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Background: 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 is important. Purpose: 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 of Transformer. Methods: 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 vertical gradients. Results: 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. Conclusions: 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 quite well. [ABSTRACT FROM AUTHOR]

Spectral computed tomography has received considerable interest in recent years since spectral measurements contain much richer information about the object of interest. In spectral computed tomography, we are interested in the energy channel-wise reconstructions of the object. However, such reconstructions suffer from a low signal-to-noise ratio and share the challenges of conventional low-dose computed tomography such as ring artifacts. Ring artifacts arise from errors in the flat fields and can significantly degrade the quality of the reconstruction. We propose an extended flat-field model that exploits high correlation in the spectral flat fields to reduce ring artifacts in channel-wise reconstructions. The extended model relies on the assumption that the spectral flat fields can be well-approximated by a low-rank matrix. Our proposed model works directly on the spectral flat fields and can be combined with any existing reconstruction model, e.g. filtered back projection and iterative methods. The proposed model is validated on a neutron data set. The results show that our method successfully diminishes ring artifacts and improves the quality of the reconstructions. Moreover, the results indicate that our method is robust; it only needs a single spectral flat-field image, whereas existing methods need multiple spectral flat-field images to reach a similar level of ring reduction. [ABSTRACT FROM AUTHOR]

The article presents the results of research in the field of computational X-ray tomography, obtained within the framework of the scientific school, by Doctor of Engineering, Corresponding Member of the Russian Academy of Sciences V.L. Arlazarov, on artificial intelligence. The field of computed tomography, which is relatively young for the school, arose as a result of a combination of combinatorial optimization approaches, training neural network models, image processing, and solving the problem of stopping information acquisition. Thanks to the accumulated experience, four areas of research can be distinguished: fast algorithms for tomographic reconstruction, scanning protocols for monitored tomographic reconstruction, neural network approaches to reconstruction, as well as methods for suppressing artifacts and distortions that occur in tomographic reconstructions. This article reflects the main successes and achievements obtained in these areas, which are demonstrated using the example of specific applied solutions. [ABSTRACT FROM AUTHOR]

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.

Spectral computed tomography has received considerable interest in recent years since spectral measurements contain much richer information about the object of interest. In spectral computed tomography, we are interested in the energy channel-wise reconstructions of the object. However, such reconstructions suffer from a low signal-to-noise ratio and share the challenges of conventional low-dose computed tomography such as ring artifacts. Ring artifacts arise from errors in the flat fields and can significantly degrade the quality of the reconstruction. We propose an extended flat-field model that exploits high correlation in the spectral flat fields to reduce ring artifacts in channel-wise reconstructions. The extended model relies on the assumption that the spectral flat fields can be well-approximated by a low-rank matrix. Our proposed model works directly on the spectral flat fields and can be combined with any existing reconstruction model, e.g. filtered back projection and iterative methods. The proposed model is validated on a neutron data set. The results show that our method successfully diminishes ring artifacts and improves the quality of the reconstructions. Moreover, the results indicate that our method is robust; it only needs a single spectral flat-field image, whereas existing methods need multiple spectral flat-field images to reach a similar level of ring reduction.

In computed tomography (CT) reconstruction, ring artifacts emerge from incorrectly normalized or defective detector elements. Correction algorithms often introduce blur or do not correctly accommodate the behaviour of those artifacts. Normalization errors stem from noise in the detector images during the normalization process and are always present to some degree. We propose a method for correcting ring artifacts from incorrectly normalized detector elements in the sinogram. Our approach compensates for errors both in the individual gain as well as offset of pixel values. We reduce blur by inferring gain and offset information for each pixel from its neighbors only in a subset of all projections. We show with datasets from real measurments that our method is effective at mitigating the shortcomings of purely offset based approaches and approaches using all projections. Furthermore, our method can be efficiently implemented compensating for most overhead times. Under usual circumstances, our method can be implemented to function with no additional time overhead at all. [ABSTRACT FROM AUTHOR]

