Removal Of Ring Artifacts Research Articles

Removal of ring artifacts in neutron tomography image slices

Removal of ring artifacts in neutron tomography image slices

Dual-beam X-ray nano-holotomography.

Nanotomography with hard X-rays is a widely used technique for high-resolution imaging, providing insights into the structure and composition of various materials. In recent years, tomographic approaches based on simultaneous illuminations of the same sample region from different angles by multiple beams have been developed at micrometre image resolution. Transferring these techniques to the nanoscale is challenging due to the loss in photon flux by focusing the X-ray beam. We present an approach for multi-beam nanotomography using a dual-beam Fresnel zone plate (dFZP) in a near-field holography setup. The dFZP generates two nano-focused beams that overlap in the sample plane, enabling the simultaneous acquisition of two projections from slightly different angles. This first proof-of-principle implementation of the dual-beam setup allows for the efficient removal of ring artifacts and noise using machine-learning approaches. The results open new possibilities for full-field multi-beam nanotomography and pave the way for future advancements in fast holotomography and artifact-reduction techniques.

Removing ring artifacts in CBCT images via Transformer with unidirectional vertical gradient loss.

Cone-beam computed tomography (CBCT), an important medical modality for disease detection and diagnosis, is currently widely used in clinical practice. However, due to the inconsistent response of CBCT detectors, the lack of proper calibration often leads to the occurrence of ring artifacts in CBCT-reconstructed images. These artifacts may affect physicians' assessment and diagnosis. Therefore, effective elimination of ring artifacts in CBCT images without degrading image quality isimportant. Given the pros and cons of existing methods for removing ring artifacts in CBCT images, this paper is devoted to designing a specific Transformer for this task, leveraging the global and local modeling ability ofTransformer. We design a loss function with dual-domain information fusion for the vanilla Transformer to correct ring artifacts in CBCT images. The method operates in image domain to predict artifact-free outputs and preserve more image details. Meanwhile, we design a tailored loss function incorporating polar domain optimization to remove ring artifacts more effectively. Specifically, an unidirectional gradient loss that constrains vertical gradients in polar domain is imposed, based on the geometric prior that in polar coordinates, ring artifacts predominately affect horizontal gradients while minimally influencing verticalgradients. We conduct extensive ablative and comparative experiments on CBCT/CT image set to validate the performance of the proposed method. First, four ablation experiments demonstrate the feasibility of our approach. Then, we compare our method with several classical methods and the latest state-of-the-arts, and our method achieves the highest quality of corrected images as well as the best evaluation metrics. In these experiments, 5332 CT images were used for training, and 550 CT images, and 500 real CBCT images were used for testing. The source code is available at https://github.com/shasha521/CBCT. Experimental results demonstrate that our method can significantly improve the effectiveness of ring artifact correction. By capitalizing on dual-domain information fusion and a customized loss function, the improved Transformer can not only effectively remove ring artifacts in CBCT images, but also preserve the details of original images quitewell.

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

Ring artifact is a common problem in Computed Tomography (CT), which can lead to inaccurate diagnoses and treatment plans. It can be caused by various factors such as detector imperfections, anti-scatter grids, or other nonuniform filters placed in the x-ray beam. Physics-based corrections for these x-ray source and detector non-uniformity, in general cannot completely get rid of the ring artifacts. Therefore, there is a need for a robust method that can effectively remove ring artifacts in the image domain while preservingdetails. This study aims to develop an effective method for removing ring artifacts from reconstructed CTimages. The proposed method starts by converting the reconstructed CT image containing ring artifacts into polar coordinates, thereby transforming these artifacts into stripes. Relative Total Variation is used to extract the image's overall structural information. For the efficient restoration of intricate details, we introduce Directional Gradient Domain Optimization (DGDO) and design objective functions that make use of both the image's gradient and its overall structure. Subsequently, we present an efficient analytical algorithm to minimize these objective functions. The image obtained through DGDO is then transformed back into Cartesian coordinates, finalizing the ring artifact correctionprocess. Through a series of synthetic and real-world experiments, we have effectively demonstrated the prowess of our proposed method in the correction of ring artifacts while preserving intricate details in reconstructed CT images. In a direct comparison, our method has exhibited superior visual quality compared to several previous approaches. These results underscore the remarkable potential of our approach for enhancing the overall quality and clinical utility of CTimaging. The proposed method offers an analytical solution for removing ring artifacts from CT images while preserving details. As ring artifacts are a common problem in CT imaging, this method has high practical value in the medical field. The proposed method can improve image quality and reduce the difficulty of disease diagnosis, thereby contributing to better patientcare.

