aoqi Xi

Edge guided image reconstruction in linear scan CT by weighted alternating direction TV minimization.

Linear scan computed tomography (CT) is a promising imaging configuration with high scanning efficiency while the data set is under-sampled and angularly limited for which high quality image reconstruction is challenging. In this work, an edge guided total variation minimization reconstruction (EGTVM) algorithm is developed in dealing with this problem. The proposed method is modeled on the combination of total variation (TV) regularization and iterative edge detection strategy. In the proposed method, the edge weights of intermediate reconstructions are incorporated into the TV objective function. The optimization is efficiently solved by applying alternating direction method of multipliers. A prudential and conservative edge detection strategy proposed in this paper can obtain the true edges while restricting the errors within an acceptable degree. Based on the comparison on both simulation studies and real CT data set reconstructions, EGTVM provides comparable or even better quality compared to the non-edge guided reconstruction and adaptive steepest descent-projection onto convex sets method. With the utilization of weighted alternating direction TV minimization and edge detection, EGTVM achieves fast and robust convergence and reconstructs high quality image when applied in linear scan CT with under-sampled data set.

Improving the accuracy of CT dimensional metrology by a novel beam hardening correction method

Its powerful nondestructive characteristics are attracting more and more research into the study of computed tomography (CT) for dimensional metrology, which offers a practical alternative to the common measurement methods. However, the inaccuracy and uncertainty severely limit the further utilization of CT for dimensional metrology due to many factors, among which the beam hardening (BH) effect plays a vital role. This paper mainly focuses on eliminating the influence of the BH effect in the accuracy of CT dimensional metrology. To correct the BH effect, a novel exponential correction model is proposed. The parameters of the model are determined by minimizing the gray entropy of the reconstructed volume. In order to maintain the consistency and contrast of the corrected volume, a punishment term is added to the cost function, enabling more accurate measurement results to be obtained by the simple global threshold method. The proposed method is efficient, and especially suited to the case where there is a large difference in gray value between material and background. Different spheres with known diameters are used to verify the accuracy of dimensional measurement. Both simulation and real experimental results demonstrate the improvement in measurement precision. Moreover, a more complex workpiece is also tested to show that the proposed method is of general feasibility.

A method to determine the detector locations of the cone-beam projection of the balls’ centers

In geometric calibration of cone-beam computed tomography (CBCT), sphere-like objects such as balls are widely imaged, the positioning information of which is obtained to determine the unknown geometric parameters. In this process, the accuracy of the detector location of CB projection of the center of the ball, which we call the center projection, is very important, since geometric calibration is sensitive to errors in the positioning information. Currently in almost all the geometric calibration using balls, the center projection is invariably estimated by the center of the support of the projection or the centroid of the intensity values inside the support approximately. Clackdoyles work indicates that the center projection is not always at the center of the support or the centroid of the intensity values inside, and has given a quantitative analysis of the maximum errors in evaluating the center projection by the centroid. In this paper, an exact method is proposed to calculate the center projection, utilizing both the detector location of the ellipse center and the two axis lengths of the ellipse. Numerical simulation results have demonstrated the precision and the robustness of the proposed method. Finally there are some comments on this work with non-uniform density balls, as well as the effect by the error occurred in the evaluation for the location of the orthogonal projection of the cone vertex onto the detector.

3D alternating direction TV-based cone-beam CT reconstruction with efficient GPU implementation.

Iterative image reconstruction (IIR) with sparsity-exploiting methods, such as total variation (TV) minimization, claims potentially large reductions in sampling requirements. However, the computation complexity becomes a heavy burden, especially in 3D reconstruction situations. In order to improve the performance for iterative reconstruction, an efficient IIR algorithm for cone-beam computed tomography (CBCT) with GPU implementation has been proposed in this paper. In the first place, an algorithm based on alternating direction total variation using local linearization and proximity technique is proposed for CBCT reconstruction. The applied proximal technique avoids the horrible pseudoinverse computation of big matrix which makes the proposed algorithm applicable and efficient for CBCT imaging. The iteration for this algorithm is simple but convergent. The simulation and real CT data reconstruction results indicate that the proposed algorithm is both fast and accurate. The GPU implementation shows an excellent acceleration ratio of more than 100 compared with CPU computation without losing numerical accuracy. The runtime for the new 3D algorithm is about 6.8 seconds per loop with the image size of 256 × 256 × 256 and 36 projections of the size of 512 × 512.

