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PURPOSE In x-ray radiography, the commonly used antiscatter grid for enhancing image quality causes artifacts in the form of periodic noises, such as shadows, cutoff, and Moire fringes. Software degridding is traditionally performed via linear or homomorphic filtering in the spectral domain. These methods inevitably result in image blurring, information loss, and distortion, thus hindering detection and assessment of diseases. We seek effective and practical solutions for grid artifact correction based on spatial-domain analysis toward high-quality imaging. METHODS By analyzing the physical process of grid artifact formation, we track down the root of the problem associated with spectral filtering. We propose the grid regression demodulation (GRD). The degridding cost is forged as a functional of the latent x-ray photon image and parametric grid model characterizing grid transmission property. Regularization on the grid spectra is incorporated. We devise optimization algorithms for artifact correction and grid pattern estimation. GRD decouples the partially overlapped spectra of the grid and anatomy, and removes the artifacts independently, thus restoring the underlying clinically relevant data. RESULTS Method efficacy is demonstrated using simulated and real data. GRD effectively preserves image edges, textures, and patterns while removing grid artifacts. For the known ground truth setting, GRD gives a near-perfect correction. For real data, GRD is capable of correcting not only the primary grid artifacts, but also the higher grid harmonic artifacts while keeping image content unaltered, which is unachievable by the other methods. Our method has low residual errors and exhibits a successful demodulation effect without introducing additional artifacts, while ringing or cilia artifacts are present in the others. CONCLUSIONS The proposed method outperforms the prevalent transform techniques for correcting grid artifacts in digital radiography. It is self-sustained and self-adaptive to a range of targets and beam quality. Our approach is advantageous in restoring the latent image while suppressing grid noises. It retrieves the true scale factor of the degridded data, which is unattainable via any spectral filtering techniques. This work unlocks a promising venue to improve and upgrade low-dose medical radiographic imaging technology.

Anti-scatter grids are routinely used to prevent degradation of image quality caused by scattered X-ray beams. However, these grids might cause linear artifacts that represent the shadows of the radiopaque septa. In this paper, we propose a machine learning-based method for grid artifact suppression in radiography. There are two major difficulties in the application of a deep learning technique for the grid-line suppression problem. The first is quantitative shortage of learning data. It is difficult to acquire a sufficient amount of learning data from observed images in consideration of the various situations in which grid lines appear. The second is difficulty determining the target data. It is practically impossible to generate a clean target image for an arbitrary grid-line image. To overcome these problems, we propose a deep convolutional neural network architecture and a learning data construction method. A discrete cosine transform-based band-stop filtering technique and an image synthesizing algorithm were adopted for the learning data construction method. A patch sampling method was employed to overcome the shortage of the amount of learning data. The proposed method enables learning without clean target data and overcomes the weakness of conventional frequency analysis-based methods with regard to the grid-line suppression problem. This method makes it possible to expect complementary performance through the construction of a combined learning data set including observed images and artificially generated grid-line images.

PURPOSE Grid artifacts occur in digital mammography when synchronization between the grid assembly and generator is not achieved, including when malfunctions occur in the grid assembly or generator subsystems. Such artifacts are not explicitly monitored or evaluated by existing mammography quality control programs. In this study, we developed an automated method for quantifying the presence of grid artifacts in two-dimensional (2D) digital mammography images and assessed its utility as a supplement to existing quality control programs. METHODS Four digital mammography systems (Hologic Dimensions 3D 5000) were configured to automatically transfer 2D images to a server where the strength of the grid pattern, γmax , was quantified using a template-matching algorithm and stored in amySQL database. This analysis was performed on both American College of Radiology (ACR) phantom and clinical images. Changes in γmax were compared with image quality and service records to establish preliminary action limits for physicist intervention for each type of image. These action limits were applied around selected service events to evaluate their clinical utility. RESULTS All systems exhibited a gradual increase in γmax in ACR phantom images prior to having identical major components of the generator subsystem replaced, despite the absence of visible gridlines in the images. Retrospective analysis of phantom images suggested that physicists should consider AEC testing when γmax exceeds 0.050 and that clinical image quality may be affected when γmax exceeds 0.060. Eighteen of 19 visible grid artifacts were identified using a threshold γmax value of 0.065 in clinical images. Warning limits that indicate abnormal operation before visible degradation in image quality were also established. These warning limits were 0.046 and 0.041 for the 24 × 29 cm and 18 × 24 cm paddles, respectively. Specific malfunctions in the generator and grid subsystems can be detected by applying these limits. CONCLUSIONS Automated monitoring of γmax provides useful information about the status of digital mammography units without affecting clinical operations. When used with appropriate action limits, this type of monitoring can help physicists identify specific equipment malfunctions before they would be detected by other quality control tests and before they affect clinical images.

