Image Reconstruction Algorithm For Computed Tomography Research Articles

Image reconstruction based on nonlinear diffusion model for limited-angle computed tomography

The problem of limited-angle computed tomography (CT) imaging reconstruction has a wide range of practical applications. Due to various factors such as high x-ray absorption, structural characteristics of the scanned object, and equipment limitations, it is often impractical to obtain a complete angular scan, resulting in limited-angle scan data. In this paper, we propose an iterative image reconstruction algorithm for limited-angle CT. The algorithm carries out a traditional CT reconstruction and a nonlinear diffusion process alternatively. Specifically, a subtle partial differential equation is constructed to guide the nonlinear diffusion process to eliminate limited-angle artifacts in the reconstructed image. Numerical experiments on both analytic data and real data validate the efficacy of the proposed nonlinear diffusion reconstruction algorithm. Furthermore, a linear diffusion reconstruction algorithm which combines a traditional CT reconstruction algorithm and a linear diffusion process is also presented in the paper.

CT target scanning in the diagnosis of solid pseudopapillary tumor of the pancreas

ObjectiveTo discuss the value of computed tomography (CT) iterative reconstruction technique combined with target scanning in the diagnosis of solid pseudopapillary tumor of the pancreas (SPTP).MethodsThe clinical information and CT examination data of 27 patients with SPTP were retrospectively analyzed, and the general condition and CT performance of the patients were observed. The CT image reconstruction algorithm of all patients used iterative reconstruction technique combined with the application of target scanning technique.ResultsA total of 27 patients were included in this study, including 6 males and 21 females, aged 14–72 years with a mean age of 39.6 ± 13.6 years. SPTP was more common in young and middle-aged females, with a low level of tumor markers, dominated by cystic-solid tumors. The combination of CT iterative reconstruction technology and targeted scanning revealed the following: the capsule of SPTP was clear and complete, where calcifications were visible, solid components were progressively enhanced, and rare pancreatic and bile duct dilation was seen. Tumors were cystic-solid in 18 of 27 patients with SPTP, of which the solid components showed isodensity or slightly low-density, with calcifications. The solid components and cyst walls were mildly enhanced during the arterial phase, and were progressively enhanced during the parenchymal phase, portal vein phase, and delayed phase, with their enhancement degree lower than that of normal pancreatic parenchyma, and pancreatic and bile duct dilation was rare. There were no statistical differences in tumor location, morphology, growth pattern, integrity of capsule, cystic or solid, calcifications, and enhancement features between the male group and the female group (P > 0.05).ConclusionThe iterative reconstruction combined with target scanning clearly displayed the CT features of tumors, helping the diagnosis and clinical treatment of the disease.

A highly accurate quantum optimization algorithm for CT image reconstruction based on sinogram patterns

Computed tomography (CT) has been developed as a nondestructive technique for observing minute internal images in samples. It has been difficult to obtain photorealistic (clean or clear) CT images due to various unwanted artifacts generated during the CT scanning process, along with the limitations of back-projection algorithms. Recently, an iterative optimization algorithm has been developed that uses an entire sinogram to reduce errors caused by artifacts. In this paper, we introduce a new quantum algorithm for reconstructing CT images. This algorithm can be used with any type of light source as long as the projection is defined. Assuming an experimental sinogram produced by a Radon transform, to find the CT image of this sinogram, we express the CT image as a combination of qubits. After acquiring the Radon transform of the undetermined CT image, we combine the actual sinogram and the optimized qubits. The global energy optimization value used here can determine the value of qubits through a gate model quantum computer or quantum annealer. In particular, the new algorithm can also be used for cone-beam CT image reconstruction and for medical imaging.

Dose-efficiency quantification of computed tomography systems using a model-observer.

