Data sinogram sparse reconstruction based on steering kernel regression and filtering strategies

Miguel A. Marquez; Edson Mojica; Henry Arguello

doi:10.1117/12.2224385

12 May 2016 Data sinogram sparse reconstruction based on steering kernel regression and filtering strategies

Miguel A. Marquez, Edson Mojica, Henry Arguello

Proceedings Volume 9847, Anomaly Detection and Imaging with X-Rays (ADIX); 98470Z (2016) https://doi.org/10.1117/12.2224385
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

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.

Citation Download Citation

Miguel A. Marquez, Edson Mojica, and 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); https://doi.org/10.1117/12.2224385

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