scholarly journals Sparse-View Neutron CT Reconstruction Using a Modified Weighted Total Difference Minimization Method

2021 ◽  
Vol 11 (22) ◽  
pp. 10942
Author(s):  
Yapeng Wu ◽  
Min Yang ◽  
Linfeng He ◽  
Qiang Lin ◽  
Meimei Wu ◽  
...  
Keyword(s):  
Spent Nuclear Fuel ◽  
Simulated Data ◽  
Complete Information ◽  
Neutron Imaging ◽  
Radiation Environment ◽  
Projection Data ◽  
Reconstruction Method ◽  
Ct Reconstruction ◽  
Minimization Method ◽  
Reconstruction Image

Indirect neutron imaging is an effective method for nondestructive testing of spent nuclear fuel elements. Considering the difficulty of obtaining experimental data in a high-radiation environment and the characteristic of high noise of neutron images, it is difficult to use the traditional FBP algorithm to recover the complete information of the sample based on the limited projection data. Therefore, it is necessary to develop the sparse-view CT reconstruction algorithm for indirect neutron imaging. In order to improve the quality of the reconstruction image, an iterative reconstruction method combining SIRT, MRP, and WTDM regularization is proposed. The reconstruction results obtained by using the proposed method on simulated data and actual neutron projection data are compared with the results of four other algorithms (FBP, SIRT, SIRT-TV, and SIRT-WTDM). The experimental results show that the SIRT-MWTDM algorithm has great advantages in both objective evaluation index and subjective observation in the reconstruction image of simulated data and neutron projection data.

scholarly journals An Improved Total Variation Minimization Method Using Prior Images and Split-Bregman Method in CT Reconstruction

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Luzhen Deng ◽  
Peng Feng ◽  
Mianyi Chen ◽  
Peng He ◽  
Biao Wei
Keyword(s):  
Total Variation ◽  
Locally Linear Embedding ◽  
Projection Data ◽  
Reconstruction Method ◽  
Ct Reconstruction ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Minimization Method ◽  
Total Variation Minimization ◽  
Image Objects

Compressive Sensing (CS) theory has great potential for reconstructing Computed Tomography (CT) images from sparse-views projection data and Total Variation- (TV-) based CT reconstruction method is very popular. However, it does not directly incorporate prior images into the reconstruction. To improve the quality of reconstructed images, this paper proposed an improved TV minimization method using prior images and Split-Bregman method in CT reconstruction, which uses prior images to obtain valuable previous information and promote the subsequent imaging process. The images obtained asynchronously were registered via Locally Linear Embedding (LLE). To validate the method, two studies were performed. Numerical simulation using an abdomen phantom has been used to demonstrate that the proposed method enables accurate reconstruction of image objects under sparse projection data. A real dataset was used to further validate the method.

Computed tomography reconstruction on distributed storage using hybrid regularization approach

2019 ◽  
Vol 33 (06) ◽  
pp. 1950063 ◽  
Author(s):  
Shailendra Tiwari ◽  
Kavkirat Kaur ◽  
Yadunath Pathak ◽  
Shivendraa Shivani ◽  
Kuldeep Kaur
Keyword(s):  
Computed Tomography ◽  
Gaussian Noise ◽  
Distributed Storage ◽  
Hybrid Approach ◽  
Computational Cost ◽  
Reconstruction Algorithm ◽  
Noise Robustness ◽  
Projection Data ◽  
Reconstruction Method ◽  
Ct Reconstruction

Computed Tomography (CT) is considered as a significant imaging tool for clinical diagnoses. Due to low-dose radiation in CT, the projection data is highly affected by Gaussian noise which may lead to blurred images, staircase effect, loss of basic fine structure and detailed information. Therefore, there is a demand for an approach that can eliminate noise and can provide high-quality images. To achieve this objective, this paper presents a new statistical image reconstruction method by proposing a suitable regularization approach. The proposed regularization is a hybrid approach of Complex Diffusion and Shock filter as a prior term. To handle the problem of prominent Gaussian noise as well as ill-posedness, the proposed hybrid regularization is further combined with the standard Maximum Likelihood Expectation Maximization (MLEM) reconstruction algorithm in an iterative manner and has been referred to as the proposed CT-Reconstruction (CT-R) algorithm here after. Besides, considering the large sizes of image data sets for medical imaging, distributed storage for images have been employed on Hadoop Distributed File System (HDFS) and the proposed MLEM algorithms have been deployed for improved performance.The proposed method has been evaluated on both the simulated and real test phantoms. The final results are compared with the other standard methods and it is observed that the proposed method has many desirable properties such as better noise robustness, less computational cost and enhanced denoising effect.

