Search

Cone-beam computed tomography is a noninvasive detection system that can obtain the three-dimensional structure of objects in a way that does not damage the object. It is widely applied in precision instruments, medical detection, and other fields. However, in the actual process, if a geometric artifact appears in the results, it will affect the quality of reconstructed images, including detail loss and decreased spatial resolution, which leads to inaccurate distinction of defects in detection. We propose a method for correcting a geometric artifact by means of data-driven projection and neural networks. The network designed is a deep neural network with six convolutional layers and six deconvolutional layers that can correct a geometric artifact by training a large number of labeled data and unlabeled data. Compared with other networks that require prior information for reconstructed images, the proposed method uses a projection data-driven approach that can avoid the requirement for prior information. The simulation data have been tested under varying degrees of noise, and satisfactory geometric artifact correction results have been obtained. Meanwhile, we use the actual data of line pairs and ball grid array solder joints to conduct experiments. The results obtained by our method are compared with two other phantom-based method and the U-net method, respectively. The results of similarity and spatial resolution show that the proposed method can achieve the comparable results as the two types of methods. At the same time, we apply a projection data-driven approach to avoid the requirement for prior information, which is more conducive to the correction of the geometric artifact in practical situations where prior information is lacking.

Dual-energy computed tomography (DECT) has attracted the attention of clinical researchers because of its outstanding capabilities to identify and decompose materials. Considering that material decomposition is unstable, reconstructed material images experience severe noise magnification resulting from the measurement data. To alleviate this problem, we propose a direct material reconstruction method by establishing a novel constrained reconstruction model based on total variation (TV) and block matching 3D (BM3D) frame regularization. TV regularization preserves the sparsity in the gradient domain of material maps and helps to preserve the image edges. BM3D frame is applied to depict the similarities among various patches in reconstructed images by grouping similar 2D image blocks into 3D data arrays combined with sparse transforms. To solve the program efficiently, a practical algorithm based on alternating direction method is developed. A modified strategy of block matching denoising is designed by incorporating the polychromatic reconstructed image into the problem solution. Digital and real data phantom studies are performed to validate the performance of the proposed method. The proposed method reduces the standard deviation on the selected region of interests by an average of 95.02% and 89.03% for the digital phantom and 95.21% and 84.19% for the real data compared with the extended simultaneous algebraic reconstruction technique and TV-based method, respectively. The reconstruction results demonstrate that the proposed method has promising capabilities in direct material reconstruction and superiority over its counterparts.

Metal artifacts seriously degrade the quality of the CT data and bring great difficulties to subsequent image processing and analysis, which nowadays become a great concern in X-ray CT applications. In this paper, we introduce a U-net-based metal artifact reduction method into CT image domain. The proposed network reduces metal artifacts by learning an end-to-end mapping of images from metal-corrupted CT images to their corresponding artifact-free ground truth images. We design and optimize the network through the simulation experiments. The experimental results show that the proposed method can well reduce metal artifacts of CT images, and this method has higher computational efficiency and greatly shortens the processing time. It avoids complex image preprocessing and can accept input images of any size. Therefore, it can be a more automated way to handle large amounts of data, making it ideal for existing CT workflows.

Compared with the conventional 1×1 acquisition mode of projection in computed tomography (CT) image reconstruction, the 2×2 acquisition mode improves the collection efficiency of the projection and reduces the x-ray exposure time. However, the collected projection based on the 2×2 acquisition mode has low resolution (LR) and the reconstructed image quality is poor, thus limiting the use of this mode in CT imaging systems. In this study, a novel sinogram-super-resolution (SR) generative adversarial network model is proposed to obtain high-resolution (HR) sinograms from LR sinograms, thereby improving the reconstruction image quality under the 2×2 acquisition mode. The proposed generator is based on the residual network for LR sinogram feature extraction and SR sinogram generation. A relativistic discriminator is designed to render the network capable of obtaining more realistic SR sinograms. Moreover, we combine the cycle consistency loss, sinogram domain loss, and reconstruction image domain loss in the total loss function to supervise SR sinogram generation. Then, a trained model can be obtained by inputting the paired LR/HR sinograms into the network. Finally, the classic filtered-back-projection reconstruction algorithm is used for CT image reconstruction based on the generated SR sinogram. The qualitative and quantitative results of evaluations on digital and real data illustrate that the proposed model not only obtains clean SR sinograms from noisy LR sinograms but also outperforms its counterparts.

