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Journal of Biomedical Optics

Meshless reconstruction method for fluorescence molecular tomography based on compactly supported radial basis function
Author(s): Yu An; Jie Liu; Guanglei Zhang; Jinzuo Ye; Yamin Mao; Shixin Jiang; Wenting Shang; Yang Du; Chongwei Chi; Jie Tian
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Paper Abstract

Fluorescence molecular tomography (FMT) is a promising tool in the study of cancer, drug discovery, and disease diagnosis, enabling noninvasive and quantitative imaging of the biodistribution of fluorophores in deep tissues via image reconstruction techniques. Conventional reconstruction methods based on the finite-element method (FEM) have achieved acceptable stability and efficiency. However, some inherent shortcomings in FEM meshes, such as time consumption in mesh generation and a large discretization error, limit further biomedical application. In this paper, we propose a meshless method for reconstruction of FMT (MM-FMT) using compactly supported radial basis functions (CSRBFs). With CSRBFs, the image domain can be accurately expressed by continuous CSRBFs, avoiding the discretization error to a certain degree. After direct collocation with CSRBFs, the conventional optimization techniques, including Tikhonov, L1-norm iteration shrinkage (L1-IS), and sparsity adaptive matching pursuit, were adopted to solve the meshless reconstruction. To evaluate the performance of the proposed MM-FMT, we performed numerical heterogeneous mouse experiments and <italic<in vivo</italic< bead-implanted mouse experiments. The results suggest that the proposed MM-FMT method can reduce the position error of the reconstruction result to smaller than 0.4 mm for the double-source case, which is a significant improvement for FMT.

Paper Details

Date Published: 9 October 2015
PDF: 11 pages
J. Biomed. Opt. 20(10) 105003 doi: 10.1117/1.JBO.20.10.105003
Published in: Journal of Biomedical Optics Volume 20, Issue 10
Show Author Affiliations
Yu An, Beijing Jiaotong Univ. (China)
Chinese Academy of Sciences (China)
Jie Liu, Beijing Jiaotong Univ. (China)
Guanglei Zhang, Beijing Jiaotong Univ. (China)
Jinzuo Ye, Institute of Automation (China)
Yamin Mao, Chinese Academy of Sciences (China)
Shixin Jiang, Beijing Jiaotong Univ. (China)
Wenting Shang, Institute of Automation (China)
Yang Du, Institute of Automation (China)
Chongwei Chi, Institute of Automation (China)
Jie Tian, Institute of Automation (China)


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