The ring artifact is a typical artifact in X-ray Computed Tomography (CT) images, the existence of which can reduce the image's quality, change the structure and details and affects the interpretation of image information. This paper conducts research from two aspects: tomographic images and sinograms, and designs a dual-domain artifact suppression network. By introducing the Inception module and the residual module, the network's ability to extract features is improved, and the gradient diffusion is suppressed. By customizing the loss function, the network's recognition of ring artifacts is improved and the network's convergence is accelerated. Finally, the outputs of the dual-domain are fused based on the Nonsubsampled Contourlet Transform (NSCT) fusion theory to improve the quality of the image. In addition, three experiments of CT scanning are designed based on microfocus cone-beam CT system to verify the effectiveness. The model is trained and tested using real datasets. Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Mean Squared Error (MSE) are employed as evaluation indicators of image quality. Finally, the proposed method is compared with typical algorithms for artifact suppression. The experimental results show that the method effectively protects the structure information in CT images while suppressing ring artifacts to the greatest extent. The PSNR, SSIM, and MSE of the processed images can respectively reach 36.167 dB, 0.9847 and 6.87 × e -4. [ABSTRACT FROM AUTHOR]

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.

Ring artifacts are inevitable in microtomographic images. In Digital Rock workflow such defects might affect subsequent segmentation and flow simulation. We propose correction of ring artifacts in reconstructed microtomographic image by inpainting. Our blind inpainting method uses 3D convolutional network U-net. For the creation of training and validation datasets, we suggest an algorithm for transferring real ring artifacts to an arbitrary place of the undistorted slices of 8 big images of sandstones and sand. Parameters of the deep neural network and loss functions are analyzed. Loss function based on multi-scale structural similarity index (MS-SSIM) allows to achieve the best performance. Developed solution corrects ring artifacts perfectly from point of view visual assessment and outperforms existing inpainting methods according to quality metrics based on MS-SSIM and mean absolute error (MAE).

Purpose: 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. Methods: 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. Results: 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. Conclusions: 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. [ABSTRACT FROM AUTHOR]

The ring artifacts introduced by the defective pixels with non-linear responses in the high-resolution detector, have a great impact on subsequent processing and quantitative analysis of the reconstructed images. In this paper, a multistep method is proposed to suppress the ring artifacts of micro CT images, which firstly locates the positions of the defective pixels in the sinogram, and then corrects the corresponding value in the projections. Since the defective pixels always appear as vertical stripes in the sinogram, a horizontal curve is derived by summing the pixel values along vertical direction, thus the abrupt segments related to the defective stripes are enhanced notably, and a proportion coefficient based on the second derivative of the curve is taken as the indicator for the position and the severity of the defective pixels. Then, the detected defective pixels in the sinogram are transferred and relocated in the projections, an improved 3D block matching filtering (BM3D) algorithm is applied to restore the defective pixels in corresponding projection images. In the end, the tomographic images are reconstructed from the corrected projections. In the experiment, a small piece of the motherwort's rhizome and a part of a mouse's lung are imaged by micro-CT, and the result shows that, compared with the other four state-of-art methods, the proposed method has a great reduction on the ring artifacts of the reconstructed images, and makes less impact in spatial resolution and contrast in the same time. [ABSTRACT FROM AUTHOR]

Cone beam computed tomography (CBCT) is an important tool for clinical diagnosis and many industrial applications. However, ring artifacts usually appear in CBCT images, due to device responding inconsistence. This paper designs a generative adversarial network (GAN) to remove ring artifacts and meanwhile to retain important texture details in CBCT images. This method firstly transforms ring artifacts in Cartesian coordinates to stripe artifacts in polar coordinates, which is very helpful for removing ring artifacts. Then, we design a new loss function for GAN, including three parts: unidirectional relative total variation loss, perceptual loss and adversarial loss. Further, inspired by super-resolution generative adversarial networks, we use very deep residual networks for both generator and discriminator. Experimental results show that the proposed method is more effective for ring artifacts removal, compared to our baseline and some traditional methods. [ABSTRACT FROM AUTHOR]