scholarly journals A CT Reconstruction Algorithm Based on Non-Aliasing Contourlet Transform and Compressive Sensing

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Lu-zhen Deng ◽  
Peng Feng ◽  
Mian-yi Chen ◽  
Peng He ◽  
Quang-sang Vo ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Iteration Process ◽  
Reconstruction Algorithm ◽  
Ct Images ◽  
Contourlet Transform ◽  
Projection Data ◽  
Reconstruction Technique ◽  
Reconstruction Method ◽  
Ct Reconstruction ◽  
Split Bregman

Compressive sensing (CS) theory has great potential for reconstructing CT images from sparse-views projection data. Currently, total variation (TV-) based CT reconstruction method is a hot research point in medical CT field, which uses the gradient operator as the sparse representation approach during the iteration process. However, the images reconstructed by this method often suffer the smoothing problem; to improve the quality of reconstructed images, this paper proposed a hybrid reconstruction method combining TV and non-aliasing Contourlet transform (NACT) and using the Split-Bregman method to solve the optimization problem. Finally, the simulation results show that the proposed algorithm can reconstruct high-quality CT images from few-views projection using less iteration numbers, which is more effective in suppressing noise and artefacts than algebraic reconstruction technique (ART) and TV-based reconstruction method.

Low-dose CT reconstruction method based on prior information of normal-dose image

2020 ◽  
Vol 28 (6) ◽  
pp. 1091-1111
Author(s):  
Zixiang Chen ◽  
Qiyang Zhang ◽  
Chao Zhou ◽  
Mengxi Zhang ◽  
Yongfeng Yang ◽  
...  
Keyword(s):  
Prior Information ◽  
Low Dose ◽  
Background Radiation ◽  
Ct Scanning ◽  
Superior Performance ◽  
Projection Data ◽  
Reconstruction Method ◽  
Ct Reconstruction ◽  
Normal Dose ◽  
Low Dose Ct

BACKGROUND: Radiation risk from computed tomography (CT) is always an issue for patients, especially those in clinical conditions in which repeated CT scanning is required. For patients undergoing repeated CT scanning, a low-dose protocol, such as sparse scanning, is often used, and consequently, an advanced reconstruction algorithm is also needed. OBJECTIVE: To develop a novel algorithm used for sparse-view CT reconstruction associated with the prior image. METHODS: A low-dose CT reconstruction method based on prior information of normal-dose image (PI-NDI) involving a transformed model for attenuation coefficients of the object to be reconstructed and prior information application in the forward-projection process was used to reconstruct CT images from sparse-view projection data. A digital extended cardiac-torso (XCAT) ventral phantom and a diagnostic head phantom were employed to evaluate the performance of the proposed PI-NDI method. The root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR) and mean percent absolute error (MPAE) of the reconstructed images were measured for quantitative evaluation of the proposed PI-NDI method. RESULTS: The reconstructed images with sparse-view projection data via the proposed PI-NDI method have higher quality by visual inspection than that via the compared methods. In terms of quantitative evaluations, the RMSE measured on the images reconstructed by the PI-NDI method with sparse projection data is comparable to that by MLEM-TV, PWLS-TV and PWLS-PICCS with fully sampled projection data. When the projection data are very sparse, images reconstructed by the PI-NDI method have higher PSNR values and lower MPAE values than those from the compared algorithms. CONCLUSIONS: This study presents a new low-dose CT reconstruction method based on prior information of normal-dose image (PI-NDI) for sparse-view CT image reconstruction. The experimental results validate that the new method has superior performance over other state-of-art methods.

