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In computed tomography (CT) image reconstruction problems, exterior CT is an important application in industrial non-destructive testing (NDT). Different from the limited-angle problem that misses part of the rotation angle, the rotation angle of the exterior problem is complete, but for each rotation angle, the projection data through the central region of the object cannot be collected, so that the exterior CT problem is ill-posed inverse problem. The results of traditional reconstruction methods like filtered back-projection (FBP) and simultaneous algebra reconstruction technique (SART) have artifacts along the radial direction edges for exterior CT reconstruction. In this study, we propose and test an anisotropic relative total variation in polar coordinates (P-ARTV) model for addressing the exterior CT problem. Since relative total variation (RTV) can effectively distinguish edges from noises, and P-ARTV with different weights in radial and tangential directions can effectively enhance radial edges, a two-step iteration algorithm was developed to solve the P-ARTV model in this study. The fidelity term and the regularization term are solved in Cartesian and polar coordinate systems, respectively. Numerical experiments show that our new model yields better performance than the existing state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Limited-angle computed tomography (CT) may appear in restricted CT scans. Since the available projection data is incomplete, the images reconstructed by filtered back-projection (FBP) or algebraic reconstruction technique (ART) often encounter shading artifacts. However, using the anisotropy property of the shading artifacts that coincide with the characteristic of limited-angle CT images can reduce the shading artifacts. Considering this concept, we combine the anisotropy property of the shading artifacts with the anisotropic structure property of an image to develop a new algorithm for image reconstruction. Specifically, we propose an image reconstruction method based on adaptive weighted anisotropic total variation (AwATV). This method, termed as AwATV method for short, is designed to preserve image structures and then remove the shading artifacts. It characterizes both of above properties. The anisotropy property of the shading artifacts accounts for reducing artifacts, and the anisotropic structure property of an image accounts for preserving structures. In order to evaluate the performance of AwATV, we use the simulation projection data of FORBILD head phantom and real CT data for image reconstruction. Experimental results show that AwATV can always reconstruct images with higher SSIM and PSNR, and smaller RMSE, which means that AwATV enables to reconstruct images with higher quality in term of artifact reduction and structure preservation. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) has been widely applied in medical diagnosis, nondestructive evaluation, homeland security, and other science and engineering applications. Image reconstruction is one of the core CT imaging technologies. In this review paper, we systematically reviewed the currently publicly available CT image reconstruction open source toolkits in the aspects of their environments, object models, imaging geometries, and algorithms. In addition to analytic and iterative algorithms, deep learning reconstruction networks and open codes are also reviewed as the third category of reconstruction algorithms. This systematic summary of the publicly available software platforms will help facilitate CT research and development. [ABSTRACT FROM AUTHOR]

PURPOSES: Restricted by the scanning environment in some CT imaging modalities, the acquired projection data are usually incomplete, which may lead to a limited-angle reconstruction problem. Thus, image quality usually suffers from the slope artifacts. The objective of this study is to first investigate the distorted domains of the reconstructed images which encounter the slope artifacts and then present a new iterative reconstruction method to address the limited-angle X-ray CT reconstruction problem. METHODS: The presented framework of new method exploits the structural similarity between the prior image and the reconstructed image aiming to compensate the distorted edges. Specifically, the new method utilizes l0 regularization and wavelet tight framelets to suppress the slope artifacts and pursue the sparsity. New method includes following 4 steps to (1) address the data fidelity using SART; (2) compensate for the slope artifacts due to the missed projection data using the prior image and modified nonlocal means (PNLM); (3) utilize l0 regularization to suppress the slope artifacts and pursue the sparsity of wavelet coefficients of the transformed image by using iterative hard thresholding (l0W); and (4) apply an inverse wavelet transform to reconstruct image. In summary, this method is referred to as “l0W-PNLM”. RESULTS: Numerical implementations showed that the presented l0W-PNLM was superior to suppress the slope artifacts while preserving the edges of some features as compared to the commercial and other popular investigative algorithms. When the image to be reconstructed is inconsistent with the prior image, the new method can avoid or minimize the distorted edges in the reconstructed images. Quantitative assessments also showed that applying the new method obtained the highest image quality comparing to the existing algorithms. CONCLUSIONS: This study demonstrated that the presented l0W-PNLM yielded higher image quality due to a number of unique characteristics, which include that (1) it utilizes the structural similarity between the reconstructed image and prior image to modify the distorted edges by slope artifacts; (2) it adopts wavelet tight frames to obtain the first and high derivative in several directions and levels; and (3) it takes advantage of l0 regularization to promote the sparsity of wavelet coefficients, which is effective for the inhibition of the slope artifacts. Therefore, the new method can address the limited-angle CT reconstruction problem effectively and have practical significance. [ABSTRACT FROM AUTHOR]

