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Wang S, Wu W, Cai A, Xu Y, Vardhanabhuti V, Liu F, Yu H. Image-spectral decomposition extended-learning assisted by sparsity for multi-energy computed tomography reconstruction. Quant Imaging Med Surg 2023; 13:610-630. [PMID: 36819292 PMCID: PMC9929415 DOI: 10.21037/qims-22-235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 11/10/2022] [Indexed: 12/13/2022]
Abstract
Background Multi-energy computed tomography (CT) provides multiple channel-wise reconstructed images, and they can be used for material identification and k-edge imaging. Nonetheless, the projection datasets are frequently corrupted by various noises (e.g., electronic, Poisson) in the acquisition process, resulting in lower signal-noise-ratio (SNR) measurements. Multi-energy CT images have local sparsity, nonlocal self-similarity in spatial dimension, and correlation in spectral dimension. Methods In this paper, we propose an image-spectral decomposition extended-learning assisted by sparsity (IDEAS) method to fully exploit these intrinsic priors for multi-energy CT image reconstruction. Particularly, a nonlocal low-rank Tucker decomposition (TD) is employed to utilize the correlation and nonlocal self-similarity priors. Moreover, considering the advantages of multi-task tensor dictionary learning (TDL) in sparse representation, an adaptive spatial dictionary and an adaptive spectral dictionary are trained during the iterative reconstruction process. Furthermore, a weighted total variation (TV) regularization term is employed to encourage local sparsity. Results Numerical simulation, physical phantom, and preclinical mouse experiments are performed to validate the proposed IDEAS algorithm. Specifically, in the simulation experiments, the proposed IDEAS reconstructed high-quality images that are very close to the references. For example, the root mean square error (RMSE) of IDEAS image in energy bin 1 is as low as 0.0672, while the RMSE of other methods are higher than 0.0843. Besides, the structural similarity (SSIM) of IDEAS reconstructed image in energy bin 1 is greater than 0.98. For material decomposition, the RMSE of IDEAS bone component is as low as 0.0152, and other methods are higher than 0.0199. In addition, the computational cost of IDEAS is as low as 98.8 s for one iteration, and the competing tensor decomposition method is higher than 327 s. Conclusions To further improve the quality of the reconstructed multi-energy CT images, multiple prior regularizations are introduced to the multi-energy CT reconstructed model, leading to an IDEAS method. Both qualitative and quantitative evaluation of our results confirm the outstanding performance of the proposed algorithm compared to the state-of-the-arts.
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Affiliation(s)
- Shaoyu Wang
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China;,Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing, China;,Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, USA
| | - Weiwen Wu
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China
| | - Ailong Cai
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Yongshun Xu
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, USA
| | - Varut Vardhanabhuti
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
| | - Fenglin Liu
- Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing, China
| | - Hengyong Yu
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, USA
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He Y, Zeng L, Xu Q, Wang Z, Yu H, Shen Z, Yang Z, Zhou R. Spectral CT reconstruction via low-rank representation and structure preserving regularization. Phys Med Biol 2023; 68. [PMID: 36595335 DOI: 10.1088/1361-6560/acabf9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022]
Abstract
Objective:With the development of computed tomography (CT) imaging technology, it is possible to acquire multi-energy data by spectral CT. Being different from conventional CT, the X-ray energy spectrum of spectral CT is cut into several narrow bins which leads to the result that only a part of photon can be collected in each individual energy channel.This can severely degrade the image qualities. To address this problem, we propose a spectral CT reconstruction algorithm based on low-rank representation and structure preserving regularization in this paper.Approach:To make full use of the prior knowledge about both the inter-channel correlation and the sparsity in gradient domain of inner-channel data, this paper combines a low-rank correlation descriptor with a structure extraction operator as priori regularization terms for spectral CT reconstruction. Furthermore, a split-Bregman based iterative algorithm is developed to solve the reconstruction model. Finally, we propose a multi-channel adaptive parameters generation strategy according to CT values of each individual energy channel.Main results: Experimental results on numerical simulations and real mouse data indicate that the proposed algorithm achieves higher accuracy on both reconstruction and material decomposition than the methods based on simultaneous algebraic reconstruction technique (SART), total variation minimization (TVM), total variation with low-rank (LRTV), and spatial-spectral cube matching frame (SSCMF). Compared with SART, our algorithm improves the feature similarity (FSIM) by 40.4% on average for numerical simulation reconstruction, whereas TVM, LRTV, and SSCMF correspond to 26.1%, 28.2%, and 29.5%, respectively.Significance: We outline a multi-channel reconstruction algorithm tailored for spectral CT. The qualitative and quantitative comparisons present a significant improvement of image quality, indicating its promising potential in spectral CT imaging.