Noise and blur removal from corrupted X-ray computed tomography scans: A multilevel and multiscale deep convolutional framework approach with synthetic training data (BAM SynthCOND)

Regardless of the experimental care practiced in acquiring X-ray computed tomography (XCT) data, artifacts might still exist, such as noise and blur. This is typical for fast XCT data acquisitions (e.g., in-situ investigations), or low-dose XCT. Such artifacts can complicate subsequent analysis of the data. Digital filters can moderately cure extensive artifacts. The selection of filter type, intensity, and order of application is not always straightforward. To tackle these problems, a complete sequential multilevel, multi-scale framework: BAM SynthCOND, employing newly designed deep convolutional neural networks (DCNNs), was formulated. Although data conditioning with neural networks is not uncommon, the main complication is that completely artifact-free XCT data for training do not exist. Thus, training data were acquired from an in-house developed library (BAM SynthMAT) capable of generating synthetic XCT material microstructures. Some novel DCNN architectures were introduced (2D/3D ACEnet_Denoise, 2D/3D ACEnet_Deblur) along with the concept of Assertive Contrast Enhancement (ACE) training, which boosts the performance of neural networks trained with continuous loss functions. The proposed methodology accomplished very good generalization from low resemblance synthetic training data. Indeed, denoising, sharpening (deblurring), and even ring artifact removal performance were achieved on experimental post-CT scans of challenging multiphase Al-Si Metal Matrix Composite (MMC) microstructures. The conditioning efficiencies were: 92% for combined denoising/sharpening, 99% for standalone denoising, and 95% for standalone sharpening. The results proved to be independent of the artifact intensity. We believe that the novel concepts and methodology developed in this work can be directly applied on the CT projections prior to reconstruction, or easily be extended to other imaging techniques such as: Microscopy, Neutron Tomography, Ultrasonics, etc.

Ring Artifacts Removal & Noise Reduction in X-Ray Computed Tomography Using Deep Learning

Analyzing data obtained from X-ray CT acquisitions may be challenging due to the presence of image artifacts. In recent years, deep learning has proven to be one of the fastest growing technologies in computer science and has become a confirmed trend in X-ray CT. In this study, we propose using deep learning-based algorithm to reduce noise and ring artifacts in CT images.

Detector shifting and deep learning based ring artifact correction method for low-dose CT.

In x-ray computed tomography (CT), the gain inconsistency of detector units leads to ring artifacts in the reconstructed images, seriously destroys the image structure, and is not conducive to image recognition. In addition, to reduce radiation dose and scanning time, especially photon counting CT, low-dose CT is required, so it is important to reduce the noise and suppress ring artifacts in low-dose CT images simultaneously. Deep learning is an effective method to suppress ring artifacts, but there are still residual artifacts in corrected images. And the feature recognition ability of the network for ring artifacts decreases due to the effect of noise in the low-dose CT images. In this paper, a method is proposed to achieve noise reduction and ring artifact removal simultaneously. To solve these problems, we propose a ring artifact correction method for low-dose CT based on detector shifting and deep learning in this paper. Firstly, at the CT scanning stage, the detector horizontally shifts randomly at each projection to alleviate the ring artifacts as front processing. Thus, the ring artifacts are transformed into dispersed noise in front processed images. Secondly, deep learning is used for dispersed noise and statistical noise reduction. Both simulation and real data experiments are conducted to evaluate the proposed method. Compared to other methods, the results show that the proposed method in this paper has better effect on removing ring artifacts in the low-dose CT images. Specifically, the RMSEs and SSIMs of the two sets of simulated and experiment data are better compared to the raw images significantly. The method proposed in this paper combines detector shifting and deep learning to remove ring artifacts and statistical noise simultaneously. The results show that the proposed method is able to get better performance.

