Projection decomposition algorithm for dual-energy computed tomography via deep neural network

Xu, Yifu; Yan, Bin; Chen, Jian; Zeng, Lei; Li, Lei

doi:10.3233/xst-17349

Cited by 15 publications

(8 citation statements)

References 14 publications

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“…is a hot issue [50]. Deep learning methods [51][52][53][54] have been used in MSCT reconstruction and basis material decomposition. However, in many cases, CT training data are difficult to obtain, especially industrial CT data.…”

Section: Introductionmentioning

confidence: 99%

Fast iterative reconstruction for multi-spectral CT by a Schmidt orthogonal modification algorithm (SOMA)

et al. 2023

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Multi-spectral CT (MSCT) is increasingly used in industrial non-destructive testing and medical diagnosis because of its outstanding performance like material distinguishability. The process of obtaining MSCT data can be modeled as nonlinear equations and the basis material decomposition comes down to the inverse problem of the nonlinear equations. For different spectra data, geometric inconsistent parameters cause geometrical inconsistent rays, which will lead to mismatched nonlinear equations. How to solve the mismatched nonlinear equations accurately and quickly is a hot issue. This paper proposes a general iterative method to invert the mismatched nonlinear equations and develops Schmidt orthogonalization to accelerate convergence. The validity of the proposed method is verified by MSCT basis material decomposition experiments. The results show that the proposed method can decompose the basis material images accurately and improve the convergence speed greatly.

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Section: Introductionmentioning

confidence: 99%

Fast iterative reconstruction for multi-spectral CT by a Schmidt orthogonal modification algorithm (SOMA)

et al. 2023

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“…While direct mapping methods are quick and easy to use considering they do not consider the spectrum information and attenuation coefficient of materials, they are highly dependent on the calibration phantom, which limits the accuracy of the reconstructed images. Deep learningbased methods [22][23][24] have been used in recent times to decompose basis material images. However, despite the promising results, numerous training data are required to train the model, and the training time is relatively long.…”

Section: Introductionmentioning

confidence: 99%

An iterative reconstruction method based on monochromatic images for dual energy CT

et al. 2021

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Purpose Dual‐energy computed tomography (DECT) scans objects using two different X‐ray spectra to acquire more information, which is also called dual spectral CT (DSCT) in some articles. Compared to traditional CT, DECT exhibits superior material distinguishability. Therefore, DECT can be widely used in the medical and industrial domains. However, owing to the nonlinearity and ill condition of DECT, studies are underway on DECT reconstruction to obtain high quality images and achieve fast convergence speed. Therefore, in this study, we propose an iterative reconstruction method based on monochromatic images (IRM‐MI) to rapidly obtain high‐quality images in DECT reconstruction. Methods An IRM‐MI is proposed for DECT. The proposed method converts DECT reconstruction problem from the basis material images decomposition to monochromatic images decomposition to significantly improve the convergence speed of DECT reconstruction by changing the coefficient matrix of the original equations to increase the angle of the high‐ and low‐energy projection curves or reduce the condition number of the coefficient matrix. The monochromatic images were then decomposed into basis material images. Furthermore, we conducted numerical experiments to evaluate the performance of the proposed method. Results The decomposition results of the simulated data and real data experiments confirmed the effectiveness of the proposed method. Compared to the extended algebraic reconstruction technique (E‐ART) method, the proposed method exhibited a significant increase in the convergence speed by increasing the angle of polychromatic projection curves or decreasing the condition number of the coefficient matrix, when choosing the appropriate monochromatic images. Therefore, the proposed method is also advantageous in acquiring high quality and rapidly converged images. Conclusions We developed an iterative reconstruction method based on monochromatic images for the material decomposition for DECT. The numerical experiments using the proposed method validated its capability of decomposing the basis material images. Furthermore, the proposed method achieved faster convergence speed compared to the E‐ART method.

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“…26 On the other hand, Shi et al used a modified U-net to estimate the material-specific projections 27 in the projection-domain. Xu et al proposed a projection decomposition network to learn a compact spectrum representation 28 . These preliminary studies already demonstrate the advancement of the DL-based DECT material decomposition methods which are solely upon the image domain or the projection domain.…”

Section: Introductionmentioning

confidence: 99%

DIRECT‐Net: A unified mutual‐domain material decomposition network for quantitative dual‐energy CT imaging

Sun

Yang

et al. 2021

Medical Physics

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The purpose of this paper is to present an end-to-end deep convolutional neural network to improve the dual-energy CT (DECT) material decomposition performance. Methods: In this study, we proposes a unified mutual-domain (sinogram domain and CT domain) material decomposition network (DIRECT-Net) for DECT imaging. By design, the DIRECT-Net has immediate access to mutual-domain data, and utilizes stacked convolution neural network layers for noise reduction and material decomposition. The training data are numerically generated following the fundamental DECT imaging physics. Numerical simulation of the XCAT digital phantom, experiments of a biological specimen, a calcium chloride phantom and an iodine solution phantom are carried out to evaluate the performance of DIRECT-Net. Comparisons are performed with different DECT decomposition algorithms. Results: Results demonstrate that the proposed DIRECT-Net can generate water and bone basis images with less artifacts compared to the other decomposition methods. Additionally, the quantification errors of the calcium chloride (75-375 mg/cm 3 ) and the iodine (2-20 mg/cm 3 ) are less than 4%. Conclusions: An end-to-end material decomposition network is proposed for quantitative DECT imaging. The qualitative and quantitative results demonstrate that this new DIRECT-Net has promising benefits in improving the DECT image quality.

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Projection decomposition algorithm for dual-energy computed tomography via deep neural network

Abstract: The DNN model is applicable to the decomposition tasks with different dual energies. Experimental results demonstrated the strong function fitting ability of DNN. Thus, the Deep learning paradigm provides a promising approach to solve the nonlinear problem in DECT.

Cited by 15 publications

References 14 publications

Fast iterative reconstruction for multi-spectral CT by a Schmidt orthogonal modification algorithm (SOMA)

Fast iterative reconstruction for multi-spectral CT by a Schmidt orthogonal modification algorithm (SOMA)

An iterative reconstruction method based on monochromatic images for dual energy CT

DIRECT‐Net: A unified mutual‐domain material decomposition network for quantitative dual‐energy CT imaging

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