X-rays image reconstruction using proximal algorithm and adapted TV regularization

Abdessalam Benammar,Redouane Drai,Aicha Allag,Wahiba Djerir,Tarek Boutkedjirt

doi:10.1016/j.matpr.2021.11.552

Abstract

Computed tomography (CT) aims to reconstruct an internal distribution of an object based on projection measurements. In the case of a limited number of projections, the reconstruction problem becomes significantly ill-posed. Practically, reconstruction algorithms play a crucial role in overcoming this problem. In the case of missing or incomplete data, and in order to improve the quality of the reconstruction image, the choice of a sparse regularisation by adding l1 norm is needed. The reconstruction problem is then based on using proximal operators. We are interested in the Douglas-Rachford method and employ total variation (TV) regularization. An efficient technique based on these concepts is proposed in this study. The primary goal is to achieve high- quality reconstructed images in terms of PSNR parameter and relative error. The numerical simulation results demonstrate that the suggested technique minimizes noise and artifacts while preserving structural information. The results are encouraging and indicate the effectiveness of the proposed strategy.

Highlights

Computed tomography (CT) aims to reconstruct an internal distribution of an object based on projection measurements
Compared to the filtered back-projection algorithm (FBP) method, in the case of limited number of projections, algorithms based on compressed sensing (CS), efficiently reconstruct images and reduce artefacts [4, 5]
In order to test the robustness of the reconstruction methods in a more realistic condition, we have considered a synthetic image from projected data corrupted by a Gaussian noise of a standard deviation σ=0.006 and σ=0.02 and a zero mean

Summary

Introduction

Computed tomography (CT) aims to reconstruct an internal distribution of an object based on projection measurements. [copyright information to be updated in production process] Keywords: Reconstruction, regularization, proximal method, X-rays, ill-posed problem; 1. Several strategies have been developed since the discovery of X-rays and their application in radiography Such us tomographic imaging, which allows for the visualization of the interior structure of objects. This is accomplished using the principle of computed tomography (CT) that allows visualization which should be similar to that of reality This reconstruction technique is based on mathematical methods. Image reconstruction using classical aproaches unsuitable for this category of problems, as it leads to unstable reconstruction due to the need for a priori information To overcome this problem, new researches suggest regularization methods that impose constraints on the solution [3].

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Methods

Conclusion