Recent advances in computed tomography (CT) technology have considerably improved the quality of CT images and reduced radiation exposure in patients. At present, however, there is no generally accepted figure of merit (FOM) for comparing the dose efficiencies of CT systems. (i) To establish an FOM that characterizes the quality of CT images in relation to the radiation dose by means of a mathematical model observer and (ii) to evaluate the new FOM on different CT systems and image reconstruction algorithms. Images of a homogeneous phantom with four low-contrast inserts were acquired using three different CT systems at three dose levels and a representative protocol for CT imaging of low-contrast objects in the abdomen. The images were reconstructed using filtered-back projection and iterative algorithms. A channelized hotelling observer with difference-of-Gaussian channels was applied to compute the detectability ( ). This was done for each insert and each of the considered imaging conditions from square regions of interest (ROIs) that were (semi-)automatically centered on the inserts. The estimated detectabilities ( ) were averaged in the first step over the three dose levels ( ), and subsequently over the four contrast inserts ( ). All calculation steps included a dedicated assessment of the related uncertainties following accepted metrological guidelines. The determined detectabilities ( ) varied considerably with the contrast and diameter of the four inserts, as well as with the radiation doses and reconstruction algorithms used for image generation ( =1.3-5.5). Thus, the specification of a single detectability as an FOM is not well suited for comprehensively characterizing the dose efficiency of a CT system. A more comprehensive and robust characterization was provided by the averaged detectabilities and, in particular, . Our analysis reveals that the model observer analysis is very sensitive to the exact position of the ROIs. The presented automatable software approach yielded with the weighted detectability an objective FOM to benchmark different CT systems and reconstruction algorithms in a robust and reliable manner. An essential advantage of the proposed model-observer approach is that uncertainties in the FOM can be provided, which is an indispensable prerequisite for type testing.

RICT: Rotating image computed tomography with a one-to-one reversible image rotation algorithm.

The Mueller, Siddon and Joseph weighting algorithms are frequently used for projection and back-projection, which are relatively complicated when they are implemented in computer code. This study aims to reduce the actual complexity of the projection and back-projection. First, we neglect the exact shape of the pixel, so that its shadow is a rectangle projecting precisely to a detector bin, which implies that all the pixel weights are exactly 1 for each ray through them, otherwise are exactly 0. Next, a one-to-one reversible image rotation algorithm (RIRA) is proposed to compute the projection and back-projection, where two one-to-one mapping lists namely, U and V, are used to store the coordinates of a rotated pixel and its corresponding new coordinates, respectively. For each 2D projection, the projection is simply the column sum in each orientation according to the lists U and V. For each 2D back-projection, it is simply to arrange the projection to the corresponding column element according to the lists U and V. Thus, there is no need for an interpolation in the projection and back-projection. Last, a rotating image computed tomography (RICT) based on RIRA is proposed to reconstruct the image. Experiments show the RICT reconstructs a good image that is close to the result of filtered back-projection (FBP) method according to the RMSE, PSNR and MSSIM values. What's more, our weight, projection and back-projection are much easier to be implemented in computer code than the FBP method. This study demonstrates that the RIRA method has potential to be used to simplify many computed tomography image reconstruction algorithms.

Non-convex optimization based optimal bone correction for various beam-hardening artifacts in CT imaging.

Tube of X-ray computed tomography (CT) system emitting a polychromatic spectrum of photons leads to beam hardening artifacts such as cupping and streaks, while the metal implants in the imaged object results in metal artifacts in the reconstructed images. The simultaneous emergence of various beam-hardening artifacts degrades the diagnostic accuracy of CT images in clinics. Thus, it should be deeply investigated for suppressing such artifacts. In this study, data consistency condition is exploited to construct an objective function. Non-convex optimization algorithm is employed to solve the optimal scaling factors. Finally, an optimal bone correction is acquired to simultaneously correct for cupping, streaks and metal artifacts. Experimental result acquired by a realistic computer simulation demonstrates that the proposed method can adaptively determine the optimal scaling factors, and then correct for various beam-hardening artifacts in the reconstructed CT images. Especially, as compared to the nonlinear least squares before variable substitution, the running time of the new CT image reconstruction algorithm decreases 82.36% and residual error reduces 55.95%. As compared to the nonlinear least squares after variable substitution, the running time of the new algorithm decreases 67.54% with the same residual error.

Addressing CT metal artifacts using photon-counting detectors and one-step spectral CT image reconstruction.

