CASIA OpenIR  > 中国科学院分子影像重点实验室
Gaussian weighted block sparse Bayesian learning strategy based on K-means clustering algorithm for accurate bioluminescence tomography in glioma
Yin, Lin; Wang, Kun; Tian, Jie
2021-02
Conference NameSPIE Medical Imaging
Conference Date2021.2.15-2021.2.19
Conference Place线上
Abstract

As a preclinical imaging modality, bioluminescence tomography (BLT) is designed to locate and quantify three-dimensional (3D) information of viable tumor cells in a living organism non-invasively. However, because of the ill-posedness of the inverse problem of reconstruction, BLT is hard to achieve the accurate recovery of the distribution of light sources. In this study, we proposed a Gaussian weighted block sparse Bayesian learning strategy based on K-means clustering algorithm (GBSBLK) for accurate BLT reconstruction. GBSBLK integrated the structured sparsity assumption, the K-means clustering strategy, and the block sparse Bayesian learning (BSBL) framework to overcome the over-smoothness and over-sparsity in BLT reconstructions, and without using the tumor segmentation from anatomical images as a priori. To better define the structured sparsity, we used the K-means clustering algorithm to directly cluster all the mesh points to get the K blocks. Furthermore, to prevent from over-smoothness of the light source, we applied Gaussian weighted distance prior to build the intra-block correlation matrix. At last, we used the BSBL framework to ensure the accuracy and robustness of the backward iterative computation. Results of both numerical simulations and in vivo experiments demonstrated that GBSBLK achieved the accurate quantitative analysis not only in tumor spatial positioning but also morphology recovery. We believe that GBSBLK can achieve great benefit in the application of BLT for quantitative analysis.

Sub direction classification医学影像处理与分析
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/44359
Collection中国科学院分子影像重点实验室
Corresponding AuthorTian, Jie
Affiliation1.the Key Laboratory of Molecular Imaging, Institute Of Automation, Chinese Academy of Sciences
2.School of Artificial Intelligence, University of Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yin, Lin,Wang, Kun,Tian, Jie. Gaussian weighted block sparse Bayesian learning strategy based on K-means clustering algorithm for accurate bioluminescence tomography in glioma[C],2021.
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