Practical geometric calibration for helical cone-beam industrial computed tomography

In helical cone-beam industrial computed tomography (ICT), the reconstructed images may be interfered by geometry artifacts due to the presence of mechanical misalignments. To obtain artifact-free reconstruction images, a practical geometric calibration method for helical scan is investigated based on Noos analytic geometric calibration method for circular scan. The presented method is implemented by first dividing the whole ascending path of helical scan into several pieces, then acquiring the projections of a dedicated calibration phantom in circular scan at each section point, of which geometry parameters are calculated using Noos analytic method. At last, the geometry parameters of each projection in a piece can be calculated by those of the two end points of the piece. We performed numerical simulations and real data experiments to study the performance of the presented method. The experimental results indicated that the method can obtain high-precision geometry parameters of helical scan and give satisfactory reconstruction images.

X-ray CT image reconstruction from few-views via total generalized p-variation minimization

Total variation (TV)-based CT image reconstruction, employing the image gradient sparsity, has shown to be experimentally capable of reducing the X-ray sampling rate and removing the unwanted artifacts, yet may cause unfavorable over-smoothing and staircase effects by the piecewise constant assumption. In this paper, we present a total generalized p-variation (TGpV) regularization model to adaptively preserve the edge information while avoiding the staircase effect. The new model is solved by splitting variables with an efficient alternating minimization scheme. With the utilization of generalized p-shrinkage mappings and partial Fourier transform, all the subproblems have closed solutions. The proposed method shows excellent properties of edge preserving as well as the smoothness features by the consideration of high order derivatives. Experimental results indicate that the proposed method could avoid the mentioned effects and reconstruct more accurately than both the TV and TGV minimization algorithms when applied to a few-view problem.Total variation (TV)-based CT image reconstruction, employing the image gradient sparsity, has shown to be experimentally capable of reducing the X-ray sampling rate and removing the unwanted artifacts, yet may cause unfavorable over-smoothing and staircase effects by the piecewise constant assumption. In this paper, we present a total generalized p-variation (TGpV) regularization model to adaptively preserve the edge information while avoiding the staircase effect. The new model is solved by splitting variables with an efficient alternating minimization scheme. With the utilization of generalized p-shrinkage mappings and partial Fourier transform, all the subproblems have closed solutions. The proposed method shows excellent properties of edge preserving as well as the smoothness features by the consideration of high order derivatives. Experimental results indicate that the proposed method could avoid the mentioned effects and reconstruct more accurately than both the TV and TGV minimization algorithms when applied to a few-view problem.

Sparse-view image reconstruction with nonlocal total variation

The concept of computed tomography (CT) reconstruction from sparse-view data has been a considerable area of much research over the last several years. With the famous piecewise constant assumption, total variation (TV) model has been shown that it could be successfully applied to sparse-view CT reconstruction for producing accurate reconstructions. However, the resulting images from the traditional TV model based on local operators always meet the problems of smeared edges or staircase effects. In this paper, the TV minimization reconstruction model is extanded to a nonlocal TV (NLTV) model, using auxiliary variables and efficient split Bregman iterartive scheme, a reconstruction algorithm based on NLTV minimization has been developed. The proposed method shows excellent properties of edge preserving and smoothness preserving by using the nonlocal operators. Experimental results indicate that the proposed method could solve the above mentioned effects and reconstruct more accurate than the popular split Bregman-TV algorithm when applied to a sparse-view problem.

