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He Y, Wang C, Yu W, Wang J. Multiobjective optimization guided by image quality index for limited-angle CT image reconstruction. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024:XST240111. [PMID: 38995762 DOI: 10.3233/xst-240111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/14/2024]
Abstract
BACKGROUND Due to the incomplete projection data collected by limited-angle computed tomography (CT), severe artifacts are present in the reconstructed image. Classical regularization methods such as total variation (TV) minimization, ℓ0 minimization, are unable to suppress artifacts at the edges perfectly. Most existing regularization methods are single-objective optimization approaches, stemming from scalarization methods for multiobjective optimization problems (MOP). OBJECTIVE To further suppress the artifacts and effectively preserve the edge structures of the reconstructed image. METHOD This study presents a multiobjective optimization model incorporates both data fidelity term and ℓ0-norm of the image gradient as objective functions. It employs an iterative approach different from traditional scalarization methods, using the maximization of structural similarity (SSIM) values to guide optimization rather than minimizing the objective function.The iterative method involves two steps, firstly, simultaneous algebraic reconstruction technique (SART) optimizes the data fidelity term using SSIM and the Simulated Annealing (SA) algorithm for guidance. The degradation solution is accepted in the form of probability, and guided image filtering (GIF) is introduced to further preserve the image edge when the degradation solution is rejected. Secondly, the result from the first step is integrated into the second objective function as a constraint, we use ℓ0 minimization to optimize ℓ0-norm of the image gradient, and the SSIM, SA algorithm and GIF are introduced to guide optimization process by improving SSIM value like the first step. RESULTS With visual inspection, the peak signal-to-noise ratio (PSNR), root mean square error (RMSE), and SSIM values indicate that our approach outperforms other traditional methods. CONCLUSIONS The experiments demonstrate the effectiveness of our method and its superiority over other classical methods in artifact suppression and edge detail restoration.
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Affiliation(s)
- Yu He
- School of Mathematical Sciences, Chongqing Normal University, ChongQing, China
| | - Chengxiang Wang
- School of Mathematical Sciences, Chongqing Normal University, ChongQing, China
| | - Wei Yu
- School of Biomedical Engineering and Imaging, Xianning Medical College, Hubei University of Science and Technology, Xianning, China
- Key Laboratory of Optoeletronic and Intelligent Control, Hubei University of Science and Technology, Xianning, China
| | - Jiaxi Wang
- College of Computer Science, Chengdu University, Chengdu, China
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Song Q, Gong C. Image reconstruction method for incomplete CT projection based on self-guided image filtering. Med Biol Eng Comput 2024; 62:2101-2116. [PMID: 38457068 DOI: 10.1007/s11517-024-03044-9] [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: 08/10/2023] [Accepted: 02/03/2024] [Indexed: 03/09/2024]
Abstract
In some fields of medical diagnosis or industrial nondestructive testing, it is difficult to obtain complete computed tomography (CT) data due to the limitation of radiation dose or other factors. Therefore, image reconstruction of incomplete projection data is the focus of this paper. In this paper, a new image reconstruction model based on self-guided image filtering (SGIF) term is proposed for few-view and segmental limited-angle (SLA) CT reconstruction. Then the alternating direction method (ADM) is used to solve this model. For simplicity, we call it ADM-SGIF method. The key idea of ADM-SGIF method is to use the reconstructed image itself as a reference and utilize its structural features to guide CT reconstruction. This method can effectively preserve image structures and remove shading artifacts. To validate the effectiveness of the proposed reconstruction method, we conduct digital phantom and real CT data experiments. The results indicate that ADM-SGIF method outperforms competing methods, including total variation (TV), relative total variation (RTV), and L0-norm minimization solved by ADM (ADM-L0) methods, in both subjective and objective evaluations.
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Affiliation(s)
- Qiang Song
- School of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing, 400067, China
| | - Changcheng Gong
- School of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing, 400067, China.
- Chongqing Key Laboratory of Statistical Intelligent Computing and Monitoring, Chongqing Technology and Business University, Chongqing, 400067, China.
