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Ren J, Zheng Z, Wang Y, Liang N, Wang S, Cai A, Li L, Yan B. Prior image-based generative adversarial learning for multi-material decomposition in photon counting computed tomography. Comput Biol Med 2024; 180:108854. [PMID: 39068902 DOI: 10.1016/j.compbiomed.2024.108854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 07/04/2024] [Accepted: 07/04/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND Photon counting detector computed tomography (PCD-CT) is a novel promising technique providing higher spatial resolution, lower radiation dose and greater energy spectrum differentiation, which create more possibilities to improve image quality. Multi-material decomposition is an attractive application for PCD-CT to identify complicated materials and provide accurate quantitative analysis. However, limited by the finite photon counting rate in each energy window of photon counting detector, the noise problem hinders the decomposition of high-quality basis material images. METHODS To address this issue, an end-to-end multi-material decomposition network based on prior images is proposed in this paper. First, the reconstructed images corresponding to the full spectrum with less noise are introduced as prior information to improve the overall signal-to-noise ratio of the data. Then, a generative adversarial network is designed to mine the relationship between reconstructed images and basis material images based on the information interaction of material decomposition. Furthermore, a weighted edge loss is introduced to adapt to the structural differences of different basis material images. RESULTS To verify the performance of the proposed method, simulation and real studies are carried out. In simulation study of structured fibro-glandular tissue model, the results show that the proposed method decreased the root mean square error by 67 % and 26 % on adipose, 66 % and 28 % on fibroglandular, 52 % and 8 % on calcification, compared to butterfly network and dual interactive Wasserstein generative adversarial network. CONCLUSION Experimentally, the proposed method shows certain advantages over other methods on noise suppression effect, detail retention ability and decomposition accuracy.
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Affiliation(s)
- Junru Ren
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Zhizhong Zheng
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Yizhong Wang
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Ningning Liang
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Shaoyu Wang
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Ailong Cai
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China.
| | - Lei Li
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China.
| | - Bin Yan
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
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Zhou H, Zhang H, Zhao X, Zhang P, Zhu Y. A model-based direct inversion network (MDIN) for dual spectral computed tomography. Phys Med Biol 2024; 69:055005. [PMID: 38271738 DOI: 10.1088/1361-6560/ad229f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 01/25/2024] [Indexed: 01/27/2024]
Abstract
Objective. Dual spectral computed tomography (DSCT) is a very challenging problem in the field of imaging. Due to the nonlinearity of its mathematical model, the images reconstructed by the conventional CT usually suffer from the beam hardening artifacts. Additionally, several existing DSCT methods rely heavily on the information of the spectra, which is often not readily available in applications. To address this problem, in this study, we aim to develop a novel approach to improve the DSCT reconstruction performance.Approach. A model-based direct inversion network (MDIN) is proposed for DSCT, which can directly predict the basis material images from the collected polychromatic projections. The all operations are performed in the network, requiring neither the conventional algorithms nor the information of the spectra. It can be viewed as an approximation to the inverse procedure of DSCT imaging model. The MDIN is composed of projection pre-decomposition module (PD-module), domain transformation layer (DT-layer), and image post-decomposition module (ID-module). The PD-module first performs the pre-decomposition on the polychromatic projections that consists of a series of stacked one-dimensional convolution layers. The DT-layer is designed to obtain the preliminary decomposed results, which has the characteristics of sparsely connected and learnable parameters. And the ID-module uses a deep neural network to further decompose the reconstructed results of the DT-layer so as to achieve higher-quality basis material images.Main results. Numerical experiments demonstrate that the proposed MDIN has significant advantages in substance decomposition, artifact reduction and noise suppression compared to other methods in the DSCT reconstruction.Significance. The proposed method has a flexible applicability, which can be extended to other CT problems, such as multi-spectral CT and low dose CT.