Purpose: It often happens that cone‐beam corrupted tomography (CBCT) images have some ring artifacts due to the inconsistent response of detector pixels. Removing ring artifacts in CBCT images without impairing the image quality is critical. The purpose of this study is to implement a whole new method of removing ring artifacts in CBCT images based on TV‐Stokes denoising equation and unidirectional total variation (UTV). Methods: This method is based on the polar coordinates, where the ring artifacts are shown as horizontal parallel stripes. To begin with, we design a UTV model with the constraint of zero divergence condition to only smooth vertical tangent vectors, and keep horizontal tangent vectors unchanged. In turn, the corresponding smooth normal vectors can be obtained. Next, in order to reconstruct the clean image that fits the obtained normal vectors, the UTV model about the potential clean image is added to the original TV‐Stokes denoising equation. Results: Our method was applied to simulated data and real corrupted data to verify its performance. High‐quality corrected images without ring artifacts were obtained. In the simulated experiments, our method is not only able to obtain the most complete corrected images, but also capable of acquiring the best quantitative assessments among several different methods. In the experiments of real data, the proposed method was effective in removing ring artifacts and in preserving the original details. Comparative results on several experiments illustrated that our algorithm corrects ring artifacts effectively and outperforms the two compared methods on objective indices and subjective image quality. Conclusions: Benefiting from the TV‐Stokes equation and the UTV model, the proposed scheme can be effectively applied to remove ring artifacts in CBCT images, simultaneously preserving the original image details and structure well. Furthermore, this new algorithm can be applied directly on reconstructed images, thus eliminating the requirement for additional imaging data. [ABSTRACT FROM AUTHOR]

Objective. Computed tomography (CT) is a widely employed imaging technology for disease detection. However, CT images often suffer from ring artifacts, which may result from hardware defects and other factors. These artifacts compromise image quality and impede diagnosis. To address this challenge, we propose a novel method based on dual contrast learning image style transformation network model (DCLGAN) that effectively eliminates ring artifacts from CT images while preserving texture details. Approach . Our method involves simulating ring artifacts on real CT data to generate the uncorrected CT (uCT) data and transforming them into strip artifacts. Subsequently, the DCLGAN synthetic network is applied in the polar coordinate system to remove the strip artifacts and generate a synthetic CT (sCT). We compare the uCT and sCT images to obtain a residual image, which is then filtered to extract the strip artifacts. An inverse polar transformation is performed to obtain the ring artifacts, which are subtracted from the original CT image to produce a corrected image. Main results. To validate the effectiveness of our approach, we tested it using real CT data, simulated data, and cone beam computed tomography images of the patient's brain. The corrected CT images showed a reduction in mean absolute error by 12.36 Hounsfield units (HU), a decrease in root mean square error by 18.94 HU, an increase in peak signal-to-noise ratio by 3.53 decibels (dB), and an improvement in structural similarity index by 9.24%. Significance. These results demonstrate the efficacy of our method in eliminating ring artifacts and preserving image details, making it a valuable tool for CT imaging., (© 2023 Institute of Physics and Engineering in Medicine.)

In practice, mis-calibrated detector pixels give rise to wide and faint ring artifacts in the reconstruction image of the In-line phase-contrast computed tomography (IL-PC-CT). Ring artifacts correction is essential in IL-PC-CT. In this study, a novel method of wide and faint ring artifacts correction was presented based on combining TV-L1 model with guided image filtering (GIF) in the reconstruction image domain. The new correction method includes two main steps namely, the GIF step and the TV-L1 step. To validate the performance of this method, simulation data and real experimental synchrotron data are provided. The results demonstrate that TV-L1 model with GIF step can effectively correct the wide and faint ring artifacts for IL-PC-CT. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) plays an important role in digital rock analysis, which is a new prospective technique for oil and gas industry. But the artifacts in CT images will influence the accuracy of the digital rock model. In this study, we proposed and demonstrated a novel method to restore detector-unit-dependent functions for polychromatic projection calibration by scanning some simple shaped reference samples. As long as the attenuation coefficients of the reference samples are similar to the scanned object, the size or position is not needed to be exactly known. Both simulated and real data were used to verify the proposed method. The results showed that the new method reduced both beam hardening artifacts and ring artifacts effectively. Moreover, the method appeared to be quite robust. [ABSTRACT FROM AUTHOR]

Ring artifacts have been studied for X–ray based Computed Tomography (CT) systems but not on γ-ray based in-situ applications. This paper discusses application of recently proposed automatic ring artifact reduction method (Yoon et al., 2016) on previously obtained experimental projection data from a γ-ray based Industrial Process Tomography (IPT) system for a prototype catalytic column. Studies include qualitative and quantitative evaluation of the method. It is observed that ring artifacts are suppressed without loss of significant information in γ-ray PT images. [ABSTRACT FROM AUTHOR]