Recently, low-dose computed tomography (CT) was considered by many researchers to be a good solution to reduce radiation risks of patients. However, lowering X-ray tube current will make reconstructed images quality be significantly degraded. To improve image quality, in this paper, we proposed a modified dual-domain U-net (MDD-U-net) that combines the projection domain and image domain losses. The proposed MDD-U-net can effectively suppress the projection domain noise and reduce the error in reconstructed images. The simulation experiment results showed that the proposed method effectively reduce the noise in the low-dose CT images while preserved images details information.

Cystic adventitial disease (CAD) is a condition that mainly affects the arterial system but rarely involves the venous system. The present study reported on two cases of CAD of femoral veins. The clinical manifestations in the two patients included pain and swelling of the lower limbs, but they had no risk factors for venous thrombosis. Color ultrasonography indicated cystic space-occupying lesions around the femoral veins, and enhanced computed tomography (CT) revealed vascular compression and space-occupying properties of the femoral veins. The two patients underwent open surgery, the postoperative treatment was uneventful, and swelling and tenderness were completely resolved. The pathological examination confirmed CAD of femoral vein in the two patients. The enhanced CT venography scan indicated patency of the femoral vein in the two patients following the operation. The circumferences of the two patients' lower limbs had returned to normal at the 2-month follow-up. A review of the literature was performed, and the presentation, diagnosis, treatment and pathology of this rare condition were discussed.

For X-ray cone-beam computed tomography (CBCT), non-calibrated system geometry causes indistinct and blurring artifacts in reconstructed images, which impacts on the image quality badly. This paper presents a practical industrial CBCT geometric calibration method, which aims to determine the geometric parameter without extra phantom and benefit to efficiency for calibration. In our method, the geometric parameter is divided into system parameter and imaging parameter to be calibrated, respectively. First, the system parameter was determined precisely at the first beginning via average for multiple measurements. Next, the imaging parameter was determined via a designed algorithm in this paper based on the invariance of rotation axis. Furthermore, the proposed method only utilizes the projection data without reconstruction iteration to calibrate geometric parameter, which benefits to efficiency and practical industrial application. 3-D simulation data have been through test with and without noise, and the results show robust and accuracy on the proposed algorithm. In addition, it is also validated with various real data include printed circuit board and industrial components acquired at actual industrial CBCT. The results of reconstructed images demonstrate that the proposed method can achieve comparable image quality in the reconstruction as some phantom-based methods. Furthermore, the advantages and obstacle of the proposed method are analyzed and discussed in this paper.

Highlights • Patients who are unsuitable for chemoembolization could progress with extrahepatic spread or vascular invasion after initial chemoembolization monotherapy. • A radiomics signature based on the machine learning algorithm was identified. • The signature combined with clinicoradiologicial predictors could predict TACE-unsuitable patients. • The combined model showed improved predictive performance compared with the model without radiomics signature. • The combined model could stratify patients into three strata with a low, intermediate, or high risk in training and external testing sets., Introduction Due to the high heterogeneity of hepatocellular carcinoma (HCC), patients with non-advanced disease who are unsuitable for initial transarterial chemoembolization (TACE) monotherapy may have the potential to develop extrahepatic spread or vascular invasion. We aimed to develop and independently validate a radiomics-based model for predicting which patients will develop extrahepatic spread or vascular invasion after initial TACE monotherapy (EVIT). Materials and methods This retrospective study included 256 HCC patients (training set: n = 136; testing set: n = 120) who underwent TACE as initial therapy between April 2007 and June 2018. Clinicoradiological predictors were selected using multivariate logistic regression and a clinicoradiological model was constructed. The radiomic features were extracted from contrast-enhanced computed tomography (CT) images and a radiomics signature was constructed based on a machine learning algorithm. A combined model integrated clinicoradiological predictor and radiomics signature was developed. The predictive performance of the two models was evaluated and compared based on its discrimination, calibration, and clinical usefulness. Results In the training set, 34 (25.0%) patients were confirmed to have EVIT, whereas 26 (21.7%) patients in the testing set had EVIT. When the radiomics signature was added, the combined model showed improved discrimination performance compared to the clinicoradiological model (area under the curves [AUCs] 0.911 vs. 0.772 in the training set; AUCs 0.847 vs. 0.746 in the testing set) and could divide HCC patients into three strata of low, intermediate, or high risk in the two sets. Decision curve analysis demonstrated that the two models were clinically useful, and the combined model provided greater benefits for discriminating patients than the clinicoradiological model. Conclusions This study presents a model that integrates clinicoradiological predictors and CT-based radiomics signature that could provide a preoperative individualized prediction of EVIT in patients with HCC.

