Sparsity-based method for ring artifact elimination in computed tomography

Abstract

Ring artifact elimination is one of the popular problems in computed tomography (CT). It appears in the reconstructed image in the form of bright or dark patterns of concentric circles. In this paper, based on the compressed sensing theory, we propose a method for eliminating the ring artifact during the image reconstruction. The proposed method is based on representing the projection data by a sum of two components. The first component contains ideal correct values, while the latter contains imperfect error values causing the ring artifact. We propose to minimize some sparsity-induced norms corresponding to the imperfect error components to effectively eliminate the ring artifact. In particular, we investigate the effect of using different sparse models, i.e. different sparsity-induced norms, on the accuracy of the ring artifact correction. The proposed cost function is optimized using an iterative algorithm derived from the alternative direction method of multipliers. Moreover, we propose improved versions of the proposed algorithms by incorporating a smoothing penalty function into the cost function. We also introduce angular constrained forms of the proposed algorithms by considering a special case as follows. The imperfect error values are constant over all the projection angles, as in the case where the source of ring artifact is the non-uniform sensitivity of the detector. Real data and simulation studies were performed to evaluate the proposed algorithms. Results demonstrate that the proposed algorithms with incorporating smoothing penalty and their angular constrained forms are effective in ring artifact elimination.

An analytical form of ring artifact correction for computed tomography based on directional gradient domain optimization

“…The proposed method underwent a comparative evaluation against existing techniques, namely the Combined Wavelet-Fourier Filtering method (referred to as M1), 28 the Iterative Image-domain Ring Artifact Removal method (referred to as M2), 24 and the Sparsity-based method for ring artifact elimination (referred to as M3). 34 The comparison was conducted based on visual quality as illustrated in Figure 4, including standard, detail-preserving, and noise-robust tests, and computation time as detailed in Table 1. Notably, M1 exhibited some ability to mitigate ring artifacts, yet it introduced wider and weaker additional ring artifacts during the detail-preserving test.…”

“…On-processing methods lie between preprocessing and post-processing methods and remove ring artifacts during the reconstruction process using specific regularizations based on compressed sensing theory. [30][31][32][33][34] However, these methods are typically limited by their high computational cost.…”

“…However, the method is limited in its ability to completely remove ring artifacts and preserve image details due to the simplicity of the mean value model used to distinguish ring artifacts and details. On‐processing methods lie between pre‐processing and post‐processing methods and remove ring artifacts during the reconstruction process using specific regularizations based on compressed sensing theory 30–34 . However, these methods are typically limited by their high computational cost.…”

Multibeam X-ray tomography optical system for narrow-energy-bandwidth synchrotron radiation

“…In our previous work, 18) we employed an advanced reconstruction method called ABD-CT (ABnormal data Detected CT) 23,24) which is able to overcome this drawback. In this work, we used a similar reconstruction method, which can be considered an extension of the previous method.…”

“…From a theoretical point of view, however, the power to identify and neglect the abnormal data is stronger with the L 0 -norm than with the L 1 -norm. 24) Therefore, we have used the L 0 -norm in this work.…”