A CT Reconstruction Algorithm Based on L1/2 Regularization.
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The sparser L1/2 regularization operator is used to replace the traditional L1 regularization and the Split Bregman method is combined to reconstruct CT images, which has good unbiasedness and can accelerate iterative convergence.Abstract:
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 L1/2 regularization operator to replace the traditional L1 regularization 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.read more
Citations
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Accelerated Compressed Sensing Based CT Image Reconstruction.
TL;DR: A new algorithm is proposed that accelerates the CS-based reconstruction by using a fast pseudopolar Fourier based Radon transform and rebinning the diverging fan beams to parallel beams and removes the rebinning and interpolation errors.
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CT Image Reconstruction from Sparse Projections Using Adaptive TpV Regularization
TL;DR: This work introduces a novel adaptive TpV regularization into sparse-projection image reconstruction and uses FISTA technique to accelerate iterative convergence and demonstrates that the proposed method suppresses noise and artifacts more efficiently, and preserves structure information better than other existing reconstruction methods.
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Sparse-view computed tomography image reconstruction via a combination of L 1 and SL 0 regularization
TL;DR: Experimental comparative results have indicated that the proposed L1/SL0-POCS algorithm can effectively suppress noise and artifacts, as well as preserve more structural information compared to other existing methods.
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Computed Tomography Image Reconstruction from Few-Views Data by Multi-Directional Total Variation
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An Improved Total Variation Minimization Method Using Prior Images and Split-Bregman Method in CT Reconstruction
TL;DR: 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 is proposed.
References
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Book
Compressed sensing
TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
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Maximum Likelihood Reconstruction for Emission Tomography
L. A. Shepp,Y. Vardi +1 more
TL;DR: In this paper, the authors proposed a more accurate general mathematical model for ET where an unknown emission density generates, and is to be reconstructed from, the number of counts n*(d) in each of D detector units d. Within the model, they gave an algorithm for determining an estimate? of? which maximizes the probability p(n*|?) of observing the actual detector count data n* over all possible densities?.
Journal ArticleDOI
The Split Bregman Method for L1-Regularized Problems
Tom Goldstein,Stanley Osher +1 more
TL;DR: This paper proposes a “split Bregman” method, which can solve a very broad class of L1-regularized problems, and applies this technique to the Rudin-Osher-Fatemi functional for image denoising and to a compressed sensing problem that arises in magnetic resonance imaging.
Journal ArticleDOI
The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming
TL;DR: This method can be regarded as a generalization of the methods discussed in [1–4] and applied to the approximate solution of problems in linear and convex programming.
Journal ArticleDOI
Algebraic Reconstruction Techniques (ART) for three-dimensional electron microscopy and X-ray photography
TL;DR: The method works for totally asymmetric objects, and requires little computer time or storage, and is also applicable to X-ray photography, and may greatly reduce the exposure compared to current methods of body-section radiography.
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