Geometrical accuracy of an X-ray Computed Tomography (CT) system is crucial to achieve high quality tomographic reconstructions. Methods to correct the resulting geometric artifacts have been comprehensively described in the past few years. Deep convolution neural network is increasingly used in CT imaging, which has a great potential in image feature learning and processing tasks. In this work, a geometric artifact correction method exploiting generative adversarial networks (GAN) is developed. The U-Net structure is employed as the generator of the network to extract the CT image features with geometric artifacts. The convolutional neural network (CNN) based on image block perception acts as a discriminator for the network, further constraining the optimization of the generator. The proposed method has been shown experimentally feasible for geometric artifacts correction in circular cone beam CT by performing more accurate feature extraction. The peak signal to noise ratio (PSNR) of the corrected phantom images increased by 11.812 on average, and the root mean square error (RMSE) decreased by 9.982 on average.

Cone-beam computed tomography (CBCT) enables three-dimensional imaging of the internal structure of objects in a non-invasive way with high accuracy. Practical misalignment of the CBCT system causes geometric artefacts in reconstructed images, which seriously degrades image quality in ways such as detail loss and decreased spatial resolution. This leads to inaccurate distinction of defects in detection, especially in precise industrial fields like aerospace and instrument manufacturing. This paper presents a method to reduce the geometric artefacts based on a data-driven strategy, which is an end-to-end modified fully convolutional neural network (M-FCNN). The designed M-FCCN contains five convolution layers and five deconvolution layers for feature extraction and output image rebuilding, respectively. In addition, the pooling layer is not used in the designed M-FCNN, considering the preservation of details in the reconstructed image. In this M-FCCN, artefact images with different features have been trained separately. After training, the M-FCNN can be applied to directly reduce geometric artefacts in the reconstructed image. The designed M-FCNN has been demonstrated with different types of synthetic data and has achieved accurate results. It is also validated with practical data, including carbon composite and medical oral phantoms with comparable quality to phantom-based methods, proving that it is an effective way to reduce geometric artefacts in the image domain by means of a data-driven strategy.

Purpose: Metal artifact reduction (MAR) is a major problem and a challenging issue in x-ray computed tomography (CT) examinations. Iterative reconstruction from sinograms unaffected by metals shows promising potential in detail recovery. This reconstruction has been the subject of much research in recent years. However, conventional iterative reconstruction methods easily introduce new artifacts around metal implants because of incomplete data reconstruction and inconsistencies in practical data acquisition. Hence, this work aims at developing a method to suppress newly introduced artifacts and improve the image quality around metal implants for the iterative MAR scheme. Methods: The proposed method consists of two steps based on the general iterative MAR framework. An uncorrected image is initially reconstructed, and the corresponding metal trace is obtained. The iterative reconstruction method is then used to reconstruct images from the unaffected sinogram. In the reconstruction step of this work, an iterative strategy utilizing unmatched projector/backprojector pairs is used. A ramp filter is introduced into the back-projection procedure to restrain the inconsistency components in low frequencies and generate more reliable images of the regions around metals. Furthermore, a constrained total variation (TV) minimization model is also incorporated to enhance efficiency. The proposed strategy is implemented based on an iterative FBP and an alternating direction minimization (ADM) scheme, respectively. The developed algorithms are referred to as “iFBP-TV” and “TV-FADM,” respectively. Two projection-completion-based MAR methods and three iterative MAR methods are performed simultaneously for comparison. Results: The proposed method performs reasonably on both simulation and real CT-scanned datasets. This approach could reduce streak metal artifacts effectively and avoid the mentioned effects in the vicinity of the metals. The improvements are evaluated by inspecting regions of interest and by comparing the root-mean-square errors, normalized mean absolute distance, and universal quality index metrics of the images. Both iFBP-TV and TV-FADM methods outperform other counterparts in all cases. Unlike the conventional iterative methods, the proposed strategy utilizing unmatched projector/backprojector pairs shows excellent performance in detail preservation and prevention of the introduction of new artifacts. Conclusions: Qualitative and quantitative evaluations of experimental results indicate that the developed method outperforms classical MAR algorithms in suppressing streak artifacts and preserving the edge structural information of the object. In particular, structures lying close to metals can be gradually recovered because of the reduction of artifacts caused by inconsistency effects.