Reconstruction algorithms in X-ray tomography provide a user with a set of images. For many scientific problems, these images should be segmented. Ring artifacts are the most noticeable artifacts that do not allow a scientist to achieve acceptable segmentation especially in case of noisy input data. Several ring artifact suppression methods based on theory of ill-posed problems exist. In this letter, the basic ring artifact suppression algorithm is rewritten as a 1-D filter, so it does not require separate data processing and can be combined with standard filtering used in filtered backprojection (FBP) algorithms. [ABSTRACT FROM PUBLISHER]

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• We fully utilized the advantages of both regularization and deep learning and embedded them into the PnP framework to develop a novel image restoration model. • The proposed method can handle structural noise and random noise simultaneously. • An effective augmented Lagrange multiplier (ALM) algorithm is designed to optimize the proposed image restoration model. Synchrotron-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. There 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. Our 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. The 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. [ABSTRACT FROM AUTHOR]

Purpose: In using flat panel detectors (FPD) for cone beam computed tomography (CBCT), pixel gain variations may lead to structured nonuniformities in projections and ring artifacts in CBCT images. Such gain variations can be caused by change in detector entrance exposure levels or beam hardening, and they are not accounted by conventional flat field correction methods. In this work, the authors presented a method to identify isolated pixel clusters that exhibit gain variations and proposed a pixel gain correction (PGC) method to suppress both beam hardening and exposure level dependent gain variations. Methods: To modulate both beam spectrum and entrance exposure, flood field FPD projections were acquired using beam filters with varying thicknesses. "Ideal" pixel values were estimated by performing polynomial fits in both raw and flat field corrected projections. Residuals were calculated by taking the difference between measured and ideal pixel values to identify clustered image and FPD artifacts in flat field corrected and raw images, respectively. To correct clustered image artifacts, the ratio of ideal to measured pixel values in filtered images were utilized as pixel-specific gain correction factors, referred as PGC method, and they were tabulated as a function of pixel value in a look-up table. Results: 0.035% of detector pixels lead to clustered image artifacts in flat field corrected projections, where 80% of these pixels were traced back and linked to artifacts in the FPD. The performance of PGC method was tested in variety of imaging conditions and phantoms. The PGC method reduced clustered image artifacts and fixed pattern noise in projections, and ring artifacts in CBCT images Conclusions: Clustered projection image artifacts that lead to ring artifacts in CBCT can be better identified with our artifact detection approach. When compared to the conventional flat field correction method, the proposed PGC method enables characterization of nonlinear pixel gain variations as a function of change in x-ray spectrum and intensity. Hence, it can better suppress image artifacts due to beam hardening as well as artifacts that arise from detector entrance exposure variation. [ABSTRACT FROM AUTHOR]

One of the challenges in the development of photon counting spectral computed tomography (CT) detectors is that the location of the energy thresholds tends to vary among detector elements. If not compensated for, this threshold variation leads to ring artifacts in the reconstructed images. In this paper, a framework is presented for the systematic comparison of different methods of compensating for inhomogeneities among detector elements in photon counting CT with multiple energy bins. Furthermore, we propose the use of an affine minimum mean square error estimator, calibrated against transmission measurements on different combinations of two materials, for inhomogeneity compensation. Using the framework developed here, this method is compared to two other compensation schemes, flatfielding using an air scan and signal-to-thickness calibration using a step wedge calibrator, in a simulation study. The results show that for all but the lowest studied level of threshold spread, the proposed method is superior to signal-to-thickness calibration, which in turn is superior to flatfielding. We also demonstrate that the effects of threshold variation can be countered to a large extent by substructuring each detector element into depth segments. [ABSTRACT FROM AUTHOR]

