A new iterative algorithm for ring artifact reduction in CT using ring total variation

Abstract

Purpose: In computed tomography (CT), miscalibrated or imperfect detector elements produce stripe artifacts in the sinogram. The stripe artifacts in Radon space are responsible for concentric ring artifacts in the reconstructed images. In this work, a novel optimization model is proposed to remove the ring artifacts in an iterative reconstruction procedure. Method: In the proposed optimization model, a novel ring total variation (RTV) regularization is developed to penalize the ring artifacts in the image domain. Moreover, to correct the sinogram, a new correcting vector is proposed to compensate for malfunctioning of detectors in the projection domain. The optimization problem is solved by using the alternating minimization scheme (AMS). In each iteration, the fidelity term along with the RTV regularization is solved using the alternating direction method of multipliers (ADMM) to find the image, and then the correcting coefficient vector is updated for certain detectors according to the obtained image. Because the sinogram and the image are simultaneously updated, the proposed method basically performs in both image and sinogram domains. Results: The proposed method is evaluated using both simulated and physical phantom datasets containing different ring artifact patterns. In the simulated datasets, the Shepp-Logan phantom, a real chest scan image and a noisy low-contrast phantom are considered for the performance evaluation of our method. We compare the quantitative root mean square error (RMSE) and structural similarity (SSIM) results of our algorithm with wavelet-Fourier sinogram filtering method by Munch et al., the ring artifact reduction method by Brun et al., and the TV-based ring correction method by Paleo and Mirone. Our proposed method is also evaluated using a physical phantom dataset where strong ring artifacts are manifest due to the miscalibration of a large number of detectors. Our proposed method outperforms the competing methods in terms of both qualitative and quantitative evaluation results. Conclusion: The experimental results in both simulated and physical phantom datasets show that the proposed method achieves the state-of-the-art ring artifact reduction performance in terms of RMSE, SSIM, and subjective visual quality.

3D ring artifacts removal algorithm combined low‐rank tensor decomposition with spatial–sequential total variation regularization and its application in phase‐contrast microtomography

“…Four state‐of‐the‐art methods were used for comparison, including Nghia's method, 12 the RRRTV method, 13 the wavelet–FFT method, 8 and the SDRSD‐GIF method 30 . The detailed description of these four ring artifacts removal methods and corresponding main parameters are shown in Table 1.…”

“…classified stripe artifact by comparing the intensity profile of defective pixels with their adjacent good pixels, and proposed corresponding methods for tackling them 12 . In 2019, a new iterative algorithm for ring artifact reduction using ring total variation (named the RRRTV method) was proposed by Zhang et al 13 …”

“…11 In the sinogram domain, Nghia et al classified stripe artifact by comparing the intensity profile of defective pixels with their adjacent good pixels, and proposed corresponding methods for tackling them. 12 In 2019, a new iterative algorithm for ring artifact reduction using ring total variation (named the RRRTV method) was proposed by Zhang et al 13 Recently, deep learning technology has been widely utilized in computer vision processing and has achieved ideal results. Wang et al applied a generative adversarial networkbased method to eliminate ring artifacts in CT images.…”

Sparsity-based method for ring artifact elimination in computed tomography

“…An optimization method based on TV and dictionary learning was investigated to correct the ring artifact [ 7 ]. Recently, an algorithm for the ring artifact reduction was proposed using ring TV and correction coefficient [ 8 ]. The proposed method follows this class of methods, where removing the ring artifact during the reconstruction process does not require any data modification before or after the reconstruction.…”

“…In this paper, inspired by the compressed sensing (CS) theory [3], we propose a software-based method to estimate and eliminate the ring artifact during the image reconstruction process. Some sparsity-based methods were presented to suppress the ring artifacts [4][5][6][7][8]. In this work, we introduce using sparsityinduced norms like ℓ 0 , Huber ℓ 0 , and Huber ℓ 1 in ring artifact removal through the reconstruction steps.…”