Dual-energy computed tomography (DECT) has capability to improve material differentiation, but most scanning schemes require two sets of full-scan measurements at different x-ray spectra, limiting its application to imaging system with incomplete scan. In this study, using one half-scan and a second limited-angle scan, we propose a DECT reconstruction method by exploiting the consistent information of gradient images at high- and low-energy spectra, which relaxes the requirement of data acquisition of DECT. Based on the theory of sampling condition analysis, the complementary support set of gradient images plays an important role in image reconstruction because it constitutes the sufficient and necessary condition for accurate CT reconstruction. For DECT, the gradient images of high- and low-energy CT images ideally share the same complementary support set for the same object. Inspired by this idea, we extract the prior knowledge of complementary support set (Pri-CSS) from the gradient image of the first half-scan CT image to promote the second limited-angle CT reconstruction. Pri-CSS will be incorporated into total variation regularization model in the form of constrains. Alternative direction method is applied to iteratively solve the modified optimization model, thereby deriving the proposed algorithm to recover low-energy CT image from limited-angle measurements. The qualitative and quantitative experiments on digital and real data are performed to validate the proposed method. The results show that the proposed method outperforms its counterparts and achieve high reconstruction quality for the designed scanning configuration.

In X-ray computed tomography (CT), it has been widely accepted that reconstruction methods inspired by compressive sensing (CS) can recover the image from an apparent reducing projection number. However, the quantitative study of sufficient sampling number of accurate reconstruction has high computational complexity, and it is limited by the size of the phantom. In this work, sampling condition of limited-angle accurate reconstruction is studied by testing the solution uniqueness in total variation (TV) minimization model. Solution uniqueness is verified by solving the l 1 -norm minimization problem. To solve the problem and improve the efficiency, we propose a fast algorithm which is based on the alternating direction method of multipliers (ADMM). In the limited-angle problem, the proposed method quantifies the number of projection acquisitions for accurate reconstruction. The experimental results indicate that the proposed method is more computationally efficient than the existing method. And our method can reduce the limit of phantom size because the computational cost of our algorithm is approximately equivalent to the same size reconstruction problem.

Limited-angle computed tomography (CT) image reconstruction is a challenging problem in the field of CT imaging. In some special applications, limited by the geometric space and mechanical structure of the imaging system, projections can only be collected with a scanning range of less than 90&deg, We call this kind of serious limited-angle problem the ultra-limited-angle problem, which is difficult to effectively alleviate by traditional iterative reconstruction algorithms. With the development of deep learning, the generative adversarial network (GAN) performs well in image inpainting tasks and can add effective image information to restore missing parts of an image. In this study, given the characteristic of GAN to generate missing information, the sinogram-inpainting-GAN (SI-GAN) is proposed to restore missing sinogram data to suppress the singularity of the truncated sinogram for ultra-limited-angle reconstruction. We propose the U-Net generator and patch-design discriminator in SI-GAN to make the network suitable for standard medical CT images. Furthermore, we propose a joint projection domain and image domain loss function, in which the weighted image domain loss can be added by the back-projection operation. Then, by inputting a paired limited-angle/180&deg, sinogram into the network for training, we can obtain the trained model, which has extracted the continuity feature of sinogram data. Finally, the classic CT reconstruction method is used to reconstruct the images after obtaining the estimated sinograms. The simulation studies and actual data experiments indicate that the proposed method performed well to reduce the serious artifacts caused by ultra-limited-angle scanning.

