An oblique projection modification technique (OPMT) for fast multispectral CT reconstruction

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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“…Algorithm 2 shows the iteration scheme in the case of noise and the convergence proof of algorithm 2 can refer to [38].…”

Section: In the Case Of Noisementioning

confidence: 99%

“…Researchers usually model the process of obtaining MSCT data as nonlinear equations [26,38]. Omitting scattered photons and taking MSCT basis material decomposition as an example, the discrete nonlinear model of obtaining polychromatic projections is…”

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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“…For example, Zhao et al utilized the first-order Taylor expansion of nonlinear observations and proposed an extended algebraic reconstruction technique (EART) [ 17 ] for DECT. For the consideration of the convergence efficiency, couple variants of EART have been proposed, e.g., the simultaneous EART (ESART) [ 18 ], the oblique projection modification technique [ 19 ], and the monochromatic images guided iteration method [ 20 ] in recent years. In addition, as the photons emitted by the X-ray source contain continuous energy distribution, and it is empirically assumed that the attenuated photons received by the detector follow the Poisson distribution, different statistical-based iterative methods have emerged.…”

Section: Introductionmentioning

confidence: 99%

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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“…Iterative methods [25][26][27][28][29][30][31][32][33][34][35][36] based on statistical models or nonlinear optimization can effectively improve the quality of the reconstructed images by introducing prior knowledge and establishing approximate model to iteratively solve the DECT model and obtain the basis material projections. However, iterative reconstruction methods exhibit low convergence speed and high computational cost.In 2000,Sukovi et alproposed a penaltyweighted least squares reconstruction method 25 by adding a statistical model to the reconstruction method to process incomplete data sets and obtain images with high signal-to-noise ratio (SNR).…”

Section: Introductionmentioning

confidence: 99%

“…Iterative methods 25–36 based on statistical models or nonlinear optimization can effectively improve the quality of the reconstructed images by introducing prior knowledge and establishing approximate model to iteratively solve the DECT model and obtain the basis material projections. However, iterative reconstruction methods exhibit low convergence speed and high computational cost.…”

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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An oblique projection modification technique (OPMT) for fast multispectral CT reconstruction

Cited by 14 publications

References 55 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)

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

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

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