Convergence and stability analysis of the half thresholding based few-view CT reconstruction

2020 ◽  
Vol 28 (6) ◽  
pp. 829-847
Author(s):  
Hua Huang ◽  
Chengwu Lu ◽  
Lingli Zhang ◽  
Weiwei Wang
Keyword(s):  
Noise Suppression ◽  
Reconstructed Image ◽  
Reference Image ◽  
Projection Data ◽  
Ct Reconstruction ◽  
Reconstruction Algorithms ◽  
Ill Posed ◽  
Thresholding Algorithm ◽  
The Stability ◽  
Well Posed

AbstractThe projection data obtained using the computed tomography (CT) technique are often incomplete and inconsistent owing to the radiation exposure and practical environment of the CT process, which may lead to a few-view reconstruction problem. Reconstructing an object from few projection views is often an ill-posed inverse problem. To solve such problems, regularization is an effective technique, in which the ill-posed problem is approximated considering a family of neighboring well-posed problems. In this study, we considered the {\ell_{1/2}} regularization to solve such ill-posed problems. Subsequently, the half thresholding algorithm was employed to solve the {\ell_{1/2}} regularization-based problem. The convergence analysis of the proposed method was performed, and the error bound between the reference image and reconstructed image was clarified. Finally, the stability of the proposed method was analyzed. The result of numerical experiments demonstrated that the proposed method can outperform the classical reconstruction algorithms in terms of noise suppression and preserving the details of the reconstructed image.

An iterative reconstruction method for sparse-projection data for low-dose CT

2021 ◽  
pp. 1-16
Author(s):  
Ying Huang ◽  
Qian Wan ◽  
Zixiang Chen ◽  
Zhanli Hu ◽  
Guanxun Cheng ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Reconstructed Image ◽  
Projection Data ◽  
Reconstruction Method ◽  
X Ray ◽  
Image Structure ◽  
Reconstruction Performance ◽  
Risk Of Cancer ◽  
Suppress Noise

Reducing X-ray radiation is beneficial for reducing the risk of cancer in patients. There are two main approaches for achieving this goal namely, one is to reduce the X-ray current, and another is to apply sparse-view protocols to do image scanning and projections. However, these techniques usually lead to degradation of the reconstructed image quality, resulting in excessive noise and severe edge artifacts, which seriously affect the diagnosis result. In order to overcome such limitation, this study proposes and tests an algorithm based on guided kernel filtering. The algorithm combines the characteristics of anisotropic edges between adjacent image voxels, expresses the relevant weights with an exponential function, and adjusts the weights adaptively through local gray gradients to better preserve the image structure while suppressing noise information. Experiments show that the proposed method can effectively suppress noise and preserve the image structure. Comparing with similar algorithms, the proposed algorithm greatly improves the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) of the reconstructed image. The proposed algorithm has the best effect in quantitative analysis, which verifies the effectiveness of the proposed method and good image reconstruction performance. Overall, this study demonstrates that the proposed method can reduce the number of projections required for repeated CT scans and has potential for medical applications in reducing radiation doses.

A new weighting scheme for arc based circle cone-beam CT reconstruction

2021 ◽  
pp. 1-19
Author(s):  
Wei Wang ◽  
Xiang-Gen Xia ◽  
Chuanjiang He ◽  
Zemin Ren ◽  
Jian Lu
Keyword(s):  
Characteristic Function ◽  
Weighting Function ◽  
Cone Beam Ct ◽  
Reconstruction Algorithm ◽  
Cone Beam ◽  
Characteristic Functions ◽  
Projection Data ◽  
Ct Reconstruction ◽  
Scanning Angle ◽  
Beam Geometry

In this paper, we present an arc based fan-beam computed tomography (CT) reconstruction algorithm by applying Katsevich’s helical CT image reconstruction formula to 2D fan-beam CT scanning data. Specifically, we propose a new weighting function to deal with the redundant data. Our weighting function ϖ ( x _ , λ ) is an average of two characteristic functions, where each characteristic function indicates whether the projection data of the scanning angle contributes to the intensity of the pixel x _ . In fact, for every pixel x _ , our method uses the projection data of two scanning angle intervals to reconstruct its intensity, where one interval contains the starting angle and another contains the end angle. Each interval corresponds to a characteristic function. By extending the fan-beam algorithm to the circle cone-beam geometry, we also obtain a new circle cone-beam CT reconstruction algorithm. To verify the effectiveness of our method, the simulated experiments are performed for 2D fan-beam geometry with straight line detectors and 3D circle cone-beam geometry with flat-plan detectors, where the simulated sinograms are generated by the open-source software “ASTRA toolbox.” We compare our method with the other existing algorithms. Our experimental results show that our new method yields the lowest root-mean-square-error (RMSE) and the highest structural-similarity (SSIM) for both reconstructed 2D and 3D fan-beam CT images.