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Affiliation(s)
- Yuanwei He
- College of Mathematics and Statistics, Chongqing University, Chongqing 401331, People's Republic of China.,Engineering Research Center of Industrial Computed Tomography Nondestructive Testing, Ministry of Education, Chongqing University, Chongqing 400044, People's Republic of China
| | - Li Zeng
- College of Mathematics and Statistics, Chongqing University, Chongqing 401331, People's Republic of China.,Engineering Research Center of Industrial Computed Tomography Nondestructive Testing, Ministry of Education, Chongqing University, Chongqing 400044, People's Republic of China
| | - Qiong Xu
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People's Republic of China.,Jinan Laboratory of Applied Nuclear Science, Jinan 250131, People's Republic of China
| | - Zhe Wang
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People's Republic of China.,Jinan Laboratory of Applied Nuclear Science, Jinan 250131, People's Republic of China
| | - Haijun Yu
- Engineering Research Center of Industrial Computed Tomography Nondestructive Testing, Ministry of Education, Chongqing University, Chongqing 400044, People's Republic of China.,Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, People's Republic of China
| | - Zhaoqiang Shen
- College of Mathematics and Statistics, Chongqing University, Chongqing 401331, People's Republic of China.,Engineering Research Center of Industrial Computed Tomography Nondestructive Testing, Ministry of Education, Chongqing University, Chongqing 400044, People's Republic of China
| | - Zhaojun Yang
- College of Mathematics and Statistics, Chongqing University, Chongqing 401331, People's Republic of China.,Engineering Research Center of Industrial Computed Tomography Nondestructive Testing, Ministry of Education, Chongqing University, Chongqing 400044, People's Republic of China
| | - Rifeng Zhou
- Engineering Research Center of Industrial Computed Tomography Nondestructive Testing, Ministry of Education, Chongqing University, Chongqing 400044, People's Republic of China.,Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, People's Republic of China.,State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, People's Republic of China
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3
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Wu W, Yu H, Liu F, Zhang J, Vardhanabhuti V, Chen J. Spectral CT reconstruction via Spectral-Image Tensor and Bidirectional Image-gradient minimization. Comput Biol Med 2022; 151:106080. [PMID: 36327881 DOI: 10.1016/j.compbiomed.2022.106080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/19/2022] [Accepted: 09/03/2022] [Indexed: 12/27/2022]
Abstract
It is challenging to obtain good image quality in spectral computed tomography (CT) as the photon-number for the photon-counting detectors is limited for each narrow energy bin. This results in a lower signal to noise ratio (SNR) for the projections. To handle this issue, we first formulate the weight bidirectional image gradient with L0-norm constraint of spectral CT image. Then, as a new regularizer, bidirectional image gradient with L0-norm constraint is introduced into the tensor decomposition model, generating the Spectral-Image Tensor and Bidirectional Image-gradient Minimization (SITBIM) algorithm. Finally, the split-Bregman method is employed to optimize the proposed SITBIM mathematical model. The experiments on the numerical mouse phantom and real mouse experiments are designed to validate and evaluate the SITBIM method. The results demonstrate that the SITBIM can outperform other state-of-the-art methods (including TVM, TV + LR, SSCMF and NLCTF). INDEX TERMS: -spectral CT, image reconstruction, tensor decomposition, unidirectional image gradient, image similarity.
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Affiliation(s)
- Weiwen Wu
- The School of Biomedical Engineering, Shenzhen Campus, Sun Yat-sen University, Shenzhen, Guangdong, 518107, China; The University of Hong Kong, Hong Kong, 999077, China
| | - Hengyong Yu
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, 01854, USA
| | - Fenglin Liu
- The Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing, 400044, China
| | - Jianjia Zhang
- The School of Biomedical Engineering, Shenzhen Campus, Sun Yat-sen University, Shenzhen, Guangdong, 518107, China.