For cone-beam computed tomography (CBCT), transversal shifts of the rotation center exist inevitably, which will result in geometric artifacts in CT images. In this work, we propose a novel geometric calibration method for CBCT, which can also be used in micro-CT. The symmetry property of the sinogram is used for the first calibration, and then L0-norm of the gradient image from the reconstructed image is used as the cost function to be minimized for the second calibration. An iterative search method is adopted to pursue the local minimum of the L0-norm minimization problem. The transversal shift value is updated with affirmatory step size within a search range determined by the first calibration. In addition, graphic processing unit (GPU)-based FDK algorithm and acceleration techniques are designed to accelerate the calibration process of the presented new method. In simulation experiments, the mean absolute difference (MAD) and the standard deviation (SD) of the transversal shift value were less than 0.2 pixels between the noise-free and noisy projection images, which indicated highly accurate calibration applying the new calibration method. In real data experiments, the smaller entropies of the corrected images also indicated that higher resolution image was acquired using the corrected projection data and the textures were well protected. Study results also support the feasibility of applying the proposed method to other imaging modalities. [ABSTRACT FROM AUTHOR]

BACKGROUND: Limited-angle problem occurs in many applications of X-ray computed tomography (CT) and it will lead to artifacts. OBJECTIVE: In this paper, we present the tomographic imaging of pipeline which is in service by cone-beam computed tomography (CBCT). And our purpose is to suppress the artifacts caused by the limited-angle problem. METHODS: First, traditional helical scanning strategy is adopted for the pipeline where can be fully scanned, and the slices with less or no defects from the reconstructed image are selected to constitute the prior image. Then a limited-angle reverse helical scanning strategy is developed for the pipeline where can be scanned within limited angular range. Second, considering the constraint of the prior image and the resemblance among slices of pipeline, an image model which incorporates the Schatten p(0 < p < 1)-norm penalty term is developed. Finally, split Bregman algorithm based method is utilized to solve the model. RESULTS: Numerical results show that our method is efficient and feasible in suppressing artifacts and it is superior to popular methods when the angular range is small. CONCLUSION: The developed method is promising in suppressing the artifacts in limited-angle problems, and it can be potentially used in pipeline testing. [ABSTRACT FROM AUTHOR]

To reconstruct images from truncated projections collected along a spiral trajectory, the most common approach is to smoothly extrapolate each projection to suppress truncation artifacts. Based on the work of Dennerlein, a local reconstruction algorithm is developed for spiral cone beam computed tomography (CT), which does not need explicit projection extrapolation. This algorithm is a filtered-backprojection (FBP) format and contains three major steps: cosine weight, filtration, and backprojection. While the cosine weight and backprojection steps are the same as the classical Feldkamp - Davis - Kress (FDK) scheme, the filtering step contains two steps: Laplace filtering and Radon transform-based filtering. Numerical simulation results show that the proposed algorithm has a better performance than FDK algorithm for region-of-interest (ROI) reconstruction of spiral cone-beam CT. [ABSTRACT FROM AUTHOR]

Intensity inhomogeneity may cause considerable difficulties in segmentation of CT image. In order to overcome the difficulties caused by intensity inhomogeneity, the region-scalable fitting (RSF) model was put forward. RSF model draws upon intensity information in local regions with a controllable scale. But only using intensity information may lead to slow convergence rate and poor denoise ability. Combining the method of robust statistics, RSF model is improved in this paper. In the improved model, the intensity in RSF model is replaced with local robust statistics which is the weighted combination of inter-quartile range, mean absolute deviation and intensity median in local region. Inter-quartile range and mean absolute deviation in local region are introduced to sharpen object boundaries, and intensity median in local region is introduced to reduce image noise. The contrast experiments between RSF model and the improved model are provided, which demonstrate the fast convergence rate and robustness to noise of the improved model. [ABSTRACT FROM AUTHOR]

To reduce time cost and improve the performance of edge extraction in CT volume data which is often in large size, we propose a novel method of 3D crack edge extraction using two fusion steps, one is fusion on Finite Line Integral Transform (FLIT) values in spatial directions called SD-FLIT and another is fusion on Local Binary Pattern (LBP) values on spatial planes called SP-LBP. By analyzing the S function of LBP operator, we find that value "0" of this function can describe the change between two equivalence planes. However, this property is sensitive to point difference, thus SD-FLIT is introduced to smooth noises and artifacts before the application of SP-LBP to extract 3D edge on binary volume data. Besides, fusions on directions and planes are aimed at extracting enough spatial information. Experimental results show that, owing to the sufficiency of information extraction and the simplicity of computation, our method can get continuous, thin and occlusive 3D edge, including the crack tip. Furthermore, it can be used to complicate volume data. Compared with 3D wavelet and Facet model, our method cost less time, saving at least 89% of that. [ABSTRACT FROM AUTHOR]

This paper is about inspecting large and long object using dual helical cone-beam computed tomography (CT) and Katsevich-type reconstruction algorithm. Conventional cone-beam helical industrial CT imaging is based on the assumption that the entire cross-section of an object is illuminated with x-rays at each view angle. The field of view is limited by the width of planar detector. As an alternative, this paper developed a dual-helical scanning. When scanning at each helix, part of cross-section is covered by x-ray at each view angle. During reconstruction, Katsevich-type algorithm is applied, which does not rebin projection data. The algorithm is approximate, as it applies Hilbert transform on truncated projection. The reconstruction result is better than extended FDK algorithm, especially for large helical pitch. [ABSTRACT FROM AUTHOR]