Projection-to-image transform frame: a lightweight block reconstruction network (LBRN) for computed tomography

2021 ◽  
Author(s):  
Genwei Ma ◽  
Xing Zhao ◽  
Yining Zhu ◽  
Huitao Zhang
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Real Data ◽  
Projection Data ◽  
Ct Reconstruction ◽  
Back Projection ◽  
Image Block ◽  
Metal Artifacts ◽  
Reconstruction Process ◽  
Memory Space

Abstract To solve the problem of learning based computed tomography (CT) reconstruction, several reconstruction networks were invented. However, applying neural network to tomographic reconstruction still remains challenging due to unacceptable memory space requirement. In this study, we presents a novel lightweight block reconstruction network (LBRN), which transforms the reconstruction operator into a deep neural network by unrolling the filter back-projection (FBP) method. Specifically, the proposed network contains two main modules, which, respectively, correspond to the filter and back-projection of FBP method. The first module of LBRN decouples the relationship of Radon transform between the reconstructed image and the projection data. Therefore, the following module, block back-projection module, can use the block reconstruction strategy. Due to each image block is only connected with part filtered projection data, the network structure is greatly simplified and the parameters of the whole network is dramatically reduced. Moreover, this approach is trained end-to-end, working directly from raw projection data and does not depend on any initial images. Five reconstruction experiments are conducted to evaluate the performance of the proposed LBRN: full angle, low-dose CT, region of interest (ROI), metal artifacts reduction and real data experiment. The results of the experiments show that the LBRN can be effectively introduced into the reconstruction process and has outstanding advantages in terms of different reconstruction problems.

A content-adaptive unstructured grid based regularized CT reconstruction method with a SART-type preconditioned fixed-point proximity algorithm

2022 ◽  
Author(s):  
Yun Chen ◽  
Yao Lu ◽  
Xiangyuan Ma ◽  
Yuesheng Xu
Keyword(s):  
Integral Equation ◽  
Fixed Point ◽  
Optimization Problem ◽  
Unstructured Grid ◽  
Reconstruction Technique ◽  
Reconstruction Method ◽  
Ct Reconstruction ◽  
Image Domain ◽  
Computational Costs ◽  
Content Adaptive

Abstract The goal of this study is to develop a new computed tomography (CT) image reconstruction method, aiming at improving the quality of the reconstructed images of existing methods while reducing computational costs. Existing CT reconstruction is modeled by pixel-based piecewise constant approximations of the integral equation that describes the CT projection data acquisition process. Using these approximations imposes a bottleneck model error and results in a discrete system of a large size. We propose to develop a content-adaptive unstructured grid (CAUG) based regularized CT reconstruction method to address these issues. Specifically, we design a CAUG of the image domain to sparsely represent the underlying image, and introduce a CAUG-based piecewise linear approximation of the integral equation by employing a collocation method. We further apply a regularization defined on the CAUG for the resulting illposed linear system, which may lead to a sparse linear representation for the underlying solution. The regularized CT reconstruction is formulated as a convex optimization problem, whose objective function consists of a weighted least square norm based fidelity term, a regularization term and a constraint term. Here, the corresponding weighted matrix is derived from the simultaneous algebraic reconstruction technique (SART). We then develop a SART-type preconditioned fixed-point proximity algorithm to solve the optimization problem. Convergence analysis is provided for the resulting iterative algorithm. Numerical experiments demonstrate the outperformance of the proposed method over several existing methods in terms of both suppressing noise and reducing computational costs. These methods include the SART without regularization and with quadratic regularization on the CAUG, the traditional total variation (TV) regularized reconstruction method and the TV superiorized conjugate gradient method on the pixel grid.

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