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Yu X, Cai A, Wang L, Zheng Z, Wang Y, Wang Z, Li L, Yan B. Framelet tensor sparsity with block matching for spectral CT reconstruction. Med Phys 2022; 49:2486-2501. [PMID: 35142376 DOI: 10.1002/mp.15529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 01/11/2022] [Accepted: 01/21/2022] [Indexed: 11/12/2022] Open
Abstract
PURPOSE Spectral computed tomography (CT) based on the photon-counting detection system has the capability to produce energy-discriminative attenuation maps of objects with a single scan. However, the insufficiency of photons collected into the narrow energy bins results in high quantum noise levels causing low image quality. This work aims to improve spectral CT image quality by developing a novel regularization based on framelet tensor prior. METHODS First, similar patches are extracted from highly correlated inter-channel images in spectral and spatial domains, and stacked to form a third-order tensor after vectorization along the energy channels. Second, the framelet tensor nuclear norm (FTNN) is introduced and applied to construct the regularization to exploit the sparsity embedded in nonlocal similarity of spectral images, and thus the reconstruction problem is modeled as a constrained optimization. Third, an iterative algorithm is proposed by utilizing the alternating direction method of multipliers framework in which efficient solvers are developed for each subproblem. RESULTS Both numerical simulations and real data verifications were performed to evaluate and validate the proposed FTNN based method. Compared to the analytic, TV-based, and the state-of-the-art tensor-based methods, the proposed method achieves higher numerical accuracy on both reconstructed CT images and decomposed material maps in the mouse data indicating the capability in noise suppression and detail preservation of the proposed method. CONCLUSIONS A framelet tensor sparsity-based iterative algorithm is proposed for spectral reconstruction. The qualitative and quantitative comparisons show a promising improvement of image quality, indicating its promising potential in spectral CT imaging. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Xiaohuan Yu
- 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
| | - Linyuan Wang
- 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
| | - Zhe Wang
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 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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5
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Wu W, Hu D, Niu C, Broeke LV, Butler APH, Cao P, Atlas J, Chernoglazov A, Vardhanabhuti V, Wang G. Deep learning based spectral CT imaging. Neural Netw 2021; 144:342-358. [PMID: 34560584 DOI: 10.1016/j.neunet.2021.08.026] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 07/14/2021] [Accepted: 08/20/2021] [Indexed: 10/20/2022]
Abstract
Spectral computed tomography (CT) has attracted much attention in radiation dose reduction, metal artifacts removal, tissue quantification and material discrimination. The x-ray energy spectrum is divided into several bins, each energy-bin-specific projection has a low signal-noise-ratio (SNR) than the current-integrating counterpart, which makes image reconstruction a unique challenge. Traditional wisdom is to use prior knowledge based iterative methods. However, this kind of methods demands a great computational cost. Inspired by deep learning, here we first develop a deep learning based reconstruction method; i.e., U-net with Lpp-norm, Total variation, Residual learning, and Anisotropic adaption (ULTRA). Specifically, we emphasize the various multi-scale feature fusion and multichannel filtering enhancement with a denser connection encoding architecture for residual learning and feature fusion. To address the image deblurring problem associated with the L22- loss, we propose a general Lpp-loss, p>0. Furthermore, the images from different energy bins share similar structures of the same object, the regularization characterizing correlations of different energy bins is incorporated into the Lpp- loss function, which helps unify the deep learning based methods with traditional compressed sensing based methods. Finally, the anisotropically weighted total variation is employed to characterize the sparsity in the spatial-spectral domain to regularize the proposed network In particular, we validate our ULTRA networks on three large-scale spectral CT datasets, and obtain excellent results relative to the competing algorithms. In conclusion, our quantitative and qualitative results in numerical simulation and preclinical experiments demonstrate that our proposed approach is accurate, efficient and robust for high-quality spectral CT image reconstruction.
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Affiliation(s)
- Weiwen Wu
- Department of Diagnostic Radiology, Queen Mary Hospital, University of Hong Kong, Hong Kong, People's Republic of China; Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, School of Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Dianlin Hu
- The Laboratory of Image Science and Technology, Southeast University, Nanjing, People's Republic of China
| | - Chuang Niu
- Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, School of Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Lieza Vanden Broeke
- Department of Diagnostic Radiology, Queen Mary Hospital, University of Hong Kong, Hong Kong, People's Republic of China
| | | | - Peng Cao
- Department of Diagnostic Radiology, Queen Mary Hospital, University of Hong Kong, Hong Kong, People's Republic of China
| | - James Atlas
- Department of Radiology, University of Otago, Christchurch, New Zealand
| | | | - Varut Vardhanabhuti
- Department of Diagnostic Radiology, Queen Mary Hospital, University of Hong Kong, Hong Kong, People's Republic of China.
| | - Ge Wang
- Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, School of Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
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6
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Cueva E, Meaney A, Siltanen S, Ehrhardt MJ. Synergistic multi-spectral CT reconstruction with directional total variation. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200198. [PMID: 34218669 DOI: 10.1098/rsta.2020.0198] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/12/2021] [Indexed: 06/13/2023]
Abstract
This work considers synergistic multi-spectral CT reconstruction where information from all available energy channels is combined to improve the reconstruction of each individual channel. We propose to fuse these available data (represented by a single sinogram) to obtain a polyenergetic image which keeps structural information shared by the energy channels with increased signal-to-noise ratio. This new image is used as prior information during a channel-by-channel minimization process through the directional total variation. We analyse the use of directional total variation within variational regularization and iterative regularization. Our numerical results on simulated and experimental data show improvements in terms of image quality and in computational speed. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.