The constrained one-step spectral CT image reconstruction (cOSSCIR) algorithm with a nonconvex alternating direction method of multipliers optimizer is proposed for addressing computed tomography (CT) metal artifacts caused by beam hardening, noise, and photon starvation. The quantitative performance of cOSSCIR is investigated through a series of photon-counting CT simulations. cOSSCIR directly estimates basis material maps from photon-counting data using a physics-based forward model that accounts for beam hardening. The cOSSCIR optimization framework places constraints on the basis maps, which we hypothesize will stabilize the decomposition and reduce streaks caused by noise and photon starvation. Another advantage of cOSSCIR is that the spectral data need not be registered, so that a ray can be used even if some energy window measurements are unavailable. Photon-counting CT acquisitions of a virtual pelvic phantom with low-contrast soft tissue texture and bilateral hip prostheses were simulated. Bone and water basis maps were estimated using the cOSSCIR algorithm and combined to form a virtual monoenergetic image for the evaluation of metal artifacts. The cOSSCIR images were compared to a "two-step" decomposition approach that first estimated basis sinograms using a maximum likelihood algorithm and then reconstructed basis maps using an iterative total variation constrained least-squares optimization (MLE+TV ). Images were also compared to a nonspectral TV reconstruction of the total number of counts detected for each ray with and without normalized metal artifact reduction (NMAR) applied. The simulated metal density was increased to investigate the effects of increasing photon starvation. The quantitative error and standard deviation in regions of the phantom were compared across the investigated algorithms. The ability of cOSSCIR to reproduce the soft-tissue texture, while reducing metal artifacts, was quantitativelyevaluated. Noiseless simulations demonstrated the convergence of the cOSSCIR and MLE+TV algorithms to the correct basis maps in the presence of beam-hardening effects. When noise was simulated, cOSSCIR demonstrated a quantitative error of -1 HU, compared to 2 HU error for the MLE+TV algorithm and -154 HU error for the nonspectral TV +NMAR algorithm. For the cOSSCIR algorithm, the standard deviation in the central iodine region of interest was 20 HU, compared to 299 HU for the MLE+TV algorithm, 41 HU for the MLE+TV +Mask algorithm that excluded rays through metal, and 55 HU for the nonspectral TV +NMAR algorithm. Increasing levels of photon starvation did not impact the bias or standard deviation of the cOSSCIR images. cOSSCIR was able to reproduce the soft-tissue texture when an appropriate regularization constraint value wasselected. By directly inverting photon-counting CT data into basis maps using an accurate physics-based forward model and a constrained optimization algorithm, cOSSCIR avoids metal artifacts due to beam hardening, noise, and photon starvation. The cOSSCIR algorithm demonstrated improved stability and accuracy compared to a two-step method of decomposition followed byreconstruction.

Increasing the Performance of the Iterative Computed Tomography Image Reconstruction Algorithms

Computed tomography (CT) imaging is an important diagnostic tool. CT imaging facilitates the internal rendering of a scanned object by measuring the attenuation of beams of X-ray radiation. CT employs a mathematical technique of image reconstruction; those techniques are classified as; analytical and iterative. The iterative reconstruction (IR) methods have been proven to be superior over the analytical methods, but due to their prolonged reconstruction time, those methods are excluded from routine use in clinical applications. In this paper the reconstruction time of an IR algorithm is minimized through the employment of an adaptive region growing segmentation method that focuses the image reconstruction process on a specified region, thus ignoring unwanted pixels that increase the computation time. This method is tested on the iterative algebraic reconstruction technique (ART) algorithm. Some phantom images are used in this paper to demonstrate the effects of the segmentation process. The simulation results are executed using MATLAB (version R2018b) programming language, and a computer system with the following specifications: CPU core i7 (2.40 GHz) for processing. Simulation results indicate that this method will reduce the reconstruction time of the iterative algorithms, and will enhance the quality of the reconstructed image.