Synchrotron microtomography image restoration via regularization representation and deep CNN prior

Background and objectiveSynchrotron-based X-ray microtomography (S-µCT) is a promising imaging technique that plays an important role in modern medical science. S-µCT systems often cause various artifacts and noises in the reconstructed CT images, such as ring artifacts, quantum noise, and electronic noise. In most situations, such noise and artifacts occur simultaneously, which results in a deterioration in the image quality and affects subsequent research. Due to the complexity of the distribution of these mixed artifacts and noise, it is difficult to restore the corrupted images. To address this issue, we propose a novel algorithm to remove mixed artifacts and noise from S-µCT images simultaneously. MethodsThere are two important aspects of our method. Regarding ring artifacts, because of their specific structural characteristics, regularization-based methods are more suitable; thus, low-rank tensor decomposition and total variation are utilized to represent their directional and locally piecewise smoothness properties. Moreover, to determine the implicit prior of the random noise, a convolutional neural network (CNN) based method is used. The advantages of traditional regularization and the deep CNN are then combined and embedded in a plug-and-play framework. Hence, an efficient image restoration algorithm is proposed to address the problem of mixed artifacts and noise in S-µCT images. ResultsOur proposed method was assessed by utilizing simulations and real data experiments. The qualitative results showed that the proposed method could effectively remove ring artifacts as well as random noise. The quantitative results demonstrated that the proposed method achieved almost the best results in terms of PSNR, SSIM and MAE compared to other methods. ConclusionsThe proposed method can serve as an effective tool for restoring corrupted S-µCT images, and it has the potential to promote the application of S-µCT.

Small animal photon counting cone-beam CT on a preclinical radiation research platform to improve radiation dose calculation accuracy

Objective. Cone beam CT (CBCT) in preclinical small animal irradiation platforms provides essential information for image guidance and radiation dose calculation for experiment planning. This project developed a photon-counting detector (PCD)-based multi(3)-energy (ME-)CBCT on a small animal irradiator to improve the accuracy of material differentiation and hence dose calculation, and compared to conventional flat panel detector (FPD)-based CBCT. Approach. We constructed a mechanical structure to mount a PCD to an existing preclinical irradiator platform and built a data acquisition pipeline to acquire x-ray projection data with a 100 kVp x-ray beam using three different energy thresholds in a single gantry rotation. We implemented an energy threshold optimization scheme to determine optimal thresholds to balance signal-to-noise ratios (SNRs) among energy channels. Pixel-based detector response calibration was performed to remove ring artifacts in reconstructed CBCT images. Feldkamp–Davis–Kress method was employed to reconstruct CBCT images and a total-variance regularization-based optimization model was used to decompose CBCT images into bone and water material images. We compared dose calculation results using PCD-based ME-CBCT with that of FPD-based CBCT. Main results. The optimal nominal energy thresholds were determined as 26, 56, and 90 keV, under which SNRs in a selected region-of-interest in the water region were 6.11, 5.91 and 5.93 in the three energy channels, respectively. Compared with dose calculation results using FPD-based CBCT, using PCD-based ME-CBCT reduced the mean relative error from 49.5% to 16.4% in bone regions and from 7.5% to 6.9% in soft tissue regions. Significance. PCD-based ME-CBCT is beneficial in improving radiation dose calculation accuracy in experiment planning of preclinical small animal irradiation researches.

Ring artifacts removal in X-ray-induced acoustic computed tomography.

X-ray-induced acoustic computed tomography (XACT) is a hybrid imaging modality for detecting X-ray absorption distribution via ultrasound emission. It facilitates imaging from a single projection X-ray illumination, thus reducing the radiation exposure and improving imaging speed. Nonuniform detector response caused by the interference between multichannel data acquisition for ring array transducers and amplifier systems yields ring artifacts in the reconstructed XACT images, which compromises the image quality. We propose model-based algorithms for ring artifacts corrected XACT imaging and demonstrate their efficacy on numerical and experimental measurements. The corrected reconstructions indicate significantly reduced ring artifacts as compared to their conventional counterparts.

Nonlinear Deviation CorrecTion for Ring-Artifact Removal With Cone-Beam Computed Tomography

In practice, the cone-beam CT images are usually tarnished by ring artifacts. To address this problem, in this paper, we proposed a ring-artifact removal technique based on the nonlinear deviation correction of pixel response for flat panel detector (FPD), named as Nonlinear Deviation CorrecTion (NDCT). Our proposed NDCT is a two-point fitting correction method of the local response from mean projection images. Its advantages lie in the following three aspects. First, it obtains correction coefficients by solving the correction equations based on the mean projection images without additional scanning, which improves efficiency. Second, it corrects all pixels of FPD at the same time, which eliminates the inconsistency of adjacent pixels and then the ring-artifact within three-dimensional CT image can be corrected. Third, the calculation is very simple so the correction speed is fast, which further improves efficiency in practice. Both numerical and real experiments showed that our NDCT method achieved excellent performance and fast speed in ring artifact removal.