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Affiliation(s)
- Evelyn Cueva
- Research Center on Mathematical Modeling (MODEMAT), Escuela Politécnica Nacional, Quito, Ecuador
| | - Alexander Meaney
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Samuli Siltanen
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
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7
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Wu W, Chen P, Wang S, Vardhanabhuti V, Liu F, Yu H. Image-domain Material Decomposition for Spectral CT using a Generalized Dictionary Learning. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021; 5:537-547. [PMID: 34222737 PMCID: PMC8248524 DOI: 10.1109/trpms.2020.2997880] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The spectral computed tomography (CT) has huge advantages by providing accurate material information. Unfortunately, due to the instability or overdetermination of material decomposition model, the accuracy of material decomposition can be compromised in practice. Very recently, the dictionary learning based image-domain material decomposition (DLIMD) can obtain high accuracy for material decompositions from reconstructed spectral CT images. This method can explore the correlation of material components to some extent by training a unified dictionary from all material images. In addition, the dictionary learning based prior as a penalty is applied on material components independently, and many parameters would be carefully elaborated in practice. Because the concentration of contrast agent in clinical applications is low, it can result in data inconsistency for dictionary based representation during the iteration process. To avoid the aforementioned limitations and further improve the accuracy of materials, we first construct a generalized dictionary learning based image-domain material decomposition (GDLIMD) model. Then, the material tensor image is unfolded along the mode-1 to enhance the correlation of different materials. Finally, to avoid the data inconsistency of low iodine contrast, a normalization strategy is employed. Both physical phantom and tissue-synthetic phantom experiments demonstrate the proposed GDLIMD method outperforms the DLIMD and direct inversion (DI) methods.
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Affiliation(s)
- Weiwen Wu
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, 999077, China
| | - Peijun Chen
- Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China
| | - Shaoyu Wang
- Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China
| | - Varut Vardhanabhuti
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, 999077, China
| | - Fenglin Liu
- Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China
| | - Hengyong Yu
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA
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Arridge SR, Ehrhardt MJ, Thielemans K. (An overview of) Synergistic reconstruction for multimodality/multichannel imaging methods. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200205. [PMID: 33966461 DOI: 10.1098/rsta.2020.0205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Imaging is omnipresent in modern society with imaging devices based on a zoo of physical principles, probing a specimen across different wavelengths, energies and time. Recent years have seen a change in the imaging landscape with more and more imaging devices combining that which previously was used separately. Motivated by these hardware developments, an ever increasing set of mathematical ideas is appearing regarding how data from different imaging modalities or channels can be synergistically combined in the image reconstruction process, exploiting structural and/or functional correlations between the multiple images. Here we review these developments, give pointers to important challenges and provide an outlook as to how the field may develop in the forthcoming years. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.
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Affiliation(s)
- Simon R Arridge
- Department of Computer Science, University College London, London, UK
| | - Matthias J Ehrhardt
- Department of Mathematical Sciences, University of Bath, Bath, UK
- Institute for Mathematical Innovation, University of Bath, Bath, UK
| | - Kris Thielemans
- Institute of Nuclear Medicine, University College London, London, UK
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9
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Yu H, Li L, Tan C, Liu F, Zhou R. X-ray source translation based computed tomography (STCT). OPTICS EXPRESS 2021; 29:19743-19758. [PMID: 34266078 DOI: 10.1364/oe.427659] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 05/31/2021] [Indexed: 06/13/2023]
Abstract
Micro computed tomography (µCT) allows the noninvasive visualization and 3D reconstruction of internal structures of objects with high resolution. However, the current commercial µCT system relatively rotates the source-detector or objects to collect projections, referred as RCT in this paper, and has difficulties in imaging large objects with high resolutions because fabrication of large-area, inexpensive flat-panel detectors remains a challenge. In this paper, we proposed a source translation based CT (STCT) for imaging large objects with high resolution to get rid of the limitation of the detector size, where the field of view is primarily determined by the source translation distance. To compensate for the deficiency of incomplete data in STCT, we introduced multi-scanning STCT (mSTCT), from which the projections theoretically meet the conditions required for accurate reconstructions. Theoretical and numerical studies showed that mSTCT has the ability to accurately image large objects without any visible artifacts. Numerical simulations also indicated that mSTCT has a potential capability to precisely image the region of interest (ROI) inside objects, which remains a challenge in RCT due to truncated projections. In addition, an experimental platform for mSTCT has been established, from which the 2D and 3D reconstructed results demonstrated its feasibility for µCT applications. Moreover, STCT also has a great potential for security inspection and product screening by using two perpendicular STCTs, with advantages of low-cost equipment and high-speed examination.