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Affiliation(s)
- Haichuan Zhou
- School of Mathematical Sciences, Capital Normal University, Beijing, 100048, People's Republic of China
- School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, 471000, People's Republic of China
| | - Huitao Zhang
- School of Mathematical Sciences, Capital Normal University, Beijing, 100048, People's Republic of China
- Shenzhen National Applied Mathematics Center, Southern University of Science and Technology, Shenzhen, 518055, People's Republic of China
| | - Xing Zhao
- School of Mathematical Sciences, Capital Normal University, Beijing, 100048, People's Republic of China
- Shenzhen National Applied Mathematics Center, Southern University of Science and Technology, Shenzhen, 518055, People's Republic of China
| | - Peng Zhang
- School of Mathematical Sciences, Capital Normal University, Beijing, 100048, People's Republic of China
| | - Yining Zhu
- School of Mathematical Sciences, Capital Normal University, Beijing, 100048, People's Republic of China
- Shenzhen National Applied Mathematics Center, Southern University of Science and Technology, Shenzhen, 518055, People's Republic of China
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Zhang W, Zhao S, Pan H, Zhao X. A Locally Weighted Linear Regression Look-Up Table-Based Iterative Reconstruction Method for Dual Spectral CT. IEEE Trans Biomed Eng 2023; 70:3028-3039. [PMID: 37155374 DOI: 10.1109/tbme.2023.3274195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
OBJECTIVE Compared with traditional computed tomography (CT), dual spectral CT (DSCT) exhibits superior material distinguishability and thus has broad prospects in industrial and medical fields. In iterative DSCT algorithms, accurately modeling forward-projection functions is crucial, but it is very difficult to analytically provide accurate functions. METHODS In this article, we propose a locally weighted linear regression look-up table-based (LWLR-LUT) iterative reconstruction method for DSCT. First, the proposed method uses LWLR to establish LUTs for the forward-projection functions through calibration phantoms, achieving good local information calibration. Second, the reconstructed images can be iteratively obtained through the established LUTs. The proposed method not only does not require knowledge of the X-ray spectra and the attenuation coefficients, but also implicitly accounts for some scattered radiation while fitting locally the forward-projection functions in the calibration space. RESULTS Both numerical simulations and real data experiments demonstrate that the proposed method can achieve highly accurate polychromatic forward-projection functions and greatly improve the quality of the images reconstructed from scattering-free and scattering projections. CONCLUSION The proposed method is simple and practical, and achieves good material decomposition effects for objects with different complex structures through simple calibration phantoms.
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Yu X, Cai A, Liang N, Wang S, Zheng Z, Li L, Yan B. Direct Multi-Material Reconstruction via Iterative Proximal Adaptive Descent for Spectral CT Imaging. Bioengineering (Basel) 2023; 10:470. [PMID: 37106656 PMCID: PMC10136068 DOI: 10.3390/bioengineering10040470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/31/2023] [Accepted: 04/04/2023] [Indexed: 04/29/2023] Open
Abstract
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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Affiliation(s)
| | | | | | | | | | | | - Bin Yan
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China
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Zhang W, Zhao S, Pan H, Zhao Y, Zhao X. An iterative reconstruction method based on monochromatic images for dual energy CT. Med Phys 2021; 48:6437-6452. [PMID: 34468032 DOI: 10.1002/mp.15200] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 08/08/2021] [Accepted: 08/26/2021] [Indexed: 11/06/2022] Open
Abstract
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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Affiliation(s)
- Weibin Zhang
- School of Mathematical Sciences, Capital Normal University, Beijing, China.,Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, China
| | - Shusen Zhao
- School of Mathematical Sciences, Capital Normal University, Beijing, China.,Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, China
| | - Huiying Pan
- School of Mathematical Sciences, Capital Normal University, Beijing, China.,Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, China
| | - Yunsong Zhao
- School of Mathematical Sciences, Capital Normal University, Beijing, China.,Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, China
| | - Xing Zhao
- School of Mathematical Sciences, Capital Normal University, Beijing, China.,Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, China.,Pazhou Lab, Guangzhou, China
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