Computed Tomography Image Reconstruction Research Articles

Computed tomography image reconstruction from few views via Log-norm total variation minimization

In this paper, we propose an iterative algorithm for the computed tomography (CT) image reconstruction from severely under-sampled data. Instead of using ℓ1-norm sparse regularization, the proposed algorithm utilizes the non-convex Log-norm penalty (LSP) of the gradient-magnitude images (GMI), which can overcome the disadvantages tendency of uniformly penalize the signal amplitude and underestimate the high-amplitude components. To rise to the challenges introduced by the non-convex regularization, we employ the difference of convex framework to decompose the objective function into a separable ℓ1 type problem and draw its connection to the alternating direction method (ADM). We show that any cluster points of the sequence generated by the proposed algorithm converge to a stationary point. The simulation result demonstrates our conclusions and indicates that the algorithm proposed in this paper can obviously improve the reconstruction quality.

Application of 3D Computed Tomography Reconstruction Images to Assess the Thickness and Dimensions of the Posterior Palatal Seal Area.

Few studies have been reported on the scientific measurements of the thickness and dimensions of the posterior palatal seal (PPS) area. The purpose of this study is to measure and analyze the thickness of palatal mucosa by using a three-dimensional (3D) model reconstructed with computed tomography (CT) images and to present objective values by identifying the PPS area. The CT images were reconstructed as a 3D model by separating the maxillary palate mucosa and teeth. Each reconstructed model was analyzed and the thickness was measured at 93 crossing points of each divided plane. The dimension of the PPS area was measured and the right and left dimensions of the PPS area were compared. The thickness of the palatal mucosa was thicker toward the posterior area. The thickness increased in the lateral direction and decreased again. In the PPS area, the mean dimension between the rearmost of anterior border and the most posterior line was 2.19 mm and the mean dimension between the forefront of anterior border and the most posterior line was 5.19 mm in the right side and 5.16 mm in the left side. The mean dimension from the center of the palate to the right most forward point was 6.85 mm, and the left was 7.36 mm. The new measurement method of palatal mucosal thickness is noninvasive, accurate, and easy to store and study, so it can be used effectively in planning and manufacturing the maxillary complete denture in the digital workflows.

Damage Constitutive Model Based on Energy Dissipation for Frozen Sandstone Under Triaxial Compression Revealed by X-Ray Tomography

A series of triaxial compression tests, with real-time computed tomography (CT) scanning performed in the loading process, were conducted on frozen sandstones at confining pressures of 0, 1, 2, and 3 MPa at different temperatures of −2, −6, −10, and 20 °C, respectively. The experimental results indicate that the strength of frozen sandstone increases with a decrease in temperature. The strength of frozen sandstone increases with an increase in the confining pressure at freezing temperatures. In the loading process, the evolution law of the CT value of the whole sandstone is explored, and three-dimensional reconstruction of CT images, which denote postfailure models of frozen sandstone, is also performed. According to energy dissipation theory, an isotropic damage variable is introduced. Based on the CT value of the whole sandstone, the concept of the compaction coefficient K, which describes the compaction degree, is proposed to modify the damage model obtained by Lemaitre’s strain equivalence hypothesis. The validity of the model is verified by comparing its computed results with the experimental results obtained from triaxial compression tests. The results indicated that the modified damage model can adequately predict the stress-strain relationship of the frozen sandstone.