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

High-resolution synchrotron radiation X-ray phase contrast microtomography (PC-μCT) images often suffer from severe ring artifacts, which are mainly caused by undesirable responses of detector elements. In the medical imaging field, the existence of ring artifacts can lead to degraded visual quality and can directly affect diagnosis accuracy. Thus, removing or at least effectively reducing ring artifacts is indispensable. The existing ring artifacts removal algorithms mainly focus on two-dimensional (matrix-based) priors, and these algorithms fail to consider correlations hidden in sequential computed tomography (CT) images. This paper proposed a novel three-dimensional (tensor-based) ring artifacts removal algorithm for synchrotron radiation X-ray PC-μCT images. In the sinogram domain, ring artifacts manifest as vertical stripe artifacts. From an image decomposition perspective, a degraded sinogram can be decomposed into a stripe artifacts component and an underlying clean sinogram component. The proposed algorithm is designed to detect and remove stripe artifacts from a degraded sinogram by fully identifying the characteristics of the two components. Specifically, for the stripe artifacts component, tensor Tucker decomposition is used to describe its low-rank character. For the underlying clean sinogram component, spatial-sequential total variation regularization is adopted to enhance the piecewise smoothness. Moreover, the Frobenius norm term is further used to model Gaussian noise. An efficient augmented Lagrange multiplier method is designed to solve the proposed optimization model. The proposed method is evaluated utilizing both simulations and real data containing different ring artifacts patterns. In the simulations, the human chest CT images are used for evaluating the proposed method. We compare the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and mean absolute error (MAE) results of our algorithm with the Naghia's method, the RRRTV method, the wavelet-FFT method, and the SDRSD-GIF method. The proposed method was also evaluated on real data from rat liver samples and rat tooth samples. Our proposed method outperforms the competing methods in terms of both qualitative and quantitative evaluation results. Additionally, the 3D visualization results were presented to make the ring artifacts removal effect more intuitive. The experimental results on simulations and real data clearly demonstrated that the proposed algorithm can significantly improve the quality of PC-μCT images compared with the existing popular algorithms, and it has great potential to promote the application of high-resolution imaging for visualizing biological tissues.

Brain MR Image Enhancement for Tumor Segmentation Using 3D U-Net.

MRI images are visually inspected by domain experts for the analysis and quantification of the tumorous tissues. Due to the large volumetric data, manual reporting on the images is subjective, cumbersome, and error prone. To address these problems, automatic image analysis tools are employed for tumor segmentation and other subsequent statistical analysis. However, prior to the tumor analysis and quantification, an important challenge lies in the pre-processing. In the present study, permutations of different pre-processing methods are comprehensively investigated. In particular, the study focused on Gibbs ringing artifact removal, bias field correction, intensity normalization, and adaptive histogram equalization (AHE). The pre-processed MRI data is then passed onto 3D U-Net for automatic segmentation of brain tumors. The segmentation results demonstrated the best performance with the combination of two techniques, i.e., Gibbs ringing artifact removal and bias-field correction. The proposed technique achieved mean dice score metrics of 0.91, 0.86, and 0.70 for the whole tumor, tumor core, and enhancing tumor, respectively. The testing mean dice scores achieved by the system are 0.90, 0.83, and 0.71 for the whole tumor, core tumor, and enhancing tumor, respectively. The novelty of this work concerns a robust pre-processing sequence for improving the segmentation accuracy of MR images. The proposed method overcame the testing dice scores of the state-of-the-art methods. The results are benchmarked with the existing techniques used in the Brain Tumor Segmentation Challenge (BraTS) 2018 challenge.

Ring artifact removal for differential phase-contrast X-ray computed tomography using a conditional generative adversarial network.