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3
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Xu R, Liu Y, Li Z, Gui Z. Sparse-view CT reconstruction based on group-based sparse representation using weighted guided image filtering. BIOMED ENG-BIOMED TE 2024; 0:bmt-2023-0581. [PMID: 38598849 DOI: 10.1515/bmt-2023-0581] [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: 11/12/2023] [Accepted: 03/21/2024] [Indexed: 04/12/2024]
Abstract
OBJECTIVES In the past, guided image filtering (GIF)-based methods often utilized total variation (TV)-based methods to reconstruct guidance images. And they failed to reconstruct the intricate details of complex clinical images accurately. To address these problems, we propose a new sparse-view CT reconstruction method based on group-based sparse representation using weighted guided image filtering. METHODS In each iteration of the proposed algorithm, the result constrained by the group-based sparse representation (GSR) is used as the guidance image. Then, the weighted guided image filtering (WGIF) was used to transfer the important features from the guidance image to the reconstruction of the SART method. RESULTS Three representative slices were tested under 64 projection views, and the proposed method yielded the best visual effect. For the shoulder case, the PSNR can achieve 48.82, which is far superior to other methods. CONCLUSIONS The experimental results demonstrate that our method is more effective in preserving structures, suppressing noise, and reducing artifacts compared to other methods.
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Affiliation(s)
- Rong Xu
- School of Information and Communication Engineering, 66291 North University of China , Taiyuan, China
| | - Yi Liu
- School of Information and Communication Engineering, 66291 North University of China , Taiyuan, China
| | - Zhiyuan Li
- School of Information and Communication Engineering, 66291 North University of China , Taiyuan, China
| | - Zhiguo Gui
- School of Information and Communication Engineering, 66291 North University of China , Taiyuan, China
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4
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Bai H, Su M, Pang C, Xiong Z, Xia B, Zhao D, Li C, Mo Z, Gao F. An image reconstruction method for transmission computed tomography with the constraint of the linear attenuation coefficients. Appl Radiat Isot 2023; 202:111062. [PMID: 37797448 DOI: 10.1016/j.apradiso.2023.111062] [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: 02/08/2023] [Revised: 09/21/2023] [Accepted: 09/29/2023] [Indexed: 10/07/2023]
Abstract
For the reconstructed image of transmission computed tomography, the linear attenuation coefficients of the diagnosed object may improve the image quality by adding additional constraint besides the projection data. In the present work, an image reconstruction method with the constraint of the linear attenuation coefficients is developed and two models including a classical numerical Shepp-Logan model and a Monte Carlo model are used to show the corresponding benefits. The results indicate that the number of the projection angles is potentially decreased to 1/3 of itself while the quality of the reconstructed image is not deteriorated.
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Affiliation(s)
- Huaiyong Bai
- Institute of Materials, China Academy of Engineering Physics, Jiangyou, 621907, China
| | - Ming Su
- Institute of Materials, China Academy of Engineering Physics, Jiangyou, 621907, China
| | - Chengguo Pang
- Institute of Materials, China Academy of Engineering Physics, Jiangyou, 621907, China
| | - Zhonghua Xiong
- Institute of Materials, China Academy of Engineering Physics, Jiangyou, 621907, China
| | - Binyuan Xia
- Institute of Materials, China Academy of Engineering Physics, Jiangyou, 621907, China
| | - Deshan Zhao
- Institute of Materials, China Academy of Engineering Physics, Jiangyou, 621907, China.