Study of CT image reconstruction algorithm based on high order total variation

The traditional total variation (TV) minimization algorithm is an image reconstruction algorithm based on compressed sensing, which can accurately reconstruct images from sparse data or highly noisy data and has been widely used in low-dose computed tomography (CT). Sometimes it may lead to staircase effect if the reconstructed image has not obvious piecewise constant feature. Recently, researches in the field of image processing suggested that the high order total variation (HOTV) can effectively suppress staircase effect. Whereas the HOTV reconstruction algorithm has not been carried out deeply and extensively in image reconstruction. Herein, we propose a HOTV reconstruction model and design its adaptive steepest descent-projection onto convex sets (ASD-POCS) solving algorithm, and then characterize its reconstruction performance. We construct the second order TV norm using the second order gradient, and design the data fidelity constrained, second order TV minimization model, and derive the its ASD-POCS algorithm. In order to characterize its reconstruction performance, we use the Shepp-Logan simulation phantom of wavy background, the gray-changing simulation phantom and the chest X-ray image simulation phantom to perform reconstructions. Sparse reconstruction results show that compared to the traditional TV algorithm, the HOTV algorithm can effectively suppress the staircase effect and improve the reconstruction accuracy. Noisy-projections reconstruction results show that the traditional TV algorithm and the HOTV algorithm both have good denoising effect but the HOTV algorithm is slightly better for it can better protect the image edge information. The HOTV algorithm is a better reconstruction algorithm than the TV algorithm in image reconstruction if the piecewise constant feature of the reconstructed image is not so obvious but the grayscale fluctuation feature is prominent. The proposed HOTV algorithm can be extended to various CT configurations and other imaging modalities.

CT image reconstruction algorithms: A comprehensive survey

SummaryWith the application of computed tomography (CT) technology to the clinic diagnosis and image‐guided surgeries, various CT image reconstruction algorithms were presented for enhancing CT image quality based on different dose acquisitions. For wide and effective uses, this paper is going to provide a comprehensive survey on representative CT image reconstruction algorithms, in the light of low‐dose and high‐dose acquisitions and algorithm performance, including their merits and limitations. The algorithm performance and complexity are discussed under Gaussian and Poisson noise environments, respectively. Finally, experimental results analyze the efficiency and performance of several popular CT image reconstruction algorithms in terms of computation time and accuracy.

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.

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.

GPU-Based Iterative Medical CT Image Reconstructions

The algebraic reconstruction technique (ART) is an iterative algorithm for CT (i.e., computed tomography) image reconstruction that delivers better image quality with less radiation dosage than the industry-standard filtered back projection (FBP). However, the high computational cost of ART requires researchers to turn to high-performance computing to accelerate the algorithm. Alas, existing approaches for ART suffer from inefficient design of compressed data structures and computational kernels on GPUs. Thus, this paper presents our CUDA-based CT image reconstruction tool based on the algebraic reconstruction technique (ART) or cuART. It delivers a compression and parallelization solution for ART-based image reconstruction on GPUs. We address the under-performing, but popular, GPU libraries, e.g., cuSPARSE, BRC, and CSR5, on the ART algorithm and propose a symmetry-based CSR format (SCSR) to further compress the CSR data structure and optimize data access for both SpMV and SpMV_T via a column-indices permutation. We also propose sorting-based global-level and sorting-free view-level blocking techniques to optimize the kernel computation by leveraging different sparsity patterns of the system matrix. The end result is that cuART can reduce the memory footprint significantly and enable practical CT datasets to fit into a single GPU. The experimental results on a NVIDIA Tesla K80 GPU illustrate that our approach can achieve up to 6.8x, 7.2x, and 5.4x speedups over counterparts that use cuSPARSE, BRC, and CSR5, respectively.