Abstract: High resolution tomographic images acquired with a digital X-ray detector are often degraded by the so called ring artifacts. In this paper, a detail analysis including the classification, detection and correction of these ring artifacts is presented. At first, a novel idea for classifying rings into two categories, namely type I and type II rings, is proposed based on their statistical characteristics. The defective detector elements and the dusty scintillator screens result in type I ring and the mis-calibrated detector elements lead to type II ring. Unlike conventional approaches, we emphasize here on the separate detection and correction schemes for each type of rings for their effective removal. For the detection of type I ring, the histogram of the responses of the detector elements is used and a modified fast image inpainting algorithm is adopted to correct the responses of the defective pixels. On the other hand, to detect the type II ring, first a simple filtering scheme is presented based on the fast Fourier transform (FFT) to smooth the sum curve derived form the type I ring corrected projection data. The difference between the sum curve and its smoothed version is then used to detect their positions. Then, to remove the constant bias suffered by the responses of the mis-calibrated detector elements with view angle, an estimated dc shift is subtracted from them. The performance of the proposed algorithm is evaluated using real micro-CT images and is compared with three recently reported algorithms. Simulation results demonstrate superior performance of the proposed technique as compared to the techniques reported in the literature. [Copyright &y& Elsevier]

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., (© 2021 Institute of Physics and Engineering in Medicine.)

Classical methods for X-ray computed tomography are based on the assumption that the X-ray source intensity is known, but in practice, the intensity is measured and hence uncertain. Under normal operating conditions, when the exposure time is sufficiently high, this kind of uncertainty typically has a negligible effect on the reconstruction quality. However, in time- or dose-limited applications such as dynamic CT, this uncertainty may cause severe and systematic artifacts known as ring artifacts. By carefully modeling the measurement process and by taking uncertainties into account, we derive a new convex model that leads to improved reconstructions despite poor quality measurements. We demonstrate the effectiveness of the methodology based on simulated and real data sets.

The imaging of objects using high-resolution detectors coupled to CT systems may be made challenging due to the presence of ring artifacts in the reconstructed data. Not only are the artifacts qualitatilvely distracting, they reduce the SNR of the reconstructed data and may lead to a reduction in the clinical utility of the image data. To address these challenges, we introduce a multistep algorithm that greatly reduces the impact of the ring artifacts on the reconstructed data through image processing in the sinogram space. First, for a single row of detectors corresponding to one slice, we compute the mean of every detector element in the row across all projection view angles and place the reciprocal values in a vector with length equal to the number of detector elements in a row. This vector is then multiplied with each detector element value for each projection view angle, obtaining a normalized or corrected sinogram. This sinogram is subtracted from the original uncorrected sinogram of the slice to obtain a difference map, which is then blurred with a median filter along the row direction. This blurred difference map is summed back to the corrected sinogram, to obtain the final sinogram, which can be back projected to obtain an axial slice of the scanned object, with a greatly reduced presence of ring artifacts. This process is done for each detector row corresponding to each slice. The performance of this algorithm was assessed using images of a mouse femur. These images were acquired using a micro-CT system coupled to a high-resolution CMOS detector. We found that the use of this algorithm led to an increase in SNR and a more uniform line-profile, as a result of the reduction in the presence of the ring artifacts.

Ring artifacts are undesirable and complicate the analysis and interpretation of microstructures in synchrotron X-ray microtomography. Here, we propose a new method to improve the image quality of an object by removing the ring artifacts and investigate the efficiency of this process with tomographic images of a dried Tenebrio molitor. In this method, before the tomographic reconstruction, ring artifacts were identified and located in the sinograms as line artifacts. Then, the identified line artifacts were corrected as single point noise via image processing of the original projections. Eventually, the corresponding line artifacts were removed, resulting in reduced ring artifacts in the reconstructed tomographic images. Simulations verified the efficiency of the proposed method. This method was successfully applied for the structural analysis of the insect T. molitor, showing superior performance in reducing ring artifacts in the tomographic image without noticeable loss of structural information.

Computed tomography (CT) plays an important role in digital rock analysis, which is a new prospective technique for oil and gas industry. But the artifacts in CT images will influence the accuracy of the digital rock model. In this study, we proposed and demonstrated a novel method to restore detector-unit-dependent functions for polychromatic projection calibration by scanning some simple shaped reference samples. As long as the attenuation coefficients of the reference samples are similar to the scanned object, the size or position is not needed to be exactly known. Both simulated and real data were used to verify the proposed method. The results showed that the new method reduced both beam hardening artifacts and ring artifacts effectively. Moreover, the method appeared to be quite robust.