study, we proposed a novel nonlocal total variation (NLTV) regularization method, which is based on structure prior information. To build the NLTV regularization term, we consider the first order difference between the voxel and its four nearest neighbors. Furthermore, we assume that the variance of fluorescence intensity between any two voxels has a non-linear inverse correlation with their Gaussian distance. We adopted the Gaussian distance between two voxels as the weight of the first order difference. Meanwhile, the split Bregman method was applied to minimize the optimization problem. To evaluate the robustness and feasibility of our proposed method, we designed numerical simulation experiments and in vivo experiments of xenograft orthotopic glioma models. The ex vivo fluorescent images of cryoslicing specimens were regarded as gold standard of probe distribution in biological tissue. The results demonstrated that the proposed method could recover the morphology of the tracer distribution more accurately compared with fast iterated shrinkage (FIS) method, Split Bregman-resolved TV (SBRTV) regularization method and Gaussian weighted Laplace prior (GWLP) regularization method. These results demonstrate the potential of our method for in vivo visualization of tracer distribution in xenograft orthotopic glioma models.

Paper Details

Date Published: 28 February 2019
PDF: 6 pages
Proc. SPIE 10859, Visualizing and Quantifying Drug Distribution in Tissue III, 108590J (28 February 2019); doi: 10.1117/12.2507622
Show Author Affiliations
Hui Meng, Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Yuan Gao, Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Kun Wang, Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Beijing Key Lab. of Molecular Imaging (China)
Jie Tian, Institute of Automation (China)
Beijing Key Lab. of Molecular Imaging (China)
Beihang Univ. (China)


Published in SPIE Proceedings Vol. 10859:
Visualizing and Quantifying Drug Distribution in Tissue III
Kin Foong Chan; Conor L. Evans, Editor(s)

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