Few-view computed tomography image reconstruction using mean curvature model with curvature smoothing and surface fitting

The edge and curve of an image surface are crucial visual cues in vision psychology. Studies show that human beings can effectively process curvature information, such as distinguishing the concavity and convexity of an image. This finding indicates that curvature is essential for a desired image to be felt authentic and real. In this paper, a novel few-view computed tomography (CT) image reconstruction model is proposed based on mean curvature (MC). Similar to the total variation model, the MC employs the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm to utilize the sparse prior information. Constructing efficient numerical algorithms for minimizing the MC model is significant due to the associated high-order Euler-Lagrange equations. A two-step numerical method, including curvature smoothing and surface fitting, is presented to solve the proposed model, which can be stably and efficiently solved by the alternating direction minimization. By applying the variable splitting method, the explicit solutions of the corresponding subproblems can be efficiently and quickly approximated by fast Fourier transform and the proximal point method. The accuracy and efficiency of the simulated and real data are qualitatively and quantitatively evaluated to verify the efficiency and feasibility of the proposed method. Comparisons with conventional algorithms demonstrate that the proposed approach has considerable advantages in few-view CT reconstruction problems.

Osteochondral regeneration using constructs of mesenchymal stem cells made by bio three‐dimensional printing in mini‐pigs

Osteoarthritis is a major joint disease that has been extensively investigated in humans and in model animals. In this study, we examined the regeneration of articular cartilage and subchondral bone using artificial scaffold-free constructs composed of adipose tissue-derived mesenchymal stem cells (AT-MSCs) created using bio three-dimensional (3D) printing with a needle-array. Printed constructs were implanted into osteochondral defects created in the right femoral trochlear groove of six mini-pigs, using femoral defects created in the left femurs as controls. Repair within the defects was evaluated at 3 and 6 months post-implantation using computed tomography (CT) and magnetic resonance (MR) imaging. The radiolucent volume (RV, mm3 ) in the defects was calculated using multi-planar reconstruction of CT images. MR images were evaluated based on a modified 2D- MOCART (magnetic resonance observation of cartilage repair tissue) grading system. Gross and microscopic pathology were scored according to the ICRS (International Cartilage Repair Society) scale at 6 months after implantation. The percentage RV at 3 months postoperation was significantly lower in the implanted defects than in the controls, whereas total scores based on the MOCART system were significantly higher in the implanted defects as compared with the controls. Although there were no statistical differences in the gross scores, the average histological scores were significantly higher in the implanted defects than in the controls. To our knowledge, this is the first report to suggest that artificial scaffold-free 3D-printed constructs of autologous AT-MSCs can be aid in the osteochondral regeneration in pigs. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:1398-1408, 2019.

Low-Dose Computed Tomography Image Super-Resolution Reconstruction via Random Forests.

Aiming at reducing computed tomography (CT) scan radiation while ensuring CT image quality, a new low-dose CT super-resolution reconstruction method based on combining a random forest with coupled dictionary learning is proposed. The random forest classifier finds the optimal solution of the mapping relationship between low-dose CT (LDCT) images and high-dose CT (HDCT) images and then completes CT image reconstruction by coupled dictionary learning. An iterative method is developed to improve robustness, the important coefficients for the tree structure are discussed and the optimal solutions are reported. The proposed method is further compared with a traditional interpolation method. The results show that the proposed algorithm can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) and has better ability to reduce noise and artifacts. This method can be applied to many different medical imaging fields in the future and the addition of computer multithreaded computing can reduce time consumption.

XCT image reconstruction by a modified superiorized iteration and theoretical analysis

ABSTRACT In this paper, we propose an improved iteration superiorization method for X-ray computed tomography image reconstruction. We simplify the classic superiorized iteration by removing two constraints imposed on the perturbation. A novel method is proposed to determine the perturbation amount and direction for the superiorized iteration simultaneously. Some theoretical properties (convergence for instance) of the superiorized iteration sequence with the proposed perturbation are analysed. We present a general proof for the convergence of ART-like iterations with summable perturbations. In addition, we prove the convergence of simultaneous iterations without the summable perturbation assumption. Experiments on simulated and real data not only verify the theoretical result but also show that the proposed algorithm is superior to the classic superiorized iteration and can reconstruct desirable images.

