scholarly journals l1/2 regularization for wavelet frames based few-view CT reconstruction

2021 ◽  
Vol 269 ◽  
pp. 01020
Author(s):  
Lingli Zhang ◽  
An Luo
Keyword(s):  
Image Reconstruction ◽  
Radiation Exposure ◽  
Reconstruction Technique ◽  
Ct Reconstruction ◽  
Wavelet Frames ◽  
Proximal Mapping ◽  
Reconstruction Quality ◽  
Significant Research ◽  
Pc Algorithm ◽  
Simultaneous Algebraic Reconstruction Technique

Reducing the radiation exposure in computed tomography (CT) is always a significant research topic in radiology. Image reconstruction from few-view projection is a reasonable and effective way to decrease the number of rays to lower the radiation exposure. But how to maintain high image reconstruction quality while reducing radiation exposure is a major challenge. To solve this problem, several researchers are absorbed in l0 or l1 regularization based optimization models to deal with it. However, the solution of l1 regularization based optimization model is not sparser than that of l1/2 or l0 regularization, and solving the l0 regularization is more difficult than solving the l1/2 regularization. In this paper, we develop l1/2 regularization for wavelet frames based image reconstruction model to research the few-view problem. First, the existence of the solution of the corresponding model is demonstrated. Second, an alternate direction method (ADM) is utilized to separate the original problem into two subproblems, where the former subproblem about the image is solved using the idea of the proximal mapping, the simultaneous algebraic reconstruction technique (SART) and the projection and contraction (PC) algorithm, and the later subproblem about the wavelet coefficients is solved using the half thresholding (HT) algorithm. Furthermore, the convergence analysis of our method is given by the simulated implementions. Simulated and real experiments confirm the effectiveness of our method.

Guided image filtering based ℓ0 gradient minimization for limited-angle CT image reconstruction

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Tianyi Wang ◽  
Chengxiang Wang ◽  
Kequan Zhao ◽  
Wei Yu ◽  
Min Huang
Keyword(s):  
Iteration Process ◽  
Real Data ◽  
Image Filtering ◽  
Reconstruction Technique ◽  
Ct Reconstruction ◽  
Imaging Device ◽  
Practical Applications ◽  
Guided Image Filtering ◽  
Simultaneous Algebraic Reconstruction Technique ◽  
Limited Angle

Abstract Limited-angle computed tomography (CT) reconstruction problem arises in some practical applications due to restrictions in the scanning environment or CT imaging device. Some artifacts will be presented in image reconstructed by conventional analytical algorithms. Although some regularization strategies have been proposed to suppress the artifacts, such as total variation (TV) minimization, there is still distortion in some edge portions of image. Guided image filtering (GIF) has the advantage of smoothing the image as well as preserving the edge. To further improve the image quality and protect the edge of image, we propose a coupling method, that combines ℓ 0 {\ell_{0}} gradient minimization and GIF. An intermediate result obtained by ℓ 0 {\ell_{0}} gradient minimization is regarded as a guidance image of GIF, then GIF is used to filter the result reconstructed by simultaneous algebraic reconstruction technique (SART) with nonnegative constraint. It should be stressed that the guidance image is dynamically updated as the iteration process, which can transfer the edge to the filtered image. Some simulation and real data experiments are used to evaluate the proposed method. Experimental results show that our method owns some advantages in suppressing the artifacts of limited angle CT and in preserving the edge of image.

scholarly journals SART-Type Image Reconstruction from Overlapped Projections

2011 ◽  
Vol 2011 ◽  
pp. 1-7
Author(s):  
Hengyong Yu ◽  
Changguo Ji ◽  
Ge Wang
Keyword(s):  
Image Reconstruction ◽  
Reconstruction Algorithm ◽  
Reconstruction Technique ◽  
Algebraic Reconstruction Technique ◽  
Multiple Sources ◽  
X Ray ◽  
Type Reconstruction ◽  
Soft Threshold ◽  
Simultaneous Algebraic Reconstruction Technique ◽  
Data Acquisition Process

To maximize the time-integrated X-ray flux from multiple X-ray sources and shorten the data acquisition process, a promising way is to allow overlapped projections from multiple sources being simultaneously on without involving the source multiplexing technology. The most challenging task in this configuration is to perform image reconstruction effectively and efficiently from overlapped projections. Inspired by the single-source simultaneous algebraic reconstruction technique (SART), we hereby develop a multisource SART-type reconstruction algorithm regularized by a sparsity-oriented constraint in the soft-threshold filtering framework to reconstruct images from overlapped projections. Our numerical simulation results verify the correctness of the proposed algorithm and demonstrate the advantage of image reconstruction from overlapped projections.