| | - Chenguang Li
- Institute of Materials, China Academy of Engineering Physics, Jiangyou, 621907, China
| | - Zhaohong Mo
- Institute of Materials, China Academy of Engineering Physics, Jiangyou, 621907, China
| | - Fan Gao
- Institute of Materials, China Academy of Engineering Physics, Jiangyou, 621907, China
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5
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Image reconstruction method for limited-angle CT based on total variation minimization using guided image filtering. Med Biol Eng Comput 2022; 60:2109-2118. [DOI: 10.1007/s11517-022-02579-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 04/22/2022] [Indexed: 10/18/2022]
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6
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Xu S, Yang B, Xu C, Tian J, Liu Y, Yin L, Liu S, Zheng W, Liu C. Sparse Angle CBCT Reconstruction Based on Guided Image Filtering. Front Oncol 2022; 12:832037. [PMID: 35574417 PMCID: PMC9093219 DOI: 10.3389/fonc.2022.832037] [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: 12/09/2021] [Accepted: 03/21/2022] [Indexed: 11/13/2022] Open
Abstract
Cone-beam Computerized Tomography (CBCT) has the advantages of high ray utilization and detection efficiency, short scan time, high spatial and isotropic resolution. However, the X-rays emitted by CBCT examination are harmful to the human body, so reducing the radiation dose without damaging the reconstruction quality is the key to the reconstruction of CBCT. In this paper, we propose a sparse angle CBCT reconstruction algorithm based on Guided Image FilteringGIF, which combines the classic Simultaneous Algebra Reconstruction Technique(SART) and the Total p-Variation (TpV) minimization. Due to the good edge-preserving ability of SART and noise suppression ability of TpV minimization, the proposed method can suppress noise and artifacts while preserving edge and texture information in reconstructed images. Experimental results based on simulated and real-measured CBCT datasets show the advantages of the proposed method.
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Affiliation(s)
- Siyuan Xu
- School of Automation, University of Electronic Science and Technology of China, Chengdu, China
| | - Bo Yang
- School of Automation, University of Electronic Science and Technology of China, Chengdu, China
| | - Congcong Xu
- School of Automation, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiawei Tian
- School of Automation, University of Electronic Science and Technology of China, Chengdu, China
| | - Yan Liu
- School of Automation, University of Electronic Science and Technology of China, Chengdu, China
| | - Lirong Yin
- Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA, United States
| | - Shan Liu
- School of Automation, University of Electronic Science and Technology of China, Chengdu, China
| | - Wenfeng Zheng
- School of Automation, University of Electronic Science and Technology of China, Chengdu, China
| | - Chao Liu
- Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier (LIRMM), Unité Mixte de Recherche (UMR) 5506, French National Center for Scientific Research (CNRS) - University of Montpellier (UM), Montpellier, France
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7
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NeuRec: Incorporating Interpatient prior to Sparse-View Image Reconstruction for Neurorehabilitation. BIOMED RESEARCH INTERNATIONAL 2022; 2022:5426643. [PMID: 35586813 PMCID: PMC9110181 DOI: 10.1155/2022/5426643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 03/31/2022] [Indexed: 12/02/2022]
Abstract
Medical imaging technologies such as computed tomography (CT) and magnetic resonance imaging (MRI) imaging are indispensable for contemporary neurorehabilitation diagnostics, intervention, and monitoring. It would be desirable to reconstruct images from sparse measurements to reduce the ionizing radiation and motion artifacts. Although recent coordinate-based representation methods have shown promise advances for sparse-view reconstruction, they overfit a single MLP on a single patient. In this work, we generalize it across many patients by incorporating an interpatient prior into the ill-posed inverse/reconstruction problem, which is the missing ingredient in the previous works. The experiment demonstrates that our method significantly improves image quality over the state-of-the-art both qualitatively and quantitatively. Thus, our method provides a powerful and principled means to deal with the measurement-scarce problem.
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8
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Polat A, Kumrular RK. A Realistic Breast Phantom Proposal for 3D Image Reconstruction in Digital Breast Tomosynthesis. Technol Cancer Res Treat 2022; 21:15330338221104567. [PMID: 36071652 PMCID: PMC9459460 DOI: 10.1177/15330338221104567] [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] [Indexed: 11/24/2022] Open
Abstract
Objectives: Iterative (eg, simultaneous algebraic reconstruction
technique [SART]) and analytical (eg, filtered back projection [FBP]) image
reconstruction techniques have been suggested to provide adequate
three-dimensional (3D) images of the breast for capturing microcalcifications in
digital breast tomosynthesis (DBT). To decide on the reconstruction method in
clinical DBT, it must first be tested in a simulation resembling the real
clinical environment. The purpose of this study is to introduce a 3D realistic
breast phantom for determining the reconstruction method in clinical
applications. Methods: We designed a 3D realistic breast phantom
with varying dimensions (643-5123) mimicking some
structures of a real breast such as milk ducts, lobules, and ribs using
TomoPhantom software. We generated microcalcifications, which mimic cancerous
cells, with a separate MATLAB code and embedded them into the phantom for
testing and benchmark studies in DBT. To validate the characterization of the
phantom, we tested the distinguishability of microcalcifications by performing
3D image reconstruction methods (SART and FBP) using Laboratory of Computer
Vision (LAVI) open-source reconstruction toolbox. Results: The
creation times of the proposed realistic breast phantom were seconds of 2.5916,
8.4626, 57.6858, and 472.1734 for 643, 1283,
2563, and 5123, respectively. We presented
reconstructed images and quantitative results of the phantom for SART (1-2-4-8
iterations) and FBP, with 11 to 23 projections. We determined qualitatively and
quantitatively that SART (2-4 iter.) yields better results than FBP. For
example, for 23 projections, the contrast-to-noise ratio (CNR) values of SART (2
iter.) and FBP were 2.871 and 0.497, respectively. Conclusions: We
created a computationally efficient realistic breast phantom that is eligible
for reconstruction and includes anatomical structures and microcalcifications,
successfully. By proposing this breast phantom, we provided the opportunity to
test which reconstruction methods can be used in clinical applications vary
according to various parameters such as the No. of iterations and projections in
DBT.