A Reweighted Total Variation Algorithm with the Alternating Direction Method for Computed Tomography

A variety of alternating direction methods have been proposed for solving a class of optimization problems. The applications in computed tomography (CT) perform well in image reconstruction. The reweighted schemes were applied in l1-norm and total variation minimization for signal and image recovery to improve the convergence of algorithms. In this paper, we present a reweighted total variation algorithm using the alternating direction method (ADM) for image reconstruction in CT. The numerical experiments for ADM demonstrate that adding reweighted strategy reduces the computation time effectively and improves the quality of reconstructed images as well.

CT IMAGE RECONSTRUCTION ON A LOW DIMENSIONAL MANIFOLD.

The patch manifold of a natural image has a low dimensional structure and accommodates rich structural information. Inspired by the recent work of the low-dimensional manifold model (LDMM), we apply the LDMM for regularizing X-ray computed tomography (CT) image reconstruction. This proposed method recovers detailed structural information of images, significantly enhancing spatial and contrast resolution of CT images. Both numerically simulated data and clinically experimental data are used to evaluate the proposed method. The comparative studies are also performed over the simultaneous algebraic reconstruction technique (SART) incorporated the total variation (TV) regularization to demonstrate the merits of the proposed method. Results indicate that the LDMM-based method enables a more accurate image reconstruction with high fidelity and contrast resolution.

Scattering Noise Model Enhanced EM-TV Algorithm for Benchtop X-ray Fluorescence Computed Tomography Image Reconstruction

Current benchtop x-ray fluorescence computed tomography (XFCT) devices, which use x-ray tubes to stimulate x-ray fluorescence (XRF) photons, suffer from the contamination of Compton scatter background produced by the polychromatic incident beam. The conventional maximum-likelihood expectation-maximization (ML-EM) algorithm only considers the noise model of the XRF signal, which results in high statistical noise in reconstructed images caused by scattered photons. In this study, we proposed a scattering noise model enhanced EM-TV algorithm for benchtop XFCT image reconstruction in order to reduce the noise of scatter background and improve the sensitivity of XFCT images. The statistical noise of scattered photons was considered in the likelihood function and the EM iteration step was modified correspondingly to suppress the statistical noise caused by Compton scattered photons. The robustness of the EM iteration was improved by applying the reweighted total variation (TV) norm as the penalty function. Numerical simulations and imaging experiments of a PMMA phantom consisting of gadolinium (Gd) solutions were performed to validate the proposed algorithm. The phantom was irradiated by a cone-beam polychromatic source and the projection was recorded by a linear-array photon counting detector. For comparison, the XFCT images of Gd were reconstructed using different algorithms. Results indicate that compared with the conventional ML-EM algorithm, the proposed algorithm can obtain XFCT images with lower background noise and higher contrast, which may further improve the sensitivity and image performance of current benchtop XFCT systems.

稀疏张量约束的低剂量CT图像重建

稀疏张量约束的低剂量CT图像重建

A new adaptive-weighted total variation sparse-view computed tomography image reconstruction with local improved gradient information.

Inspired by the compressed sensing (CS) theory, introducing priori information of sparse image into sparse-view reconstruction algorithm of computed tomography (CT) can improve image quality. In recent years, as a special case of CS, total variation (TV) reconstruction algorithm that uses both image sparsity and prior information of edge direction have attracted much research interest in sparse-view image reconstruction due to its ability to preserve image edges. In this paper, we propose a new adaptive-weighted total variation (NAWTV) algorithm for CT image reconstruction, which is derived by considering local gradient direction continuity and the anisotropic edge property. The anisotropic edge property is used to consolidate the image sparsity, where the associated weights are expressed as a combination of exponential and cosine function. The weights can also be adjusted adaptively according the local image intensity gradient. The NAWTV algorithm is numerically implemented with gradient descent method. The typical Shepp-Logan phantom and FORBILD head phantom are employed to perform image reconstruction simulation. To evaluate performance of NAWTV algorithm, we compared it with TV and AwTV reconstruction algorithms in experiments. Numerical experimental results verified the effectiveness and feasibility of the proposed algorithm. Comparison results also showed that the NAWTV algorithm achieved a satisfactory performance in suppressing artifacts and preserving the edge structure details information of the reconstructed image.

