GDP-Net: Global Dependency-Enhanced Dual-Domain Parallel Network for Ring Artifact Removal.

DuDo-RAC: Dual-domain optimization for ring artifact correction in photon counting CT.

Photon-counting computed tomography (Photon counting CT) utilizes photon-counting detectors to precisely count incident photons and measure their energy. These detectors, compared to traditional energy integration detectors, provide better image contrast and material differentiation. However, Photon counting CT tends to show more noticeable ring artifacts due to limited photon counts and detector response variations, unlike conventional spiral CT. To comprehensively address this issue, we propose a novel feature shared multi-decoder network (FSMDN) that utilizes complementary learning to suppress ring artifacts in Photon counting CT images. Specifically, we employ a feature-sharing encoder to extract context and ring artifact features, facilitating effective feature sharing. These shared features are also independently processed by separate decoders dedicated to the context and ring artifact channels, working in parallel. Through complementary learning, this approach achieves superior performance in terms of artifact suppression while preserving tissue details. We conducted numerous experiments on Photon counting CT images with three-intensity ring artifacts. Both qualitative and quantitative results demonstrate that our network model performs exceptionally well in correcting ring artifacts at different levels while exhibiting superior stability and robustness compared to the comparison methods. In this paper, we have introduced a novel deep learning network designed to mitigate ring artifacts in Photon counting CT images. The results illustrate the viability and efficacy of our proposed network model as a new deep learning-based method for suppressing ring artifacts.

Ring artifacts in computed tomography (CT) images degrade image quality and obscure the true shapes of objects. While several correction methods have been developed, their performances are often task-dependent and not generally applicable. Here, we propose a novel method to reduce ring artifacts by calculating the ratio of adjacent detector elements in the projection data, termed the line-ratio. Our method estimates the sensitivity of each detector element and equalizes them in sinogram space. As a result, the stripe pattern can be effectively removed from sinogram data, thereby also removing ring artifacts from the reconstructed CT image. Numerical simulations were performed to evaluate and compare the performance of our method with that of conventional methods. We also tested our method experimentally and demonstrated that our method has superior performance to other methods.

Ring artifacts are a major obstacle that prevent the easy adoption of photon counting detectors (PCDs) into next-generation CT scanners. PCDs use a direct conversion semiconductor rather than an indirect conversion scintillator, but the stability of common direct conversion semiconductors (CdTe, CZT) remains limited. Small changes in sensitivity between neighboring pixels are amplified in CT reconstruction and result in ring artifacts in PCD CT images. To reduce these artifacts, we propose that pixels be dynamically autocalibrated to each other during the scan. This is done by measuring line integrals with two neighboring detector pixels and building a model of the differences between these two pixels. This can be done practically using flying focal spot, which minimizes the temporal offset between the two datasets. In this work, we demonstrate the proof of principle with a detector shift. The two resulting sinograms are used in an optimization problem that estimates gain factors on a per-pixel level. Preliminary results in a chicken thigh specimen and resolution phantom show strong reductions in ring artifact by about 75%.

Ring artifacts usually appear in photon counting computed tomography (PCCT) images, which may compromise image quality and cause non-uniformity bias. This study proposed a fast ring artifacts removal method by exploring the correlation from projections with different views for PCCT. This method has three advantages. First, our method only employ mean projection of current scan to correct projections without additional scans. Second, our method can correct the inconsistency of all detector pixels simultaneously without locating the inconsistent response pixels. Third, it can preserve reconstructed image details well without extra computational cost. Both numerical and preclinical experiments demonstrate the proposed method can suppress the ring artifacts very well than the competitors.