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Affiliation(s)
- Adem Polat
- 52950Department of Electrical-Electronics Engineering, Çanakkale Onsekiz Mart University, Çanakkale, Turkey
| | - Raziye Kubra Kumrular
- Institute of Sound and Vibration Research, 7423University of Southampton, Southampton, UK
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9
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Computed Tomography as a Characterization Tool for Engineered Scaffolds with Biomedical Applications. MATERIALS 2021; 14:ma14226763. [PMID: 34832165 PMCID: PMC8619049 DOI: 10.3390/ma14226763] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 10/29/2021] [Accepted: 11/04/2021] [Indexed: 12/16/2022]
Abstract
The ever-growing field of materials with applications in the biomedical field holds great promise regarding the design and fabrication of devices with specific characteristics, especially scaffolds with personalized geometry and architecture. The continuous technological development pushes the limits of innovation in obtaining adequate scaffolds and establishing their characteristics and performance. To this end, computed tomography (CT) proved to be a reliable, nondestructive, high-performance machine, enabling visualization and structure analysis at submicronic resolutions. CT allows both qualitative and quantitative data of the 3D model, offering an overall image of its specific architectural features and reliable numerical data for rigorous analyses. The precise engineering of scaffolds consists in the fabrication of objects with well-defined morphometric parameters (e.g., shape, porosity, wall thickness) and in their performance validation through thorough control over their behavior (in situ visualization, degradation, new tissue formation, wear, etc.). This review is focused on the use of CT in biomaterial science with the aim of qualitatively and quantitatively assessing the scaffolds’ features and monitoring their behavior following in vivo or in vitro experiments. Furthermore, the paper presents the benefits and limitations regarding the employment of this technique when engineering materials with applications in the biomedical field.
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10
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Li S, Wang Q, Wei X, Cao Z, Zhao Q. Three-dimensional reconstruction of integrated implosion targets from simulated small-angle pinhole images. OPTICS EXPRESS 2020; 28:34848-34859. [PMID: 33182944 DOI: 10.1364/oe.400778] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 10/19/2020] [Indexed: 06/11/2023]
Abstract
To indirectly evaluate the asymmetry of the radiation drive under limited measurement conditions in inertial confinement fusion research, we have proposed an integral method to approximate the three-dimensional self-radiation distribution of the compressed plasma core using only four pinhole images from a single laser entrance hole at a maximum projection angle of 10°. The simultaneous algebraic reconstruction technique (SART) that uses spatial constraints provided by the prior structural information and the central pinhole image is utilized in the simulation. The simulation results showed that the normalized mean square deviation between the original distribution and reconstruction results of the central radiation area of the simulated cavity was 0.4401, and the structural similarity of the cavity radiation distribution was 0.5566. Meanwhile, using more diagnostic holes could achieve better structural similarity and lower reconstruction error. In addition, the results indicated that our new proposed method could reconstruct the distribution of a compressed plasma core in a vacuum hohlraum with high accuracy.