Iterative image reconstruction methods based on conventional total variation (TV) for computed tomography imaging may have limitations for specific applications or situations, such as images with non-sparse gradient parts, complicated textures, or severely contaminated data sets. For these cases, encoders in optimization programs with only TV-based terms may not be sufficient for high-precision image reconstruction. Here, we incorporate a new regularization method, which is based on a block matching local SVD operator, in the reconstruction program. In addition, we develop the corresponding algorithm, which is based on projection onto convex sets and the alternating direction method of multipliers. Theoretically, the proposed method indicates improved sparse representations for complicated textures. Necessary description and discussion on the design of the proposed algorithm are provided. Preliminary results obtained using the simulation data set and the real CT projection verify the capabilities of the proposed algorithm.

In medical imaging many conventional regularization methods, such as total variation or total generalized variation, impose strong prior assumptions which can only account for very limited classes of images. A more reasonable sparse representation frame for images is still badly needed. Visually understandable images contain meaningful patterns, and combinations or collections of these patterns can be utilized to form some sparse and redundant representations which promise to facilitate image reconstructions. In this work, we propose and study block matching sparsity regularization (BMSR) and devise an optimization program using BMSR for computed tomography (CT) image reconstruction for an incomplete projection set. The program is built as a constrained optimization, minimizing the L1-norm of the coefficients of the image in the transformed domain subject to data observation and positivity of the image itself. To solve the program efficiently, a practical method based on the proximal point algorithm is developed and analyzed. In order to accelerate the convergence rate, a practical strategy for tuning the BMSR parameter is proposed and applied. The experimental results for various settings, including real CT scanning, have verified the proposed reconstruction method showing promising capabilities over conventional regularization.

Purpose Low-dose computed tomography (CT) imaging has been widely explored because it can reduce the radiation risk to human bodies. This presents challenges in improving the image quality because low radiation dose with reduced tube current and pulse duration introduces severe noise. In this study, we investigate block-matching sparsity regularization (BMSR) and devise an optimization problem for low-dose image reconstruction. Method The objective function of the program is built by combining the sparse coding of BMSR and analysis error, which is subject to physical data measurement. A practical reconstruction algorithm using hard thresholding and projection-onto-convex-set for fast and stable performance is developed. An efficient scheme for the choices of regularization parameters is analyzed and designed. Results In the experiments, the proposed method is compared with a conventional edge preservation method and adaptive dictionary-based iterative reconstruction. Experiments with clinical images and real CT data indicate that the obtained results show promising capabilities in noise suppression and edge preservation compared with the competing methods. Conclusions A block-matching-based reconstruction method for low-dose CT is proposed. Improvements in image quality are verified by quantitative metrics and visual comparisons, thereby indicating the potential of the proposed method for real-life applications.

For applications of imaging large objects by small planar detectors in helical cone-beam computed tomography (CT), the field-of-view (FOV) half-covered scanning technique is a preferred choice as it can almost double the FOV with only about half the cross-section of an object irradiated with x-rays at all projection views. However, the projections are transversely truncated, resulting in truncation errors in reconstructed images if no compensatory measures are taken. In this paper, the half-covered single slice rebinning method is developed, in which images are reconstructed by performing two steps of rebinning followed by a three-dimensional (3D) filtered-backprojection (FBP) -based algorithm. The algorithm rebins the half-covered cone-beam projections into parallel-beam data which covers the whole cross-section of the object, thus resulting in nearly no truncation errors in the reconstructed images. Numerical simulations and experimental results are presented to demonstrate the algorithm and to compare it with existing algorithms. Preliminary results indicate the proposed half-covered single slice rebinning algorithm improves reconstruction quality.

Objective We aimed to study the pathology underlying traumatic temporomandibular joint ankylosis (TMJA). Study Design Specimens from 10 patients with traumatic TMJA were categorized using the Sawhney classification and were decalcified and stained with hematoxylin-eosin, alcian blue/periodic acid–Schiff, alizarin red, and Masson stains. Immunostaining with anti-CD34 antibody was performed. Computed tomography and pathologic findings were compared. Results Ankylosed areas consisted of fibrocartilaginous tissues. Bone formation occurred by osteophyte extension from the osteochondral surface toward the mass center. Endochondral ossification and osteophyte proliferation, alone or simultaneously, participated in bony ankylosis. Sequestra in the cartilaginous ankylosis preferentially formed bony bridges. Newly formed capillaries participated in ossification from the bony surface of the bone-cartilage junction; bone formed around the capillaries. Osteoclasts were present at the capillary tips. Conclusions Types II and III were cartilaginous-bony ankylosis, with similar components. Bony traumatic TMJA was formed by osteophyte proliferation and endochondral ossification.