Ring artifacts are prevalent in 3D cone-beam computed tomography (CBCT) due to non-ideal responses of X-ray detectors, substantially affecting image quality and diagnostic reliability. Existing state-of-the-art (SOTA) ring artifact reduction (RAR) methods rely on supervised learning with large-scale paired CT datasets. While effective in-domain, supervised methods tend to struggle to fully capture the physical characteristics of ring artifacts, leading to pronounced performance drops in complex real-world acquisitions. Moreover, their scalability to 3D CBCT is limited by high memory demands. In this work, we propose Riner, a new unsupervised RAR method. Based on a theoretical analysis of ring artifact formation, we reformulate RAR as a multi-parameter inverse problem, where the non-ideal responses of X-ray detectors are parameterized as solvable physical variables. Using a new differentiable forward model, Riner can jointly learn the implicit neural representation of artifact-free images and estimate the physical parameters directly from CT measurements, without external training data. Additionally, Riner is memory-friendly due to its ray-based optimization, enhancing its usability in large-scale 3D CBCT. Experiments on both simulated and real-world datasets show Riner outperforms existing SOTA supervised methods.

Purpose: Defective pixels in a flat panel detector may be characterized by a nonlinear signal response drastically different from those in the neighboring pixels. They could lead to ring artifacts in cone beam CT. In this presentation, we will describe and demonstrate the use of a filter based calibration technique to detect these pixels. In addition, we will employ this technique as a flat field correction method to correct nonlinear signal response. Methods and Materials: To force the signal responses of all pixels to vary smoothly without sudden changes, we acquired images at fixed mAs but with various filtrations. Each filtered image is fitted to a smooth surface whose values are close to those of normal pixels but vary smoothly in the image. The ratios of the surface fit values to the original values were then computed on a pixel‐by‐pixel basis and used to map pixel values during subsequent image acquisition. This mapping would compensate for the nonlinear signal response associated with the defective pixels thus eliminating the ring artifacts. Using the surface fits as the reference, defective pixels were detected by automatic thresholding. Results: Using filter based calibration, defective (∼0.035 %) pixels were successfully detected and corrected for. Therefore, ring artifacts were largely eliminated in cone beam CT images. A reduction in patterned noise artifacts in the projection images was also observed. The automated surface fitting procedure was found to be robust. Conclusions: Although the conventional flat field correction addresses non‐uniform response across the detector, artifacts may still form as the pixel response varies with the beam quality and signal intensity. The filter based calibration procedure was successfully used to detect and correct for these artifacts.This work was supported in part by a research grant EB00117 from the NIH‐NIBIB and a research grant CA104759 from the NIH‐NCI.

Ring artifacts often appear in flat-panel detector based CT images due to the malfunction and mis-calibration of detector elements resulting in stripe artifacts in the sinogram data. In this paper, we propose an improved ring artifacts removal (IRAR) approach by combining multi-resolution analysis and weighted moving average filter methods. Specifically, the multi-resolution analysis method can suppress these high-intensity vertical stripes in the sinogram domain and the moving average filter method is effective to deal with the relatively high-intensity vertical stripes. Experimental results on the data acquired from a commercial micro-CT scanner show that the presented approach can achieve significant gains over the existing methods in the ring artifacts removal while preserving spatial resolution.

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.

In synchrotron x-ray tomography, systematic defects in certain detector elements can result in arc-shaped artifacts in the final reconstructed image of the scanned sample. These ring artifacts are commonly found in many applications of synchrotron tomography, and can make it difficult or impossible to use the reconstructed image in further analyses. The severity of ring artifacts is often reduced in practice by applying pre-processing on the acquired data, or post-processing on the reconstructed image. However, such additional processing steps can introduce additional artifacts as well, and rely on specific choices of hyperparameter values.In this paper, a different approach to reducing the severity of ring artifacts is introduced: a helical acquisition mode. By moving the sample parallel to the rotation axis during the experiment, the sample is detected at different detector positions in each projection, reducing the effect of systematic errors in detector elements. Alternatively, helical acquisition can be viewed as a way to transform ring artifacts to helix-like artifacts in the reconstructed volume, reducing their severity. We show that data acquired with the proposed mode can be transformed to data acquired with a virtual circular trajectory, enabling further processing of the data with existing software packages for circular data. Results for both simulated data and experimental data show that the proposed method is able to significantly reduce ring artifacts in practice, even compared with popular existing methods, without introducing additional artifacts.