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11
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He Y, Zeng L, Yu W, Gong C. Noise suppression-guided image filtering for low-SNR CT reconstruction. Med Biol Eng Comput 2020; 58:2621-2629. [PMID: 32839918 DOI: 10.1007/s11517-020-02246-1] [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] [Received: 10/09/2019] [Accepted: 08/16/2020] [Indexed: 10/23/2022]
Abstract
In practical computed tomography (CT) applications, projections with low signal-to-noise ratio (SNR) are often encountered due to the reduction of radiation dose or device limitations. In these situations, classical reconstruction algorithms, like simultaneous algebraic reconstruction technique (SART), cannot reconstruct high-quality CT images. Block-matching and 3D filtering (BM3D)-based iterative reconstruction algorithm (POCS-BM3D) has remarkable effect in dealing with CT reconstruction from noisy projections. However, BM3D may restrain noise with excessive loss of details in the case of low-SNR CT reconstruction. In order to achieve a preferable trade-off between noise suppression and edge preservation, we introduce guided image filtering (GIF) into low-SNR CT reconstruction, and propose noise suppression-guided image filtering reconstruction (NSGIFR) algorithm. In each iteration of NSGIFR, the output image of SART reserves more details and is used as input image of GIF, while the image denoised by BM3D serves as guidance image of GIF. Experimental results indicate that the proposed algorithm displays outstanding performance on preserving structures and suppressing noise for low-SNR CT reconstruction. NSGIFR can achieve more superior image quality than SART, POCS-TV and POCS-BM3D in terms of visual effect and quantitative analysis. Graphical abstract Block-matching and 3D filtering (BM3D)-based iterative reconstruction algorithm (POCS-BM3D) has remarkable effect in dealing with CT reconstruction from noisy projections. However, BM3D may restrain noise with excessive loss of details in the case of low-SNR CT reconstruction. In order to achieve a preferable trade-off between noise suppression and edge preservation, we introduce guided image filtering (GIF) into low-SNR CT reconstruction, and propose noise suppression-guided image filtering reconstruction (NSGIFR) algorithm.
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Affiliation(s)
- Yuanwei He
- College of Mathematics and Statistics, Chongqing University, Chongqing, 401331, China.,Engineering Research Center of Industrial Computed Tomography Nondestructive Testing of the Education Ministry of China, Chongqing University, Chongqing, 400044, China
| | - Li Zeng
- College of Mathematics and Statistics, Chongqing University, Chongqing, 401331, China. .,Engineering Research Center of Industrial Computed Tomography Nondestructive Testing of the Education Ministry of China, Chongqing University, Chongqing, 400044, China.
| | - Wei Yu
- School of Biomedical Engineering, Hubei University of Science and Technology, Xianning, 437100, China
| | - Changcheng Gong
- Engineering Research Center of Industrial Computed Tomography Nondestructive Testing of the Education Ministry of China, Chongqing University, Chongqing, 400044, China.,Key Laboratory of Optoelectronic Technology and System of the Education Ministry of China, Chongqing University, Chongqing, 400044, China
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12
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Murase K. Simultaneous correction of sensitivity and spatial resolution in projection-based magnetic particle imaging. Med Phys 2020; 47:1845-1859. [PMID: 32003025 DOI: 10.1002/mp.14056] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 01/17/2020] [Accepted: 01/21/2020] [Indexed: 11/11/2022] Open
Abstract
PURPOSE The purpose of this study was to develop a method to simultaneously correct the spatial resolution and inhomogeneous sensitivity of a receiving coil in projection-based magnetic particle imaging - and to investigate its efficacy through simulation and experimental studies. METHODS Magnetic particle imaging (MPI) images were reconstructed using the simultaneous algebraic reconstruction technique (SART), and simultaneous corrections to sensitivity and spatial resolution were performed by incorporating the sensitivity map of the receiving coil and the system function into the SART algorithm. After each SART update, the regularization method - with total variation (TV) minimization - was used to suppress noise amplification and artifact generation. For comparison, MPI images were also reconstructed using the filtered backprojection (FBP) method and the FBP-truncated singular value decomposition (TSVD) method, in which the system function was deconvolved from the projection data using TSVD. In simulation studies, the sensitivity map of a second-order, gradiometer-type receiving coil was generated using the Biot-Savart law, while the system function was obtained by calculating the MPI signals induced by magnetic