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

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Bin Yan ◽  
Zhao Jin ◽  
Hanming Zhang ◽  
Lei Li ◽  
Ailong Cai
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Imaging System ◽  
Reconstruction Technique ◽  
Ct Reconstruction ◽  
Time Duration ◽  
Total Variation Minimization ◽  
Scanning Method ◽  
X Ray ◽  
Alternating Direction

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.

scholarly journals SART-Type Image Reconstruction from a Limited Number of Projections with the Sparsity Constraint

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Hengyong Yu ◽  
Ge Wang
Keyword(s):  
Image Reconstruction ◽  
Haar Wavelet ◽  
Reconstruction Technique ◽  
Algebraic Reconstruction Technique ◽  
Haar Wavelet Transform ◽  
Linear Inverse Problems ◽  
Sparsity Constraints ◽  
Sparsity Constraint ◽  
Simultaneous Algebraic Reconstruction Technique

Based on the recent mathematical findings on solving the linear inverse problems with sparsity constraints by Daubechiesx et al., here we adapt a simultaneous algebraic reconstruction technique (SART) for image reconstruction from a limited number of projections subject to a sparsity constraint in terms of an invertible compression transform. The algorithm is implemented with an exemplary Haar wavelet transform and tested with a modified Shepp-Logan phantom. Our preliminary results demonstrate that the sparsity constraint helps effectively improve the quality of reconstructed images and reduce the number of necessary projections.

scholarly journals Line-Source Based X-Ray Tomography

2009 ◽  
Vol 2009 ◽  
pp. 1-8 ◽  
Author(s):  
Deepak Bharkhada ◽  
Hengyong Yu ◽  
Hong Liu ◽  
Robert Plemmons ◽  
Ge Wang
Keyword(s):  
Image Reconstruction ◽  
Line Source ◽  
Spot Size ◽  
Projection Data ◽  
Reconstruction Technique ◽  
Focal Spot Size ◽  
X Ray ◽  
Ct Scanners ◽  
Size Limits ◽  
Simultaneous Algebraic Reconstruction Technique

Current computed tomography (CT) scanners, including micro-CT scanners, utilize a point x-ray source. As we target higher and higher spatial resolutions, the reduced x-ray focal spot size limits the temporal and contrast resolutions achievable. To overcome this limitation, in this paper we propose to use a line-shaped x-ray source so that many more photons can be generated, given a data acquisition interval. In reference to the simultaneous algebraic reconstruction technique (SART) algorithm for image reconstruction from projection data generated by an x-ray point source, here we develop a generalized SART algorithm for image reconstruction from projection data generated by an x-ray line source. Our numerical simulation results demonstrate the feasibility of our novel line-source based x-ray CT approach and the proposed generalized SART algorithm.

scholarly journals A CT Reconstruction Algorithm Based on L1/2Regularization

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Mianyi Chen ◽  
Deling Mi ◽  
Peng He ◽  
Luzhen Deng ◽  
Biao Wei
Keyword(s):  
Reconstruction Algorithm ◽  
Ct Images ◽  
Projection Data ◽  
Reconstruction Technique ◽  
Ct Reconstruction ◽  
Split Bregman ◽  
Reconstruction Methods ◽  
Significant Research ◽  
Regularization Operator

Computed tomography (CT) reconstruction with low radiation dose is a significant research point in current medical CT field. Compressed sensing has shown great potential reconstruct high-quality CT images from few-view or sparse-view data. In this paper, we use the sparser L1/2regularization operator to replace the traditional L1regularization and combine the Split Bregman method to reconstruct CT images, which has good unbiasedness and can accelerate iterative convergence. In the reconstruction experiments with simulation and real projection data, we analyze the quality of reconstructed images using different reconstruction methods in different projection angles and iteration numbers. Compared with algebraic reconstruction technique (ART) and total variance (TV) based approaches, the proposed reconstruction algorithm can not only get better images with higher quality from few-view data but also need less iteration numbers.

X-Ray Image Reconstruction of Solder Joints by Using Several Algebraic Reconstruction Techniques

2009 ◽  
Vol 419-420 ◽  
pp. 281-284
Author(s):  
Dein Shaw ◽  
Po Yen Hsu ◽  
Shih Liang Chen
Keyword(s):  
Image Reconstruction ◽  
Printed Circuit Board ◽  
Circuit Board ◽  
Reconstruction Technique ◽  
X Ray ◽  
Pcb Assembly ◽  
Reconstruction Quality ◽  
Algebraic Reconstruction Techniques ◽  
Algebraic Reconstruction

In this study, several Algebraic Reconstruction Technique (ART) algorithms are used to construct the 3 D sliced images from several transparent X-ray images. The speed and quality of image reconstruction are two important requirements when doing the inspection in a printed circuit board (PCB) assembly line. . To fulfill the requirements, several methodologies are discussed. The image qualities of regular ART method, binary ART method and Mixed ART method are developed and compared. The binary ART method uses the property of x-ray absorb rate of metal, silicon and other non- metal material are extremely different. The absorb rates of non-metal part are assumed to be zero and metal parts are assumed to be a single value. By using this method the quality of image reconstruction is improved. The mixed ART method is the combination of the tradition ART method and binary ART. Due to the X-ray sensor’s framing time, a method to improve the image reconstruction quality due to effect of X-ray sensor framing time is also discussed. Finally, the best solution algorithm is found to be mixed binary steering ART when consider the signal noise of X-ray detector.

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