Micro-modulated luminescence tomography by Wenxiang Cong, Fenglin Liu, Chao Wang, Ge Wang
Imaging depth of optical microscopy has been fundamentally limited to millimeter or sub-millimeter due to light scattering. X-ray microscopy can resolve spatial details of few microns deeply inside a sample but the contrast resolution is still inadequate to depict heterogeneous features at cellular or sub-cellular levels. To enhance and enrich biological contrast at large imaging depth, various nanoparticles are introduced and become essential to basic research and molecular medicine. Nanoparticles can be functionalized as imaging probes, similar to fluorescent and bioluminescent proteins. LiGa5O8:Cr3+ nanoparticles were recently synthesized to facilitate luminescence energy storage with x-ray pre-excitation and the subsequently stimulated luminescence emission by visible/near-infrared (NIR) light. In this paper, we suggest a micro-modulated luminescence tomography (MLT) approach to quantify a nanophosphor distribution in a thick biological sample with high resolution. Our numerical simulation studies demonstrate the feasibility of the proposed approach.
the algorithm used was detailed in this behins a paywall paper: A few-view reweighted sparsity hunting (FRESH) method for CT image reconstruction by Ming Chang, Liang Li, Zhiqiang Chen, Yongshun Xiao, Li Zhang, Ge Wang
In recent years, the total variation (TV) minimization method has been widely used for compressed sensing (CS) based CT image reconstruction. In this paper, we propose a few-view reweighted sparsity hunting (FRESH) method for CT image reconstruction, and demonstrate the superior performance of this method. Specifically, the key of the purposed method is that a reweighted total variation (RwTV) measure is used to characterize image sparsity in the cost function, outperforming the conventional TV counterpart. To solve the RwTV minimization problem efficiently, the Split-Bregman method and other state-of-the-art L1 optimization methods are compared. Inspired by the fast iterative shrinkage/thresholding algorithm (FISTA), a predication step is incorporated for fast computation in the Split-Bregman framework. Extensive numerical experiments have shown that our FRESH approach performs significantly better than competing algorithms in terms of image quality and convergence speed for few-view CT. High-quality images were reconstructed by our FRESH method after 250 iterations using only 15 few-view projections of the Forbild head phantom while other competitors needed more than 800 iterations. Remarkable improvements in details in the experimental evaluation using actual sheep thorax data further indicate the potential real-world application of the FRESH method.
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