nanoparticles at various distances from a field-free line (FFL), using a lock-in-amplifier model. The effects of a regularization parameter for TV minimization (α), number of iterations (N), and signal-to-noise ratio (SNR) of the MPI signals on the reconstructed MPI images of a numerical phantom were evaluated, using the image profiles and percent root mean square error (PRMSE). Experimental studies involved the calculation of the system function using a tube phantom. Projection data for an A-shaped phantom were acquired using our MPI scanner, and their MPI images were reconstructed from the projection data, as described above. RESULTS When both the sensitivity and spatial resolution were corrected (SART-SR), the quality of the reconstructed images was seen to have improved, compared to when the spatial resolution was not corrected - or when the FBP and FBP-TSVD methods were used. When SNR was low (20), a larger α value yielded a better image. The minimum PRMSE occurred at N ≈ 200-400 and increased with increasing N thereafter. When SNR was high (100), the image quality was generally not dependent on the α value within its studied range. The PRMSE decreased slowly with increasing N, and tended to converge to a constant value. The full width at half maximum (FWHM) of the profile was obtained from the A-shaped phantom, reconstructed using the SART-SR algorithm with α = 0.05 and N = 1000. The FWHM value of the tube (2 mm diameter) in the A-shaped phantom image was found to be 2.2 mm on average, whereas those calculated from the images obtained by the FBP and FBP-TSVD methods were 4.4 and 3.0 mm on average, respectively. Spatial resolution improved when using the FBP-TSVD method as compared to the FBP method but image distortion and artifacts were observed. CONCLUSIONS Although further studies are necessary to optimize the parameters used in the SART algorithm and in TV minimization, the present results suggest that the proposed method is useful for improving the image quality of projection-based MPI.
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Affiliation(s)
- Kenya Murase
- Department of Medical Physics and Engineering, Faculty of Health Science, Graduate School of Medicine, Osaka University, Suita, Osaka, 565-0871, Japan.,Center for Borderless Design of Medicine, Graduate School of Medicine, Osaka University, Suita, Osaka, 565-0871, Japan
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13
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Zhao Y, Ji D, Li Y, Zhao X, Lv W, Xin X, Han S, Hu C. Three-dimensional visualization of microvasculature from few-projection data using a novel CT reconstruction algorithm for propagation-based X-ray phase-contrast imaging. BIOMEDICAL OPTICS EXPRESS 2020; 11:364-387. [PMID: 32010522 PMCID: PMC6968748 DOI: 10.1364/boe.380084] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 11/29/2019] [Accepted: 12/12/2019] [Indexed: 05/23/2023]
Abstract
Propagation-based X-ray phase-contrast imaging (PBI) is a powerful nondestructive imaging technique that can reveal the internal detailed structures in weakly absorbing samples. Extending PBI to CT (PBCT) enables high-resolution and high-contrast 3D visualization of microvasculature, which can be used for the understanding, diagnosis and therapy of diseases involving vasculopathy, such as cardiovascular disease, stroke and tumor. However, the long scan time for PBCT impedes its wider use in biomedical and preclinical microvascular studies. To address this issue, a novel CT reconstruction algorithm for PBCT is presented that aims at shortening the scan time for microvascular samples by reducing the number of projections while maintaining the high quality of reconstructed images. The proposed algorithm combines the filtered backprojection method into the iterative reconstruction framework, and a weighted guided image filtering approach (WGIF) is utilized to optimize the intermediate reconstructed images. Notably, the homogeneity assumption on the microvasculature sample is adopted as prior knowledge, and therefore, a prior image of microvasculature structures can be acquired by a k-means clustering approach. Then, the prior image is used as the guided image in the WGIF procedure to effectively suppress streaking artifacts and preserve microvasculature structures. To evaluate the effectiveness and capability of the proposed algorithm, simulation experiments on 3D microvasculature numerical phantom and real experiments with CT reconstruction on the microvasculature sample are performed. The results demonstrate that the proposed algorithm can, under noise-free and noisy conditions, significantly reduce the artifacts and effectively preserve the microvasculature structures on the reconstructed images and thus enables it to be used for clear and accurate 3D visualization of microvasculature from few-projection data. Therefore, for 3D visualization of microvasculature, the proposed algorithm can be considered an effective approach for reducing the scan time required by PBCT.