The integration process used as a pre-processing step in the reconstruction of differential phase-contrast X-ray CT (d-PCCT) causes the measurement noise to propagate throughout the projection image, which is leading to increased ring artifacts (RA) in the reconstructed image. It is difficult to eliminate the RA using conventional RA removal methods that were developed for the absorption-based CT field. We propose an effective method that can remove RA of d-PCCT images. The proposed method uses Laplacian images reconstructed from second-derivative projections of d-PCCT. This method is based on a conditional generative adversarial network (cGAN), whose loss function is designed by adding the L1- and L2-norm to the original cGAN. The training data were taken from a numerical phantom generated by a d-PCCT imaging simulator. To validate the applicability of the trained network, we tested its RA removal effect on test data from numerical phantoms generated randomly and actual experimental data. The results of numerical validation using numerical phantoms showed that the proposed method improved the RA removal effect compared to conventional methods. In addition, image comparison by visual evaluation showed that only the proposed method was able to remove RA while preserving original structures in the actual biological d-PCCT images. We proposed a cGAN-based method for RA removal that exploits the physical properties of d-PCCT. The proposed method was able to completely remove RA from d-PCCT images on both simulated data and biological data. We believe that this method is useful for the observation of various types of biological soft tissue.

Pre- and post-reconstruction digital image processing solutions for computed tomography with spectral photon counting detectors

Due to several issues still affecting pixelated photon-counting detector technology, such as e.g. inhomogeneities in the energy threshold calibration, polarization effects of high-Z semiconductor sensors and limitations of the hardware solutions aimed at compensating for the charge sharing issue, several artifacts corrupt the reconstructed images when adopting these detectors for computed tomography (CT). This article presents and discusses the most recurrent artifacts occurring in the raw images acquired with the innovative CdTe Pixirad-1/Pixie-III detector. By considering an experimental CT dataset of a suitable test object, digital image processing solutions for spectral CT imaging are here suggested and optimized. The proposed solutions consist of a pre-reconstruction despeckle filtering of each acquired projection and a post-reconstruction ring artifacts removal. The effects of these filters are quantitatively analyzed in terms of spatial resolution and signal-to-noise ratio. Finally, the suitability of the optimized image processing procedure is validated with two practical CT applications in which a plastinated small animal specimen is considered.

Removal of artifacts caused by grating imperfections in X-ray phase contrast tomography

Artifact removal algorithms are crucial for image quality in computed tomography. Many ring artifact removal algorithms have been proposed in conventional X-ray absorption computed tomography, but relatively fewer algorithms were suggested for differential X-ray phase contrast imaging. In this paper, a novel method is proposed to eliminate the ring artifacts caused by grating imperfections in X-ray phase contrast tomography. This method requires 360-degree projection data and exploits the anti-symmetry of conjugate rays to locate the artifact regions. Moreover, an interpolation scheme for sinogram-domain is proposed to correct artifacts. Experimental results show that the algorithm performs well in the case of severe artifacts.

Sparse-domain regularized stripe decomposition combined with guided-image filtering for ring artifact removal in propagation-based x-ray phase-contrast CT

Propagation-based x-ray phase-contrast computed tomography (PB-PCCT) images often suffer from severe ring artifacts. Ring artifacts are mainly caused by the nonuniform response of detector elements, and they can degrade image quality and affect the subsequent image processing and quantitative analyses. To remove ring artifacts in PB-PCCT images, a novel method combined sparse-domain regularized stripe decomposition (SDRSD) method with guided image filtering (GIF) was proposed. In this method, polar coordinate transformation was utilized to convert the ring artifacts to stripe artifacts. And then considering the directional and sparse properties of the stripe artifacts and the continuity characteristics of the sample, the SDRSD method was designed to remove stripe artifacts. However, for the SDRSD method, the presence of noise may destroy the edges of the stripe artifacts and lead to incomplete decomposition. Hence, a simple and efficient smoothing technique, namely GIF, was employed to overcome this issue. The simulations and real experiments demonstrated that the proposed method could effectively remove ring artifacts as well as preserve the structures and edges of the samples. In conclusion, the proposed method can serve as an effective tool to remove ring artifacts in PB-PCCT images, and it has high potential for promoting the biomedical and preclinical applications of PB-PCCT.

Empirical beam hardening and ring artifact correction for x-ray grating interferometry (EBHC-GI).

