12 May 2016 Data sinogram sparse reconstruction based on steering kernel regression and filtering strategies
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Abstract
Computed tomography images have an impact in many applications such as medicine, and others. Recently, compressed sensing-based acquisition strategies have been proposed in order to reduce the x-ray radiation dose. However, these methods lose critical information of the sinogram. In this paper, a reconstruction method of sparse measurements from a sinogram is proposed. The proposed approach takes advantage of the redundancy of similar patches in the sinogram, and estimates a target pixel using a weighted average of its neighbors. Simulation results show that the proposed method obtained a gain up to 2 dB with respect to an l1 minimization algorithm.
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Miguel A. Marquez, Edson Mojica, Henry Arguello, "Data sinogram sparse reconstruction based on steering kernel regression and filtering strategies", Proc. SPIE 9847, Anomaly Detection and Imaging with X-Rays (ADIX), 98470Z (12 May 2016); doi: 10.1117/12.2224385; https://doi.org/10.1117/12.2224385
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