The mammography is the first choice of breast cancer screening, which has proven to be the most effective screening method. An antiscatter grid is usually employed to enhance the contrast of image by absorbing unexpected scattered signals. However, the grid pattern casts shadows and grid artifacts, which severely degrade the image quality. To solve the problem, we propose the patch based frequency signal filtering for fast grid artifacts suppressing. As opposed to whole image processing synchronously, the proposed method divides image into a number of blocks for tuning filter simultaneously, which reduces the frequency interference among image blocks and saves computation time by multithread processing. Moreover, for mitigating grid artifacts more precisely, characteristic peak detection is employed in each block automatically, which can accurately identify the location of the antiscatter grid and its motion pattern. Qualitative and quantitative studies were performed on simulation and real machine data to validate the proposed method. The results show great potential for fast suppressing grid artifacts and generating high quality of digital mammography.

In digital radiography imaging, the antiscatter grid is usually employed to obtain clear projected images by absorbing scattered x-ray beams. However, due to the shadow of the stationary antiscatter grid, the grid artifact appears in the obtained x-ray image as stripes if a linear grid is considered. In order to alleviate the grid artifact, band-stop filters (BSFs) can be used and may cause annoying ringing artifact especially for metallic materials. In this paper, in order to reduce the ringing artifact while applying BSFs to alleviate the grid artifact, a spatial prefiltering technique, which is called the gradient-reduction filter, is proposed. The gradient-reduction filter can remove steep edges in the obtained x-ray images especially for metallic materials and can prevent the grid- artifact BSFs from yielding serious ringing artifact. The structure of the proposed filter is similar to that of the Crawford noise-reduction filter. However, the nonlinear function in the proposed filter has decreasing slopes as its input increases contrary to the Crawford filter case. Through extensive experiments for real digital x-ray images, which are obtained from a direct digital radiography detector, we can see the superior ringing-artifact reduction performance comparing to the conventional filtering approaches especially when the metallic materials present in or on the patients.

In digital radiography imaging, the x-ray images are formed based on a multiplicative model, in which the projected image of an object is multiplied by the antiscatter grid shadow. Hence, the formed image is amplitude- modulated by the grid shadow and the resultant modulated terms appear as the grid artifacts. Since the bandwidths of the modulated terms are as wide as that of the projected image, we should employ relatively wide-bandwidth band-stop filters (BSFs) to reduce the grid artifacts. When we apply such BSFs, the object to be recovered is prone to distortion due to the wide filter bandwidth. In this paper, to reduce the signal bandwidth of the grid shadow images in reduction of the grid artifacts, applying BSFs based on the homomorphic operation is proposed by employing the logarithmic function. By taking the logarithm of the formed image, we can separate the multiplicative grid component from both projected image and the exposure of x-rays. Hence, by employing a relatively narrow and fixed-bandwidth BSFs, we can efficiently alleviate the grid artifacts independently of the strength of the grid artifacts comparing to the conventional linear approaches. For real x-ray images, the superior performance of the proposed approach is compared in this paper.

In order to reduce the grid artifacts, which are caused by using the antiscatter grid in obtaining x-ray digital images, we analyze the effect of applying band-rejection filters (BRFs) in the frequency domain. The property of the grid artifacts is quite different depending on the shape of the object to be recovered in the spatial domain. Furthermore, if the frequency component of the grid artifact is relatively close to that of the object, then simply applying a BRF may seriously distort the object and cause the ringing artifact. In this paper, we propose a hybrid filtering scheme, which can cooperate such different properties in the spatial domain. In order to perform a robust detection of the position of the ringing artifact, we propose the frequency-modulation (FM) model for the extracted signal, which corresponds to a specific grid artifact. An estimation of the position image for the ringing artifact is then conducted based on the slope detection algorithm, which is commonly used as an FM discriminator in the communication area. Numerical result for real x-ray images shows that applying BRFs in the frequency domain in conjunction with the spatial property of the ringing artifact can successfully remove the grid artifact, distorting the object less.