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Wu W, Shi J, Yu H, Wu W, Vardhanabhuti V. Tensor Gradient L₀-Norm Minimization-Based Low-Dose CT and Its Application to COVID-19. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2021; 70:4503012. [PMID: 35582003 PMCID: PMC8769022 DOI: 10.1109/tim.2021.3050190] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 12/15/2020] [Accepted: 12/30/2020] [Indexed: 05/03/2023]
Abstract
Methods to recover high-quality computed tomography (CT) images in low-dose cases will be of great benefit. To reach this goal, sparse-data subsampling is one of the common strategies to reduce radiation dose, which is attracting interest among the researchers in the CT community. Since analytic image reconstruction algorithms may lead to severe image artifacts, the iterative algorithms have been developed for reconstructing images from sparsely sampled projection data. In this study, we first develop a tensor gradient L0-norm minimization (TGLM) for low-dose CT imaging. Then, the TGLM model is optimized by using the split-Bregman method. The Coronavirus Disease 2019 (COVID-19) has been sweeping the globe, and CT imaging has been deployed for detection and assessing the severity of the disease. Finally, we first apply our proposed TGLM method for COVID-19 to achieve low-dose scan by incorporating the 3-D spatial information. Two COVID-19 patients (64 years old female and 56 years old man) were scanned by the [Formula: see text]CT 528 system, and the acquired projections were retrieved to validate and evaluate the performance of the TGLM.
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Affiliation(s)
- Weiwen Wu
- Department of Diagnostic RadiologyThe University of Hong Kong Hong Kong China
| | - Jun Shi
- School of Communication and Information EngineeringShanghai Institute for Advanced Communication and Data Science, Shanghai University Shanghai 200444 China
| | - Hengyong Yu
- Department of Electrical and Computer EngineeringUniversity of Massachusetts Lowell Lowell MA 01854 USA
| | - Weifei Wu
- People's Hospital of China Three Gorges University Yichang 443000 China
- First People's Hospital of Yichang Yichang 443000 China
| | - Varut Vardhanabhuti
- Department of Diagnostic RadiologyThe University of Hong Kong Hong Kong China
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11
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Wang S, Wu W, Feng J, Liu F, Yu H. Low-dose spectral CT reconstruction based on image-gradient L 0-norm and adaptive spectral PICCS. Phys Med Biol 2020; 65:245005. [PMID: 32693399 DOI: 10.1088/1361-6560/aba7cf] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The photon-counting detector based spectral computed tomography (CT) is promising for lesion detection, tissue characterization, and material decomposition. However, the lower signal-to-noise ratio within multi-energy projection dataset can result in poorly reconstructed image quality. Recently, as prior information, a high-quality spectral mean image was introduced into the prior image constrained compressed sensing (PICCS) framework to suppress noise, leading to spectral PICCS (SPICCS). In the original SPICCS model, the image gradient L1-norm is employed, and it can cause blurred edge structures in the reconstructed images. Encouraged by the advantages in edge preservation and finer structure recovering, the image gradient L0-norm was incorporated into the PICCS model. Furthermore, due to the difference of energy spectrum in different channels, a weighting factor is introduced and adaptively adjusted for different channel-wise images, leading to an L0-norm based adaptive SPICCS (L0-ASPICCS) algorithm for low-dose spectral CT reconstruction. The split-Bregman method is employed to minimize the objective function. Extensive numerical simulations and physical phantom experiments are performed to evaluate the proposed method. By comparing with the state-of-the-art algorithms, such as the simultaneous algebraic reconstruction technique, total variation minimization, and SPICCS, the advantages of our proposed method are demonstrated in terms of both qualitative and quantitative evaluation results.