Linear space-invariant image-restoration algorithms often introduce ringing artifacts near sharp intensity transition areas. This paper presents a new adaptive post-filtering algorithm to reduce the ringing artifacts. The proposed method extracts an edge map of the image using autocorrelation. On the basis of the edge information, ringing artifacts are detected and removed by an adaptive bilateral filter. Experimental results show that the proposed algorithm can efficiently remove ringing artifacts with edge preservation.

To address the limitations of BP neural networks, such as their sensitivity to initial weights and thresholds and their tendency to get stuck in local optima, this paper uses the cuckoo algorithm to search for the initial weights and thresholds of BP neural networks, thereby eliminating their dependence on initial weights and thresholds. Improvements were made to the step size and discovery probability of the cuckoo algorithm. The improved cuckoo algorithm was used to search for the initial weights and thresholds of the BP neural network, and the optimized network was applied to restore the Lena image . Experimental results show that for the Lena image, the ICS-BP neural network achieves a peak signal-to-noise ratio (PSNR) that is 4.83% and 3.53% higher than the BP neural network and PSO-BP neural network, respectively, and a structural similarity (SSIM) that is 1.62% and 0.67% higher than the BP neural network and PSO-BP neural network, respectively, indicating that the optimized BP neural network achieves better image restoration performance. Additionally, the ICS-BP converged after 35 iterations, which is faster than the BP neural network (137 iterations) and the PSO-BP neural network (81 iterations), thereby improving efficiency.

Cone-beam CT (CBCT) scanners are based on volumetric tomography, using a 2D extended digital array providing an area detector [1]. Compared to traditional CT, CBCT has many advantages, such as less X-ray beam limitation, high image accuracy, rapid scan time, etc. However, In CBCT images there are always some ring artifacts that appear as rings centered on the rotation axis. Due to the data of the constructed images are corrupted by these ring artifacts, qualitative and quantitative analysis of CBCT images will be compromised. Post processing and application such as image segmentation and registration also turn more complex as the presence of such artifacts. In this paper, a method based on independent component analysis (ICA) is presented. It deals with the reconstructed CBCT image and can effectively reduce such ring artifacts.

In this paper, a novel technique based on morphological filtering is proposed for the detection and removal of ring artifacts with different patterns, e.g., sharp isolated rings with varying intensity, isolated light rings, and band rings, from the μ- and/or C-Arm CT images. To optimize the performance of our ring removal algorithm, three different types of iterative morphological filters (IMFs) are proposed here for different ring patterns. All the IMFs detect the positions of the defective detector elements using the mean curve constructed from the corrupted sinogram. However, the basic principle of sinogram correction differs. For the elimination of sharp intense rings, the IMF is directly applied on the raw projection data for each angle of view, whereas in other cases an estimate of the ring-noise-free mean curve is first obtained to remove the ring generating artifacts from the sinogram data through a normalization process. The effectiveness of the proposed IMFs is tested for a variety of μ-CT acquired images. Experimental results show that the IMFs perform better than the previously reported techniques in the removal of ring artifacts from real μ-CT images.

Image restoration is an effective way to improve the quality of images degraded by wave-front aberrations. If the wave-front aberration is too large, the performance of the image restoration will not be good. In this paper, the relationship between the performance of image restoration and the degree of wave-front aberrations is studied. A set of different wave-front aberrations is constructed by Zernike polynomials, and the corresponding PSF under white-light illumination is calculated. A set of blurred images is then obtained through convolution methods. Next we recover the images with the regularized Richardson-Lucy algorithm and use the RMS of the original image and the homologous deblurred image to evaluate the quality of restoration. Consequently, we determine the range of wave-front errors in which the recovered images are acceptable.