Use of X-ray computed tomography to investigate the meso-mechanisms of localized deformation in soil-rock mixture under uniaxial compression

This article aims at investigating the meso-scale mechanisms that result in failure by strain localization in soil and rock mixtures (SRM). While the macroscopic failure of SRM has been widely studied, the physical meso-scale mechanisms governing the SRM deformation have not been well understood. A uniaxial compressive test on the SRM sample was performed under tomographic monitoring using a 450 kV industrial X-ray computed tomography (CT). A series of 2D CT images were obtained by carrying out CT scanning at key points throughout the test and from different positions in the sample. The damage evolution of the meso-element and the rock block ID tracking during deformation have been studied using the reconstructed CT images. This work suggests that formation and propagation of localized bands in SRM are mainly controlled by the existing rock blocks, which were the results of interaction between rock blocks and the soil matrix.

Unconventional Facial Entry Points Confirmed Using a 3D CT Reconstruction-Guided Stereotactic Approach to Radiofrequency Thermocoagulation for the Treatment of Trigeminal Neuralgia: A Series of Case Reports.

Patients with trigeminal neuralgia who are refractory to medical therapy may choose to undergo Gasserian ganglion percutaneous radiofrequency thermocoagulation. However, in cases where the foramen ovale is difficult to access due to various anatomical anomalies, the typical estimation of the facial entry point is suboptimal. Three-dimensional computed tomography reconstruction imaging performed before surgery revealed anatomical variations in each of the four adult patient cases that made it more difficult to successfully access the foramen ovale (FO) for percutaneous radiofrequency thermocoagulation. Using measurements collected from preoperative imaging that showed each specific anatomical variation in the FO, researchers marked alternate facial entry points that would allow successful probe placement into the FO and recorded the arc angle data in the stereotactic instrument. Patients were evaluated during follow-up visits ranging from seven to 26 months after surgery and asked to rate postoperative pain using a visual analog scale. These scores decreased from 10 to 3 in all four patients by the third day after the procedure. There were no permanent complications or morbidities from the surgery. One patient experienced mild facial numbness; however, this side effect subsided within three months after surgery. During the follow-up period, no patient reported pain recurrence. The expectation for clinicians approaching trigeminal nerve block using a peri-oral approach should be to expect a great degree of potential variability in terms of both distances from the corner of the mouth and needle angle taken to successfully navigate the anatomy and access the foramen ovale.

A Joint Row and Column Action Method for Cone-Beam Computed Tomography

The inversion of linear systems is fundamental in computed tomography (CT) reconstruction. Computational challenges arise when trying to invert large linear systems, as limited computing resources mean that only a part of the system can be kept in computer memory at any one time. In linear tomographic inversion problems, such as X-ray tomography, even a standard scan can produce millions of individual measurements and the reconstruction of X-ray attenuation profiles typically requires the estimation of a million attenuation coefficients. To deal with the large data sets encountered in real applications and to efficiently utilize modern graphics processing unit based computing architectures, combinations of iterative reconstruction algorithms and parallel computing schemes are increasingly applied. Whilst different parallel methods have been proposed, individual computations currently need to access either the entire set of observations or estimated X-ray absorptions, which can be prohibitive in many realistic applications. We present a fully parallelizable CT image reconstruction algorithm where each computation node works on arbitrary partial subsets of the data and the reconstructed volume. We further develop a nonhomogeneously randomized selection criterion which guarantees that submatrices of the system matrix are selected more frequently if they are dense, thus maximizing information flow through the algorithm. We compare our algorithm with block alternating direction method of multipliers and show that our method is significantly faster for CT reconstruction.