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Affiliation(s)
- Shaoyu Wang
- Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, People's Republic of China. Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA 01854, United States of America. Engineering Research Center of Industrial Computed Tomography Nondestructive Testing, Ministry of Education, Chongqing University, Chongqing 400044, People's Republic of China
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Zhang W, Liang N, Wang Z, Cai A, Wang L, Tang C, Zheng Z, Li L, Yan B, Hu G. Multi-energy CT reconstruction using tensor nonlocal similarity and spatial sparsity regularization. Quant Imaging Med Surg 2020; 10:1940-1960. [PMID: 33014727 DOI: 10.21037/qims-20-594] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background Multi-energy computed tomography (MECT) based on a photon-counting detector is an emerging imaging modality that collects projections at several energy bins with a single scan. However, the limited number of photons collected into the divided, narrow energy bins results in high quantum noise levels in reconstructed images. This study aims to improve MECT image quality by minimizing noise levels while retaining image details. Methods A novel MECT reconstruction method was proposed by exploiting the nonlocal tensor similarity among interchannel images and spatial sparsity in single-channel images. Similar patches were initially extracted from the interchannel images in spectral and spatial domains, then stacked into a new three-order tensor. Intrinsic tensor sparsity regularization that combined the Tuker and canonical polyadic (CP) low-rank decomposition techniques were applied to exploit the nonlocal similarity of the formulated tensor. Spatial sparsity in single-channel images was modeled by total variation (TV) regularization that utilizes the compressibility of gradient image. A new MECT reconstruction model was established by simultaneously incorporating the intrinsic tensor sparsity and TV regularizations. The iterative alternating minimization method was utilized to solve the reconstruction model based on a flexible framework. Results The proposed method was applied to the digital phantom and real mouse data to assess its feasibility and reliability. The reconstruction and decomposition results in the mouse data were encouraging and demonstrated the ability of the proposed method in noise suppression while preserving image details, not observed with other methods. Imaging data from the digital phantom illustrated this method as achieving the best intuitive reconstruction and decomposition results among all compared methods. They reduced the root mean square error (RMSE) by 89.75%, 50.75%, and 36.54% on the reconstructed images compared with analytic, TV-based, and tensor-based methods, respectively. This phenomenon was also observed with decomposition results, where the RMSE was also reduced by 97.96%, 67.74%, 72.05%, respectively. Conclusions In this study, we proposed a reconstruction method for photon counting detector-based MECT, using the intrinsic tensor sparsity and TV regularizations. Improvements in noise suppression and detail preservation in the digital phantom and real mouse data were validated by the qualitative and quantitative evaluations on the reconstruction and decomposition results, verifying the potential of the proposed method in MECT reconstruction.
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Affiliation(s)
- Wenkun Zhang
- Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Ningning Liang
- Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Zhe Wang
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
| | - Ailong Cai
- Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Linyuan Wang
- Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Chao Tang
- Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Zhizhong Zheng
- Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Lei Li
- Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Bin Yan
- Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Guoen Hu
- Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
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Tao S, Marsh JF, Tao A, Michalak GJ, Rajendran K, McCollough CH, Leng S. Multi-energy CT imaging for large patients using dual-source photon-counting detector CT. Phys Med Biol 2020; 65:17NT01. [PMID: 32503022 DOI: 10.1088/1361-6560/ab99e4] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Multi-energy CT imaging of large patients with conventional dual-energy (DE)-CT using an energy-integrating-detector (EID) is challenging due to photon starvation-induced image artifacts, especially in lower tube potential (80-100 kV) images. Here, we performed phantom experiments to investigate the performance of DECT for morbidly obese patients, using an iodine and water material decomposition task as an example, on an emulated dual-source (DS)-photon-counting-detector (PCD)-CT, and compared its performance with a clinical DS-EID-CT. An abdominal CT phantom with iodine inserts of different concentrations was wrapped with tissue-equivalent gel layers to emulate a large patient (50 cm lateral size). The phantom was scanned on a research whole-body single-source (SS)-PCD-CT (140 kV tube potential), a DS-PCD-CT (100/Sn140 kV; Sn140 indicates 140 kV with Sn filter), and a clinical DS-EID-CT (100/Sn140 kV) with the same radiation dose. Phantom scans were repeated five times on each system. The DS-PCD-CT acquisition was emulated by scanning twice on the SS-PCD-CT using different tube potentials. The multi-energy CT images acquired on each system were then reconstructed, and iodine- and water-specific images were generated using material decomposition. The root-mean-square-error (RMSE) between true and measured iodine concentrations were calculated for each system and compared. The images acquired on the DS-EID-CT showed severe artifacts, including ringing, reduced uniformity, and photon starvation artifacts, especially for low-energy images. These were largely reduced in DS-PCD-CT images. The CT number difference that was measured using regions-of-interest across field-of-view were reduced from 20.3 ± 0.9 (DS-EID-CT) to 2.5 ± 0.4 HU on DS-PCD-CT, showing improved image uniformity using DS-PCD-CT. Iodine RMSE was reduced from 3.42 ± 0.03 mg ml-1 (SS-PCD-CT) and 2.90 ± 0.03 mg ml-1 (DS-EID-CT) to 2.39 ± 0.05 mg ml-1 using DS-PCD-CT. DS-PCD-CT out-performed a clinical DS-EID-CT for iodine and water-based material decomposition on phantom emulating obese patients by reducing image artifacts and improving iodine quantification (RMSE reduced by 20%). With DS-PCD-CT, multi-energy CT can be performed on large patients that cannot be accommodated with current DECT.