Few-view computed tomography (CT) reconstruction has had wide utilization in reducing radiation dose in medical imaging. But it is an ill-posed mathematical problem caused by insufficient sampling. Additional sparsity prior of images can make contributions to solving this kind of problem and has been the topical subject of few-view reconstruction. Conventional regularization methods usually utilize pre-constructed sparsity, such as total variation (TV) and wavelet (WL) transformation. However, these fixed sparsity transformations cannot fully exhibit their advantages when the pre-constructed sparsity of reconstruction images is not suitable for the initial regularization model. To solve this problem, we propose a data driven tight frames regularization algorithm (DDTR) for few-view reconstruction. Data driven regularization introduces the learning strategy to update the wavelet basis based on the results of iterative reconstruction. This help to provide the optimal sparse approximations of reconstruction image for the proposed model. Alternating direction method (ADM) are utilized to efficiently resolve the DDTR algorithm in this paper. The experimental results and quantitative evaluation indicate that DDTR algorithm outperforms the traditional sparsity-exploiting methods in details reservation and reconstructs a higher quality image.

Iterative reconstruction algorithms for computed tomography (CT) through total variation (TV) regularization can provide accurate and stable reconstruction results. TV minimization is the L1-norm of gradient-magnitude images and can be regarded as a convex relaxation method to replace the L0 norm. In this study, a fast and efficient algorithm, which is named a weighted difference of L1 and L2 (L1 - αL2) on the gradient minimization, was proposed and investigated. The new algorithm provides a better description of sparsity for the optimization-based algorithms than TV minimization algorithms. The alternating direction method is an efficient method to solve the proposed model, which is utilized in this study. Both simulations and real CT projections were tested to verify the performances of the proposed algorithm. In the simulation experiments, the reconstructions from the proposed method provided better image quality than TV minimization algorithms with only 7 views in 180 degrees, which is also computationally faster. Meanwhile, the new algorithm enabled to achieve the final solution with less iteration numbers.

The reconstructed image may be distorted by geometry artifacts due to the presence of mechanical misalignments in circular cone-beam computed tomography (CT) system. To avoid geometry artifacts intervention, a novel geometric calibration method belonging to the iterative-type methods, which uses a dedicated calibration phantom to determine all geometry parameters, is presented in this paper. The phantom consists of two steel balls, and the trajectories of two ball centers are two circles in circular scan. Minimization of the errors between the predicted trajectories of ball center and the ideal circles is the principle in the proposed method. In summary, the proposed method consists of three parts. Firstly, an efficient description of geometry which reduces the number of geometry parameters from 7 to 4 is stated. Then, a nonlinear least square model depending on the 4 parameters is derived. At last, the Levenberg–Marquardt algorithm (LMA) is applied to solve the optimization problem. We implemented the proposed method and the classical analytic method presented by Noo et al. on real data in our laboratory cone-beam CT. The results indicated that our method could provide satisfactory reconstructed images as good as Noo's method.

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.

International audience; Projection and back-projection are the most computationally intensive parts in Computed Tomography (CT) reconstruction, and are essential to acceleration of CT reconstruction algorithms. Compared to back-projection, parallelization efficiency in projection is highly limited by racing condition and thread unsynchronization. In this paper, a strategy of Fixed Sampling Number Projection (FSNP) is proposed to ensure the operation synchronization in the ray-driven projection with Graphical Processing Unit (GPU). Texture fetching is also used utilized to further accelerate the interpolations in both projection and back-projection. We validate the performance of this FSNP approach using both simulated and real cone-beam CT data. Experimental results show that compare to the conventional approach, the proposed FSNP method together with texture fetching is 10~16 times faster than the conventional approach based on global memory, and thus leads to more efficient iterative algorithm in CT reconstruction

With the development of compressive sensing theory, image reconstruction from few-view projections has received considerable research attentions in the field of computed tomography (CT). Total-variation- (TV-) based CT image reconstruction has been shown to be experimentally capable of producing accurate reconstructions from sparse-view data. In this study, a distributed reconstruction algorithm based on TV minimization has been developed. This algorithm is very simple as it uses the alternating direction method. The proposed method can accelerate the alternating direction total variation minimization (ADTVM) algorithm without losing accuracy.