A variational proximal alternating linearized minimization in a given metric for limited-angle CT image reconstruction

• A nonconvex and nonsmooth optimization model is investigated for limited-angle CT imaging. • The proposed method can avoid computing the inverse of a large system matrix. • We show that each bounded sequence generated by our method globally converges to a critical point. • The results by our method are shown superior than some classical CT reconstruction methods by real data experiments. Due to the restriction of computed tomography (CT) scanning environment, the acquired projection data may be incomplete for exact CT reconstruction. Though some convex optimization methods, such as total variation minimization based method, can be used for incomplete data reconstruction, the edge of reconstruction image may be partly distorted for limited-angle CT reconstruction. To promote the quality of reconstruction image for limited-angle CT imaging, in this paper, a nonconvex and nonsmooth optimization model was investigated. To solve the model, a variational proximal alternating linearized minimization (VPALM) method based on proximal mapping in a given metric was proposed. The proposed method can avoid computing the inverse of a huge system matrix thus can be used to deal with the larger-scale inverse problems. What’s more, we show that each bounded sequence generated by VPALM globally converges to a critical point based on the Kurdyka–Lojasiewicz property . Real data experiments are used to demonstrate the viability and effectiveness of VPALM method, and the results show that the proposed method outperforms two classical CT reconstruction methods.

DPP-4 inhibitor impedes lipopolysaccharide-induced osteoclast formation and bone resorption in vivo

ObjectivesDipeptidyl peptidase 4 (DPP-4) inhibition is a new therapeutic strategy for type 2 diabetic patients. DPP-4 has been reported to enhance inflammation. However, the effect of DPP-4 inhibition on inflammation remains unknown. Lipopolysaccharide (LPS) is a strong inducer of inflammation and osteoclast formation. In this study, we investigated in vivo effects of DPP-4 inhibition on LPS-induced osteoclast formation and bone resorption, as well as in vitro effects of DPP-4 inhibition on RANKL-induced osteoclastogenesis and TNF-α-induced osteoclastogenesis. MethodsLPS with or without a DPP-4 inhibitor was subcutaneously injected into mouse calvaria for 5 days. Histological sections of calvaria were stained for tartrate-resistant acid phosphatase, and osteoclast numbers were determined. The ratio of calvaria bone resorption was evaluated via microfocal computed tomography reconstruction images. ResultsOsteoclast number and bone resorption were significantly lower in mice that underwent LPS and DPP-4 inhibitor co-administration than in those that underwent LPS administration alone. Moreover, RANKL, TNF-α, and M-CSF expression was reduced in the LPS and DPP-4 inhibitor co-administration group. In vitro, there were no direct effects of DPP-4 inhibitor or DPP-4 on RANKL- and TNF-α-induced osteoclastogenesis, or on LPS-induced RANKL expression in stromal cells. Nevertheless, macrophages from LPS and DPP-4 inhibitor co-administered mice exhibited lower TNF-α expression than macrophages from LPS-only mice. Notably, TNF-α expression was not reduced in LPS and DPP-4 inhibitor co-treated macrophages in vitro, compared with macrophages treated with LPS alone.

Computed Tomographic Trochlear Depth Measurement in Normal Dogs.

The main purpose of this study was to describe the relationship between patellar maximal craniocaudal thickness and femoral trochlear groove depth in normal dogs and to valuate the intra-observer or inter-observer variability of maximal trochlear depth and maximal patellar craniocaudal thickness using computed tomography. Trochlear groove depth and patellar maximal craniocaudal thickness of 40 limbs (20 dogs) were measured by three independent veterinarians using three-dimensional multiplanar reconstruction computed tomography images. The patellar maximal craniocaudal thickness/trochlear depth ratio was determined. The mean ratio of these stifles was 0.46 (range 0.24-0.70), meaning that the mean maximal depth of the trochlea was 46% of the mean maximal-patellar thickness. A wide range of maximal-patellar-craniocaudal-thickness/maximal trochlear-depth ratio was found suggesting that breed studies should be performed to determine a breed-specific patellar-thickness/trochlear-depth ratio. To make decisions on when and where to perform a sulcoplasty during patellar luxation surgery, patella/trochlea thickness relationship should be measured for each breed with patellar tracking from stifle hyperflexion to stifle hyperextension.