An optimized acquisition approach exploiting geometrical calibration in x-ray cone-beam computed tomography

X-ray cone-beam computed tomography, featuring high precision and fast-imaging speed, has been widely used in industrial non-destructive testing applications for the three dimensional visualization of internal structures. Due to mechanical imperfections, geometric calibrations are imperative to high quality image reconstruction. Currently, the twoball phantom-based calibration procedures exploiting the projection trajectories of the phantoms are the most commonly used approach for the estimation of the geometrical parameters and the calibration of CT system. However, an additional scan needs to be performed, even after each object acquisition when lack of system reproducibility, leading to multiplied calibration times. The emphasis of this paper is to optimize the process of acquisition in cone-beam CT imaging with minimal time, based on the understanding of the determination of the ball position in typical phantom-based geometric calibration algorithms. An applicable condition of the calibration algorithm for simultaneously scanning objects and calibration phantoms is proposed and demonstrated, which is that the minimum projection value of the scanned object needs to be at least 100 counts higher than those of the calibration phantom, with consideration of the system noise. The CT experiments are based on a laboratory industrial cone-beam CT system with a micro-focus x-ray tube (Thales Hawkeye 130) and a flat panel detector (Thales Pixium RF4343). Objects imaged are chosen with a wide projection value range, from low-Z watermelon seeds and high-Z materials, including a standard Micro CT Bar Pattern Phantom (QRM) for image quality assessment. In these experiments, objects, as well as two-ball phantoms, both placed in the field of view without overlapping in the vertical direction, are projected over 360 degrees, instead of scanning the calibration phantoms separately. Hence, the true geometrical relationship is resolved utilizing the two-ball algorithm. Both simulation and experimental results confirm that the calculated geometrical parameters will not be affected by the objects as long as their projection value difference meeting the requirements above. And the reconstruction image quality is almost the same with those by an independent calibration. Compared to the traditional application of the phantombased geometrical calibration method, the novel approach presented in this paper has obvious advantages from an imaging perspective, saving acquisition time and eliminating the undesired influence from the operation staff for the same cost.

A hybrid projection decomposition algorithm for dual-energy computed tomography

Dual Energy Computed Tomography (DECT) has recently gained significantly research interest because of its ability of object separation, contrast enhancement, artifact reduction and material composition assessment. Aiming for the nonlinear projection decomposition problem in DECT image reconstruction, a hybrid projection decomposition algorithm is proposed with combination of iterative method and isotransmission fitting method. Firstly, the nonlinear projection aquation for a given polychromatic projection value is transformed to linear equation plus a nonlinear pertubative item for single variable to speed up the iteration speed and avoid non-convergence. Secondly, a linear approximation fitting model of iso-transmission line is built for high and low energy projection with iso-transmission point pairs solved above. Then the high and low energy projection joint nonlinear solving problem is transformed into solving high and low energy projection iso-transmission line intersection problem, so as to realize the fast decomposition of dual energy projection. Finally, local searching near the intersection of projection matching is used to compensate the error of linear approximation fitting to get more accurate decomposition result. The accuracy and efficiency of the proposed method are demonstrated by numerical simulation.

A filter design method for beam hardening correction in middle-energy x-ray computed tomography

Beam hardening artifact is common in X-ray computed tomography (X-CT). Using the metal sheet as a filter to preferentially attenuate low-energy photons is a simple and effective way for beam hardening artifact correction. However, generally it requires a large quantity of experiments to compare the filter material and thickness, which is lack of guidance of theory. In this paper, a novel filter design method for beam hardening correction, especially for middle energy X-CT, is presented. First, the spectrum of X-ray source under a certain tube voltage is estimated by Monte Carlo (MC) simulation or other simulation methods. Next, according to the X-ray mass attenuation coefficients of the object material, the energy range to be retained can be roughly determined in which the attenuation coefficients change slowly. Then, the spectra filtering performance with different filter materials and thicknesses can be calculated using the X-ray mass attenuation coefficients of each filter material and the simulated primitive spectrum. After that, the mean energy ratio (MER) of post-filter mean energy to pre-filter mean energy is obtained. Finally, based on the spectrum filtering performance and MER of the metal material, a suitable filter strategy is easily selected. Experimental results show that, the proposed method is simple and effective on beam hardening correction as well as increasing the image quality.