X-ray computed tomography (CT) is a powerful and common inspection technique used for the industrial non-destructive testing. However, large-sized and heavily absorbing objects cause the formation of artifacts because of either the lack of specimen penetration in specific directions or the acquisition of data from only a limited angular range of views. Although the sparse optimization-based methods, such as the total variation (TV) minimization method, can suppress artifacts to some extent, reconstructing the images such that they converge to accurate values remains difficult because of the deficiency in continuous angular data and inconsistency in the projections. To address this problem, we use the idea of regional enhancement of the true values and suppression of the illusory artifacts outside the region to develop an efficient iterative algorithm. This algorithm is based on the combination of regional enhancement of the true values and TV minimization for the limited angular reconstruction. In this algorithm, the segmentation approach is introduced to distinguish the regions of different image knowledge and generate the support mask of the image. A new regularization term, which contains the support knowledge to enhance the true values of the image, is incorporated into the objective function. Then, the proposed optimization model is solved by variable splitting and the alternating direction method efficiently. A compensation approach is also designed to extract useful information from the initial projections and thus reduce false segmentation result and correct the segmentation support and the segmented image. The results obtained from comparing both simulation studies and real CT data set reconstructions indicate that the proposed algorithm generates a more accurate image than do the other reconstruction methods. The experimental results show that this algorithm can produce high-quality reconstructed images for the limited angular reconstruction and suppress the illusory artifacts caused by the deficiency in valid data.

The speed of image reconstruction is an important indicator of the three-dimensional Computed Tomography (CT) imaging systems. GPU has become a focus of the research in CT reconstruction due to its characteristic of parallel computing. In this paper, the acceleration of backprojection, which is the most time consuming step of reconstruction, was implemented on multi-GPU hardware and OpenCL platform. The optimization techniques of parallelization strategy and memory management were explored and studied. The experimental results showed that the method obtained a considerable speedup, and the calculation times of total backprojection using 2 GPUs that were 1.67 times faster than using a single GPU. The method may be taken as the useful reference for researchers working on GPU applications and high-performance applications.

There's great influence on the reconstructed image by X-ray scatter in cone-beam computed tomography (CBCT). X-ray scatter correction technique is one of the hot research fields in 3D imaging of CBCT. A hybrid scatter correction method is proposed, which combines the beam attenuation grid and the beam stop block. Combined with the scatter correction equipments, a scatter correction algorithm is designed to reduce the X-ray scatter in this paper. The scatter correction performance is investigated by real experiments with an industrial metallic bolt. The results showed that the proposed method could more effectively suppress the scatter artifacts than which based on an attenuation grid alone.

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.

The high radiation dose in computed tomography (CT) scans increases the lifetime risk of cancer, which becomes a major clinical concern. The backprojection-filtration (BPF) algorithm could reduce the radiation dose by reconstructing the images from truncated data in a short scan. In a dental CT, it could reduce the radiation dose for the teeth by using the projection acquired in a short scan, and could avoid irradiation to the other part by using truncated projection. However, the limit of integration for backprojection varies per PI-line, resulting in low calculation efficiency and poor parallel performance. Recently, a tent BPF has been proposed to improve the calculation efficiency by rearranging the projection. However, the memory-consuming data rebinning process is included. Accordingly, the selective BPF (S-BPF) algorithm is proposed in this paper. In this algorithm, the derivative of the projection is backprojected to the points whose x coordinate is less than that of the source focal spot to obtain the differentiated backprojection. The finite Hilbert inverse is then applied to each PI-line segment. S-BPF avoids the influence of the variable limit of integration by selective backprojection without additional time cost or memory cost. The simulation experiment and the real experiment demonstrated the higher reconstruction efficiency of S-BPF.

In cone-beam computed tomography (CBCT), there are often cases where the size of the specimen is larger than the field of view (FOV) (referred to as over FOV-sized (OFS)). To acquire the complete projection data for OFS objects, some scan modes have been developed for long objects and short but over-wide objects. However, these modes still cannot meet the requirements for both longitudinally long and transversely wide objects. In this paper, we propose a multiple helical scan mode and a corresponding reconstruction algorithm for both longitudinally long and transversely wide objects. The simulation results show that our model can deal with the problem and that the results are acceptable, while the OFS object is twice as long compared with the FOV in the same latitude.