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Affiliation(s)
- Shengzhen Tao
- Department of Radiology, Mayo Clinic, Rochester, MN, United States of America
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14
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Niu S, Lu S, Zhang Y, Huang X, Zhong Y, Yu G, Wang J. Statistical image-based material decomposition for triple-energy computed tomography using total variation regularization. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2020; 28:751-771. [PMID: 32597827 DOI: 10.3233/xst-200672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
BACKGROUND Triple-energy computed tomography (TECT) can obtain x-ray attenuation measurements at three energy spectra, thereby allowing identification of different material compositions with same or very similar attenuation coefficients. This ability is known as material decomposition, which can decompose TECT images into different basis material image. However, the basis material image would be severely degraded when material decomposition is directly performed on the noisy TECT measurements using a matrix inversion method. OBJECTIVE To achieve high quality basis material image, we present a statistical image-based material decomposition method for TECT, which uses the penalized weighted least-squares (PWLS) criteria with total variation (TV) regularization (PWLS-TV). METHODS The weighted least-squares term involves the noise statistical properties of the material decomposition process, and the TV regularization penalizes differences between local neighboring pixels in a decomposed image, thereby contributing to improving the quality of the basis material image. Subsequently, an alternating optimization method is used to minimize the objective function. RESULTS The performance of PWLS-TV is quantitatively evaluated using digital and mouse thorax phantoms. The experimental results show that PWLS-TV material decomposition method can greatly improve the quality of decomposed basis material image compared to the quality of images obtained using the competing methods in terms of suppressing noise and preserving edge and fine structure details. CONCLUSIONS The PWLS-TV method can simultaneously perform noise reduction and material decomposition in one iterative step, and it results in a considerable improvement of basis material image quality.
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Affiliation(s)
- Shanzhou Niu
- Jiangxi Key Laboratory of Numerical Simulation Technology, School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, China
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Shaohui Lu
- Jiangxi Key Laboratory of Numerical Simulation Technology, School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, China
| | - You Zhang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Xiaokun Huang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Yuncheng Zhong
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Gaohang Yu
- School of Science, Hangzhou Dianzi University, Hangzhou, China
| | - Jing Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
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15
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Abstract
Spectral computed tomography (CT) has a great potential in material identification and decomposition. To achieve high-quality material composition images and further suppress the x-ray beam hardening artifacts, we first propose a one-step material reconstruction model based on Taylor's first-order expansion. Then, we develop a basic material reconstruction method named material simultaneous algebraic reconstruction technique (MSART). Considering the local similarity of each material image, we incorporate a powerful block matching frame (BMF) into the material reconstruction (MR) model and generate a BMF based MR (BMFMR) method. Because the BMFMR model contains the L 0-norm problem, we adopt a split-Bregman method for optimization. The numerical simulation and physical phantom experiment results validate the correctness of the material reconstruction algorithms and demonstrate that the BMF regularization outperforms the total variation and no-local mean regularizations.