First Dual MeV Energy X-ray CT for Container Inspection: Design, Algorithm, and Preliminary Experimental Results

Dual-energy mega-electron-volt (MeV) X-ray container radiography has become a well-established technique in customs security application, because of its material discrimination capability. The main difficulty of X-ray radiography is dealing with the materials overlapping problem. When two or more materials exist along the X-ray beam path, its material discrimination performance will be obviously affected. Computed tomography (CT) collects many X-ray measurements taken from different angles surrounding an object to produce cross-sectional (tomographic) images of the scanned object. Therefore, CT can provide real 3-D images inside the object. However, due to the bulky container volume and complex types of cargos, it is very hard to develop such a huge CT system for container inspection. To the best of our knowledge, there has no such commercial X-ray CT system for container inspection yet. This paper presents the design of a dual MeV energy X-ray CT system for cargo container inspection which uses an accelerator with fast 6/9 MeV switching spectra, an arc detector array and rotating mechanism. A dual MeV energy X-ray CT image reconstruction and material decomposition algorithm are developed. An experimental system was built with the same accelerator and detector array as used in the designed container CT system. Experimental results that prove the validity and effectiveness of the algorithm and CT system are presented.

Deriving Quantitative Crystallographic Information from the Wavelength-Resolved Neutron Transmission Analysis Performed in Imaging Mode

Current status of Bragg-edge/dip neutron transmission analysis/imaging methods is presented. The method can visualize real-space distributions of bulk crystallographic information in a crystalline material over a large area (~10 cm) with high spatial resolution (~100 μm). Furthermore, by using suitable spectrum analysis methods for wavelength-dependent neutron transmission data, quantitative visualization of the crystallographic information can be achieved. For example, crystallographic texture imaging, crystallite size imaging and crystalline phase imaging with texture/extinction corrections are carried out by the Rietveld-type (wide wavelength bandwidth) profile fitting analysis code, RITS (Rietveld Imaging of Transmission Spectra). By using the single Bragg-edge analysis mode of RITS, evaluations of crystal lattice plane spacing (d-spacing) relating to macro-strain and d-spacing distribution’s FWHM (full width at half maximum) relating to micro-strain can be achieved. Macro-strain tomography is performed by a new conceptual CT (computed tomography) image reconstruction algorithm, the tensor CT method. Crystalline grains and their orientations are visualized by a fast determination method of grain orientation for Bragg-dip neutron transmission spectrum. In this paper, these imaging examples with the spectrum analysis methods and the reliabilities evaluated by optical/electron microscope and X-ray/neutron diffraction, are presented. In addition, the status at compact accelerator driven pulsed neutron sources is also presented.

Clinical evaluation of a novel CT image reconstruction algorithm for direct dose calculations

Abstract Background and purpose Computed tomography (CT) imaging is frequently used in radiation oncology to calculate radiation dose distributions. In order to calculate doses, the CT numbers must be converted into densities by an energy dependent conversion curve. A recently developed algorithm directly reconstructs CT projection data into relative electron densities which eliminates the use of separate conversion curves for different X-ray tube potentials. Our work evaluates this algorithm for various cancer sites and shows its applicability in a clinical workflow. Materials and methods The Gammex phantom with tissue mimicking inserts was scanned to characterize the CT number to density conversion curves. In total, 33 patients with various cancer sites were scanned using multiple tube potentials. All CT acquisitions were reconstructed with the standard filtered back-projection (FBP) and the new developed DirectDensity™ (DD) algorithm. The mean tumor doses and the volume percentage that receives more than 95% of the prescribed dose were calculated for the planning target volume. Relevant parameters for the organs at risk for each tumor site were also calculated. Results The relative mean dose differences between the standard 120kVp FBP CT scan workflow and the DD CT scans (80, 100, 120 and 140kVp) were in general less than 1% for the planned target volume and organs at risk. Conclusion The energy independent DD algorithm allows for accurate dose calculations over a variety of body sites. This novel algorithm eliminates the tube potential specific calibration procedure and thereby simplifies the clinical radiotherapy workflow.

A hybrid stochastic-deterministic gradient descent algorithm for image reconstruction in cone-beam computed tomography

There is a growing interest in algorithms for computed tomography (CT) reconstruction from a small number of projection measurements. In this paper, we propose an algorithm that is based on variance-reduced stochastic gradient descent (SGD). Variance-reduced SGD methods are a new class of stochastic optimization algorithms that have proved highly successful in solving large-scale optimization problems. These algorithms store a copy of the full gradient direction or stochastic gradient directions and use them in building update directions. We propose an algorithm for CT image reconstruction that starts off with variance-reduced SGD updates and gradually increases the batch size. As the algorithm approaches the solution and the batch size grows, the algorithm turns into a limited-memory quasi-Newton algorithm to exploit the curvature information in the vicinity of the solution. We apply the proposed algorithm on simulated and real cone-beam CT projections and compare it with several other algorithms. Our results show that the proposed algorithm is very fast and is able to reconstruct very high-quality images in a small number of iterations.