Investigation of maximum a posteriori probability expectation-maximization for image-based weighting spectral X-ray CT image reconstruction.

Development of spectral X-ray computer tomography (CT) equipped with photon counting detector has been recently attracting great research interest. This work aims to improve the quality of spectral X-ray CT image. Maximum a posteriori (MAP) expectation-maximization (EM) algorithm is applied for reconstructing image-based weighting spectral X-ray CT images. A spectral X-ray CT system based on the cadmium zinc telluride photon counting detector and a fat cylinder phantom were simulated. Comparing with the commonly used filtered back projection (FBP) method, the proposed method reduced noise in the final weighting images at 2, 4, 6 and 9 energy bins up to 85.2%, 87.5%, 86.7% and 85%, respectively. CNR improvement ranged from 6.53 to 7.77. Compared with the prior image constrained compressed sensing (PICCS) method, the proposed method could reduce noise in the final weighting images by 36.5%, 44.6%, 27.3% and 18% at 2, 4, 6 and 9 energy bins, respectively, and improve the contrast-to-noise ratio (CNR) by 1.17 to 1.81. The simulation study also showed that comparing with the FBP and PICCS algorithms, image-based weighting imaging using MAP-EM statistical algorithm yielded significant improvement of the CNR and reduced the noise of the final weighting image.

Association between Modic changes and endplate sclerosis: Evidence from a clinical radiology study and a rabbit model

PurposeTo analyse the presence of endplate sclerosis in patients with various types of Modic changes (MCs) and to confirm the results using a rabbit model. MethodsParticipants in the clinical study included 1023 consecutive inpatients with lumbar degenerative disease who attended the Department of Orthopaedics between August 2011 and August 2015. All patients underwent computed tomography (CT) and magnetic resonance imaging of the lumbar spine. In those patients with MCs, endplate sclerosis was evaluated from sagittally reconstructed CT images. In addition to the clinical study, MCs type I, II and III were initiated using a previously developed rabbit model of MCs. Specimens of MCs type I, II and III and normal endplates were harvested, bone mineral density and bone volume/tissue volume of “treated” vertebrae were evaluated using μCT and osteogenic protein expressions of runt-related transcription factor 2 and osteocalcin were assessed using immunohistochemical staining. Measurements were compared between vertebrae with normal endplates and those with different types of MCs. ResultsOf 1023 patients, 214 (20.9%) had MCs in one or more endplates; these changes affected 1044 (10.2%) of 10230 endplates. Type I, II and III MCs were seen in 164 (1.6%), 838(8.2%) and 40 (0.4%) endplates, respectively. Of 1044 endplates with MCs, 274 (26.2%) showed evidence of sclerosis on CT images including: 26/164 endplates (15.8%) with type I MCs, 208/838 (24.8%) with type II and 40/40 (100%) with type III. HU (CT value) ratios for sclerotic and nonsclerotic endplates with MCs were 2.0 ± 0.3 and 1.1 ± 0.1, respectively. In the animal study, the bone mineral density, bone volume/tissue volume and expression of runt-related transcription factor 2 and osteocalcin of endplates with type I and II MCs were higher than those of normal endplates and lower than those of endplates with type III MCs. ConclusionSclerosis can occur in endplates with any type of MCs. However, the clinical and animal study suggests that sclerosis is greatest in endplates showing type III MCs. The translational potential of this articleThe study showed that sclerosis can occur in endplates with MCs type I, II and III. In patients with endplate sclerosis on plain radiographs or CT scans, the endplate can still represent an inflammatory process associated with chronic lower back pain.