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Affiliation(s)
- Weiwen Wu
- Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, People’s Republic of China
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA 01854, United States of America
- The contributions of W Wu and Q Wang are equal
| | - Qian Wang
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA 01854, United States of America
- The contributions of W Wu and Q Wang are equal
| | - Fenglin Liu
- Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, People’s Republic of China
- Engineering Research Center of Industrial Computed Tomography Nondestructive Testing, Ministry of Education, Chongqing University, Chongqing 400044, People’s Republic of China
| | - Yining Zhu
- School of Mathematical Sciences, Capital Normal University, Beijing 100048, People’s Republic of China
| | - Hengyong Yu
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA 01854, United States of America
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16
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Wu W, Liu F, Zhang Y, Wang Q, Yu H. Non-Local Low-Rank Cube-Based Tensor Factorization for Spectral CT Reconstruction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:1079-1093. [PMID: 30371357 PMCID: PMC6536296 DOI: 10.1109/tmi.2018.2878226] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Spectral computed tomography (CT) reconstructs material-dependent attenuation images from the projections of multiple narrow energy windows, which is meaningful for material identification and decomposition. Unfortunately, the multi-energy projection datasets usually have lower signal-noise ratios (SNR). Very recently, a spatial-spectral cube matching frame (SSCMF) was proposed to explore the non-local spatial-spectral similarities for spectral CT. This method constructs a group by clustering up a series of non-local spatial-spectral cubes. The small size of spatial patches for such a group makes the SSCMF fail to fully encode the sparsity and low-rank properties. The hard-thresholding and collaboration filtering in the SSCMF also cause difficulty in recovering the image features and spatial edges. While all the steps are operated on 4-D group, the huge computational cost and memory load might not be affordable in practice. To avoid the above limitations and further improve the image quality, we first formulate a non-local cube-based tensor instead of group to encode the sparsity and low-rank properties. Then, as a new regularizer, the Kronecker-basis-representation tensor factorization is employed into a basic spectral CT reconstruction model to enhance the capability of image feature extraction and spatial edge preservation, generating a non-local low-rank cube-based tensor factorization (NLCTF) method. Finally, the split-Bregman method is adopted to solve the NLCTF model. Both numerical simulations and preclinical mouse studies are performed to validate and evaluate the NLCTF algorithm. The results show that the NLCTF method outperforms the other state-of-the-art competing algorithms.
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17
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Yu H, Wu W, Chen P, Gong C, Jiang J, Wang S, Liu F, Yu H. Image gradient L 0-norm based PICCS for swinging multi-source CT reconstruction. OPTICS EXPRESS 2019; 27:5264-5279. [PMID: 30876127 PMCID: PMC6410921 DOI: 10.1364/oe.27.005264] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 01/24/2019] [Accepted: 01/29/2019] [Indexed: 06/09/2023]
Abstract
Dynamic computed tomography (CT) is usually employed to image motion objects, such as beating heart, coronary artery and cerebral perfusion, etc. Recently, to further improve the temporal resolution for aperiodic industrial process imaging, the swinging multi-source CT (SMCT) systems and the corresponding swinging multi-source prior image constrained compressed sensing (SM-PICCS) method were developed. Since the SM-PICCS uses the L1-norm of image gradient, the edge structures in the reconstructed images are blurred and motion artifacts are still present. Inspired by the advantages in terms of image edge preservation and fine structure recovering, the L0-norm of image gradient is incorporated into the prior image constrained compressed sensing, leading to an L0-PICCS Algorithm 1Table 1The parameters of L0-PICCS (δ1,δ2,λ1*,λ2*) for numerical simulation.Sourceswδ1(10-2)δ2(10-2)λ1*(10-2)λ2*(10-8)Noise-free510522.001.525522.001.55035002.00471014.33332.00500025522.00500050222.005000Noise51062002.505002554502.501.55054502.901.571027.385.91.5810000258.285.91.5850050522.001.5. The experimental results confirm that the L0-PICCS outperforms the SM-PICCS in both visual inspection and quantitative analysis.
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Affiliation(s)
- Haijun Yu
- Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China
- Engineering Research Center of Industrial Computed Tomography Nondestructive Testing, Ministry of Education, Chongqing University, Chongqing 400044, China
- College of Mechanical Engineering, Chongqing University, Chongqing 400044, China
- These authors contributed equally to the work
| | - Weiwen Wu
- Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China
- Engineering Research Center of Industrial Computed Tomography Nondestructive Testing, Ministry of Education, Chongqing University, Chongqing 400044, China
- These authors contributed equally to the work
| | - Peijun Chen
- Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China
| | - Changcheng Gong
- Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China
- Engineering Research Center of Industrial Computed Tomography Nondestructive Testing, Ministry of Education, Chongqing University, Chongqing 400044, China
| | - Junru Jiang
- College of Mechanical Engineering, Chongqing University, Chongqing 400044, China
| | - Shaoyu Wang
- Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China
- Engineering Research Center of Industrial Computed Tomography Nondestructive Testing, Ministry of Education, Chongqing University, Chongqing 400044, China
| | - Fenglin Liu
- Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China
- Engineering Research Center of Industrial Computed Tomography Nondestructive Testing, Ministry of Education, Chongqing University, Chongqing 400044, China
| | - Hengyong Yu
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA
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