Optimization of SPECT-CT Hybrid Imaging Using Iterative Image Reconstruction for Low-Dose CT: A Phantom Study.

BackgroundHybrid imaging combines nuclear medicine imaging such as single photon emission computed tomography (SPECT) or positron emission tomography (PET) with computed tomography (CT). Through this hybrid design, scanned patients accumulate radiation exposure from both applications. Imaging modalities have been the subject of long-term optimization efforts, focusing on diagnostic applications. It was the aim of this study to investigate the influence of an iterative CT image reconstruction algorithm (ASIR) on the image quality of the low-dose CT images.Methodology/Principal FindingsExaminations were performed with a SPECT-CT scanner with standardized CT and SPECT-phantom geometries and CT protocols with systematically reduced X-ray tube currents. Analyses included image quality with respect to photon flux. Results were compared to the standard FBP reconstructed images. The general impact of the CT-based attenuation maps used during SPECT reconstruction was examined for two SPECT phantoms. Using ASIR for image reconstructions, image noise was reduced compared to FBP reconstructions for the same X-ray tube current. The Hounsfield unit (HU) values reconstructed by ASIR were correlated to the FBP HU values(R2 ≥ 0.88) and the contrast-to-noise ratio (CNR) was improved by ASIR. However, for a phantom with increased attenuation, the HU values shifted for low X-ray tube currents I ≤ 60 mA (p ≤ 0.04). In addition, the shift of the HU values was observed within the attenuation corrected SPECT images for very low X-ray tube currents (I ≤ 20 mA, p ≤ 0.001).Conclusion/SignificanceIn general, the decrease in X-ray tube current up to 30 mA in combination with ASIR led to a reduction of CT-related radiation exposure without a significant decrease in image quality.

NUFFT-Based Iterative Image Reconstruction via Alternating Direction Total Variation Minimization for Sparse-View CT.

Sparse-view imaging is a promising scanning method which can reduce the radiation dose in X-ray computed tomography (CT). Reconstruction algorithm for sparse-view imaging system is of significant importance. The adoption of the spatial iterative algorithm for CT image reconstruction has a low operation efficiency and high computation requirement. A novel Fourier-based iterative reconstruction technique that utilizes nonuniform fast Fourier transform is presented in this study along with the advanced total variation (TV) regularization for sparse-view CT. Combined with the alternating direction method, the proposed approach shows excellent efficiency and rapid convergence property. Numerical simulations and real data experiments are performed on a parallel beam CT. Experimental results validate that the proposed method has higher computational efficiency and better reconstruction quality than the conventional algorithms, such as simultaneous algebraic reconstruction technique using TV method and the alternating direction total variation minimization approach, with the same time duration. The proposed method appears to have extensive applications in X-ray CT imaging.

Distributed CT image reconstruction algorithm based on the alternating direction method.

With the development of compressive sensing theory, image reconstruction from few-view projections has been paid considerable research attention in the field of computed tomography (CT). Total variation (TV)-based CT image reconstruction has been shown experimentally to be capable of producing accurate reconstructions from sparse-view data. Motivated by the need of solving few-view reconstruction problem with large scale data, a general block distribution reconstruction algorithm based on TV minimization and the alternating direction method (ADM) has been developed in this study. By utilizing the inexact ADM, which involves linearization and proximal point techniques, the algorithm is relatively simple and hence convenient for the derivation and distributed implementation. And because the data as well as the computation are distributed to individual nodes, an outstanding acceleration factor is achieved. Experimental results demonstrate that the proposed method can accelerate the alternating direction total variation minimization (ADTVM) algorithm with nearly no loss of accuracy, which means compared with ADTVM, the proposed algorithm has a better accuracy with same running time.