A Direct Material Reconstruction Method for DECT Based on Total Variation and BM3D Frame

Zhang, Wenkun; Cai, Ailong; Zheng, Zhizhong; Wang, Linyuan; Liang, Ningning; Li, Lei; Yan, Bin; Hu, Guoen

doi:10.1109/access.2019.2942620

Cited by 2 publications

(3 citation statements)

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“…It contains the forward transmission and the back projection in Equation (8). This paper utilized the multi-material ESART algorithm [ 18 , 33 ] to solve the proximal step and adopt the ordered subsets (OS) technique [ 34 ] to accelerate the algorithm; the detailed formulas are given in the Appendix A . Equation (10) is solved by a soft-shrinkage operator

where

.…”

Section: Methodsmentioning

confidence: 99%

“…Chen et al [ 31 , 32 ] applied the convex indicator function of the gradient image to enforce an upper bound on the material images and the monochromatic images. Zhang et al [ 33 ] proposed a direct material reconstruction method that combined a total variation (TV) term with block matching and a 3D filtering term for DECT. However, for the above one-step iterative method, the convergence of algorithms based on the primal-dual framework is not satisfying, and some methods are even susceptible to noise due to a lack of the ability of noise suppression or the need to adjust the parameters of the regularization terms manually when the number of materials increases.…”

Section: Introductionmentioning

confidence: 99%

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Direct Multi-Material Reconstruction via Iterative Proximal Adaptive Descent for Spectral CT Imaging

Cai

Liang

et al. 2023

Bioengineering

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Spectral computed tomography (spectral CT) is a promising medical imaging technology because of its ability to provide information on material characterization and quantification. However, with an increasing number of basis materials, the nonlinearity of measurements causes difficulty in decomposition. In addition, noise amplification and beam hardening further reduce image quality. Thus, improving the accuracy of material decomposition while suppressing noise is pivotal for spectral CT imaging. This paper proposes a one-step multi-material reconstruction model as well as an iterative proximal adaptive decent method. In this approach, a proximal step and a descent step with adaptive step size are designed under the forward–backward splitting framework. The convergence analysis of the algorithm is further discussed according to the convexity of the optimization objective function. For simulation experiments with different noise levels, the peak signal-to-noise ratio (PSNR) obtained by the proposed method increases approximately 23 dB, 14 dB, and 4 dB compared to those of other algorithms. Magnified areas of thorax data further demonstrated that the proposed method has a better ability to preserve details in tissues, bones, and lungs. Numerical experiments verify that the proposed method efficiently reconstructed the material maps, and reduced noise and beam hardening artifacts compared with the state-of-the-art methods.

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where

.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Direct Multi-Material Reconstruction via Iterative Proximal Adaptive Descent for Spectral CT Imaging

Cai

Liang

et al. 2023

Bioengineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Chen et al [31,32] applied the convex indicator function of the gradient image to enforce an upper bound on the material images and monochromatic images. Zhang et al [33] proposed a direct material reconstruction method that combined total variation (TV) and blockmatching and 3D filtering for DECT. However, for the above one-step iterative method, some methods are even susceptible to noise due to lack the ability of noise suppression or one need to adjust the parameters of the regularization terms manually when the number of materials increases.…”

Section: Introductionmentioning

confidence: 99%

Volume Conservation Constrained Multi-Material Reconstruction for Inconsistent Spectral CT Imaging

Cai

Liang

et al. 2024

IEEE Access

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Spectral computed tomography (spectral CT) is a promising medical imaging technology because of its ability to provide information on material characterization and quantification. However, the difficulty of decomposition also increases due to the nonlinearity nature of the measurements and ill condition of the problem, especially in the case of geometric inconsistency, which typically leads to low image qualities. Therefore, it is a crucial issue for inconsistent spectral CT imaging to improve the accuracy of material decomposition while suppressing the noise. This paper depends on a statistical reconstruction model with different priors to propose onestep multi-material algorithms. In these approaches, the gradient sparisty-based and convolutional neural network based methods are designed for the case of the consistent numbers of material and energies. And volume conservation constraint are further developed while the two numbers are not equal. An efficient Newton descent method is adopted based on the simple surrogate function. For simulation experiments with different noise levels, the largest peak signal-to-noise ratio (PSNR) obtained by the proposed method approximately increases by 20.924 dB and 18.283 dB compared with those of other algorithms. Magnified areas of real data also further demonstrated that the proposed methods has a better ability to suppress noise. Numerical experiments verify that the proposed method efficiently reconstructed the material maps, and reduced noise compared with the state-of-the-art methods.

show abstract

A Direct Material Reconstruction Method for DECT Based on Total Variation and BM3D Frame

Cited by 2 publications

References 48 publications

Direct Multi-Material Reconstruction via Iterative Proximal Adaptive Descent for Spectral CT Imaging

Direct Multi-Material Reconstruction via Iterative Proximal Adaptive Descent for Spectral CT Imaging

Volume Conservation Constrained Multi-Material Reconstruction for Inconsistent Spectral CT Imaging

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