Talbot-Lau grating interferometry enables the use of polychromatic x-ray sources, extending the range of potential applications amenable to phase contrast imaging. However, these sources introduce beam hardening effects not only from the samples but also from the gratings. As a result, grating inhomogeneities due to manufacturing imperfections can cause spectral nonuniformity artifacts when used with polychromatic sources. Consequently, the different energy dependencies of absorption, phase, and visibility contrasts impose challenges that so far have limited the achievable image quality. The purpose of this work was to develop and validate a correction strategy for grating-based x-ray imaging that accounts for beam hardening generated from both the imaged object and the gratings. The proposed two-variable polynomial expansion strategy was inspired by work performed to address beam hardening from a primary modulator. To account for the multicontrast nature of grating interferometry, this approach was extended to each contrast to obtain three sets of correction coefficients, which were determined empirically from a calibration scan. The method's feasibility was demonstrated using a tabletop Talbot-Lau grating interferometer micro-computed tomography (CT) system using CT acquisitions of a water sample and a silicon sample, representing low and high atomic number materials. Spectral artifacts such as cupping and ring artifacts were quantified using mean squared error (MSE) from the beam-hardening-free target image and standard deviation within a reconstructed image of the sample. Finally, the model developed using the water sample was applied to a fixated murine lung sample to demonstrate robustness for similar materials. The water sample's absorption CT image was most impacted by spectral artifacts, but following correction to decrease ring artifacts, an 80% reduction in MSE and 57% reduction in standard deviation was observed. The silicon sample created severe artifacts in all contrasts, but following correction, MSE was reduced by 94% in absorption, 96% in phase, and 90% in visibility images. These improvements were due to the removal of ring artifacts for all contrasts and reduced cupping in absorption and phase images and reduced capping in visibility images. When the water calibration coefficients were applied to the lung sample, ring artifacts most prominent in the absorption contrast were eliminated. The described method, which was developed to remove artifacts in absorption, phase, and normalized visibility micro-CT images due to beam hardening in the system gratings and imaged object, reduced the MSE by up to 96%. The method depends on calibrations that can be performed on any system and does not require detailed knowledge of the x-ray spectrum, detector energy response, grating attenuation properties and imperfections, or the geometry and composition of the imaged object.

Post-processing method for the removal of mixed ring artifacts in CT images.

Ring artifacts seriously deteriorate the quality of CT images. Intensity-dependence of detector responses will result in intensity-dependent ring artifacts and time-dependence of CT hardware systems will result in time-dependent ring artifacts. However, only the intensity-dependent ring artifacts are taken into consideration in most post-processing methods. Therefore, the purpose of this study is to propose a general post-processing method, which has a significant removal effect on the intensity-dependent ring artifacts and the time-dependent ring artifacts. First in the proposed method, transform raw CT images into polar coordinate images, and the ring artifacts will manifest as stripe artifacts. Secondly, obtain structure images by smoothing the polar coordinate images and acquire texture images containing some details and stripe artifacts by subtracting the structure images from the polar coordinate images. Third, extract the stripe artifacts from the texture images using mean extraction and texture classification, and obtain the extracted ring artifacts by transforming the extracted stripe artifacts from polar coordinates into Cartesian coordinates. Finally, obtain corrected CT images by subtracting the extracted ring artifacts from the raw CT images, and iterate the corrected CT images in above steps until the ring artifacts extracted in the last iteration are weak enough. Simulation and real data show that the proposed method can remove the intensity-dependent ring artifacts and the time-dependent ring artifacts effectively while preserving image details and spatial resolution. In particular, real data prove that the method is suitable for new CT systems such as the photon counting CT.

Iterative Ring Artifact Removal Method for Helical Computed Tomography Scans.

In this article, a statistical-based iterative ring removal (IRR) algorithm that effectively removes ring artifacts generated by defective detector cells is proposed. The physical state of computed tomography (CT) detector elements can change dynamically owing to their temperature dependence and the varying irradiation caused by focal spot movements. This variation in the properties of cells may cause false pixel values in sinograms, resulting in rings or segments of rings in reconstructed images. In this article, the proposed algorithm is studied on clinical CT. Two patients were scanned using a clinical CT scanner (AnyScan SPECT/CT, Mediso). Artificial rings and band rings were generated on the real sinogram data to examine the algorithm in different cases. The method was performed also on real ring artifacts. The IRR can correct both single and band-like ring artifacts with one or more defective pixels. The proposed algorithm can detect the period when pixels contain false signals and only those periods are corrected. The IRR reduces ring artifacts, even in cases where low-contrast rings occur in the reconstructed image. This statistical correction method efficiently detects and corrects false pixel values in the projection data without causing new artifacts in the reconstructed image. The algorithm is less sensitive to its parameters.