As a preclinical imaging modality, bioluminescence tomography (BLT) is designed to locate and quantify threedimensional (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.
KEYWORDS: Reconstruction algorithms, Signal to noise ratio, Chemical species, In vivo imaging, Tomography, Luminescence, Fluorescence tomography, Mouse models, Detection and tracking algorithms, Tumors
As a promising tomographic method in preclinical research, fluorescence molecular tomography (FMT) can obtain real-time three-dimensional (3D) visualization for in vivo studies. However, because of the ill-posed nature and sensitivity to noise of the inverse problem, it remains challenging for effective and robust reconstruction of fluorescent probe distribution in animals. In this study, we present a two-stage matching pursuit (TSMP) method. The iterative process is divided into two processes: In the first stage, we iterate several times using the OMP algorithm to improve the accuracy of the support set, which is because most of the atoms selected by the OMP algorithm are accurate. In the second stage, we use CoSaMP algorithm to iterative. The initial input of the second stage is the residual and atom obtained by the first stage OMP algorithm, which can change the dependence of CoSaMP to sparsity. Meanwhile, considering the time of reconstruction, we set the iterative times of the first stage to K/2 (K is the sparisty). Because of the accuracy of the initial output and the choice of atomic criteria, the proposed algorithm has better performance than OMP and CoSaMP algorithm. The result of numerical simulation show that TSMP method can not only achieves accurate and desirable fluorescent source reconstruction, but also demonstrates enhanced robustness to noise.
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