By applying band-rejection filters (BRFs) in the frequency domain, we can efficiently reduce the grid artifacts, which are caused by using the antiscatter grid in obtaining x-ray digital images. However, if the frequency component of the grid artifact is relatively close to that of the object, then simply applying a BRF may seriously distort the object and cause the ringing artifacts. Since the ringing artifacts are quite dependent on the shape of the object to be recovered in the spatial domain, the spatial property of the x-ray image should be considered in applying BRFs. In this paper, we propose an adaptive filtering scheme, which can cooperate such different properties in the spatial domain. In the spatial domain, we compare several approaches, such as the mangnitude, edge, and frequency-modulation (FM) model-based algorithms, to detect the ringing artifact or the grid artifact component. In order to perform a robust detection whether the ringing artifact is strong or not, we employ the FM model for the extracted signal, which corresponds to a specific grid artifact. A detection of the position for the ringing artifact is then conducted based on the slope detection algorithm, which is commonly used as an FM discriminator in the communication area. However, the detected position of the ringing artifact is not accurate. Hence, in order to obtain an accurate detection result, we combine the edge-based approach with the FM model approach. Numerical result for real x-ray images shows that applying BRFs in the frequency domain in conjunction with the spatial property of the ringing artifact can successfully remove the grid artifact, distorting the object less.

In order to reduce the grid artifacts, which are caused by using the antiscatter grid in obtaining x-ray digital images, we analyze the grid artifacts based on the multiplicative image formation model instead of the traditional additive model, and apply filters to suppress the artifact terms. The artifact terms are aliases of the modulated terms from the harmonics of the grid frequency. Hence, several filters are required to efficiently suppress the grid artifacts. However, applying filters also distort the original image that will be recovered. If the distance between the origin and the center frequency of the artifact term is relatively large, then we can suppress the artifact term less distorting the original image. In this paper, by increasing the distances for a given sampling frequency of the image detector, we design antiscatter grids, which are good in terms of efficient grid artifact reduction. In order to design optimal grids, we formulate min-max optimization problems and provide optimal grid frequencies for a fixed grid angle with respect to the sampling direction of the image detector and optimal grid angles for a fixed grid frequency. We then propose using rotated grids with the optimal grid angles in digital radiography imaging. By applying band-rejection filters to the artifact, we can considerably reduce the grid artifacts when comparing to the traditional non-rotated grid case for real x-ray images.

In order to reduce the grid artifacts in the digital radiography with stationary antiscatter grids, artifact frequency detection schemes should be adopted and appropriate band-rejection filters should be applied. However, by rotating the grid angle for certain grid densities and sampling frequencies, the artifact frequencies can be placed near the boundary of the frequency domain, and then the grid artifacts can be easily alleviated by applying a simple low-pass filter without any estimates of the artifact frequencies. In this paper, we propose a grid artifact reduction algorithm, which is based on rotating the grid and applying a low-pass filter. For the sampling frequency 7.194 lines/mm, an optimal rotate angle is arctan(1/2) ≈ 26.57° and the grid density is 102.2 lines/inch. Better performances are obtained for real x-ray images.

Antiscatter grids absorb scattered radiation and increase X- ray image contrast. Stationary grids leave line artifacts or create Moire patterns on resized digital images. Various grid designs were investigated to determine relevant physical properties that affect an image. A detection algorithm is based on grid peak determination in the image's averaged 1D Fourier spectrum. Grid artifact removal is based on frequency filtering in a spatial dimension orthogonal to the grid stripes. Different filter design algorithms were investigated to choose the transfer function that maximizes the suppression of grid artifacts with minimal image distortion. Algorithms were tested on synthetic data containing a variety of SNRs and grid spatial inclinations, on radiographic data containing phantoms with and without grids, and on a set of real CR images. Detector and filter performance were optimized by using Intel Signal Processing Library, resulting in a time of about 3 sec to process a 2Kx2.5K CR image on a Pentium II PC> There are no grid artifacts and no image blur revealed on processed images as evaluated by third party technical and medical experts. This automated grid artifact suppression method is built into a new version of Kodak PACS Link Medical Image Manager.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.