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Affiliation(s)
- Yuqing Zhao
- School of Biomedical Engineering and
Technology, Tianjin Medical University, Tianjin 300070, China
| | - Dongjiang Ji
- The School of Science, Tianjin University
of Technology and Education, Tianjin 300222, China
| | - Yimin Li
- School of Biomedical Engineering and
Technology, Tianjin Medical University, Tianjin 300070, China
| | - Xinyan Zhao
- Liver Research Center, Beijing Friendship
Hospital, Capital Medical University, Beijing 100050, China
| | - Wenjuan Lv
- School of Biomedical Engineering and
Technology, Tianjin Medical University, Tianjin 300070, China
| | - Xiaohong Xin
- School of Biomedical Engineering and
Technology, Tianjin Medical University, Tianjin 300070, China
| | - Shuo Han
- School of Biomedical Engineering and
Technology, Tianjin Medical University, Tianjin 300070, China
| | - Chunhong Hu
- School of Biomedical Engineering and
Technology, Tianjin Medical University, Tianjin 300070, China
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14
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Ding G, Liu Y, Zhang R, Xin HL. A joint deep learning model to recover information and reduce artifacts in missing-wedge sinograms for electron tomography and beyond. Sci Rep 2019; 9:12803. [PMID: 31488874 PMCID: PMC6728317 DOI: 10.1038/s41598-019-49267-x] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 08/22/2019] [Indexed: 11/10/2022] Open
Abstract
We present a joint model based on deep learning that is designed to inpaint the missing-wedge sinogram of electron tomography and reduce the residual artifacts in the reconstructed tomograms. Traditional methods, such as weighted back projection (WBP) and simultaneous algebraic reconstruction technique (SART), lack the ability to recover the unacquired project information as a result of the limited tilt range; consequently, the tomograms reconstructed using these methods are distorted and contaminated with the elongation, streaking, and ghost tail artifacts. To tackle this problem, we first design a sinogram filling model based on the use of Residual-in-Residual Dense Blocks in a Generative Adversarial Network (GAN). Then, we use a U-net structured Generative Adversarial Network to reduce the residual artifacts. We build a two-step model to perform information recovery and artifacts removal in their respective suitable domain. Compared with the traditional methods, our method offers superior Peak Signal to Noise Ratio (PSNR) and the Structural Similarity Index (SSIM) to WBP and SART; even with a missing wedge of 45°, our method offers reconstructed images that closely resemble the ground truth with nearly no artifacts. In addition, our model has the advantage of not needing inputs from human operators or setting hyperparameters such as iteration steps and relaxation coefficient used in TV-based methods, which highly relies on human experience and parameter fine turning.
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Affiliation(s)
- Guanglei Ding
- Department of Physics and Astronomy, University of California, Irvine, CA, 92697, United States.,School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Yitong Liu
- School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Rui Zhang
- Department of Physics and Astronomy, University of California, Irvine, CA, 92697, United States
| | - Huolin L Xin
- Department of Physics and Astronomy, University of California, Irvine, CA, 92697, United States.
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15
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Yu W, Wang C, Nie X, Zeng D. Sparsity-induced dynamic guided filtering approach for sparse-view data toward low-dose x-ray computed tomography. ACTA ACUST UNITED AC 2018; 63:235016. [DOI: 10.1088/1361-6560/aaeea6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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16
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Jiang C, Zhang Q, Fan R, Hu Z. Super-resolution CT Image Reconstruction Based on Dictionary Learning and Sparse Representation. Sci Rep 2018; 8:8799. [PMID: 29892023 PMCID: PMC5996061 DOI: 10.1038/s41598-018-27261-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Accepted: 05/31/2018] [Indexed: 12/22/2022] Open
Abstract
In this paper, a single-computed tomography (CT) image super-resolution (SR) reconstruction scheme is proposed. This SR reconstruction scheme is based on sparse representation theory and dictionary learning of low- and high-resolution image patch pairs to improve the poor quality of low-resolution CT images obtained in clinical practice using low-dose CT technology. The proposed strategy is based on the idea that image patches can be well represented by sparse coding of elements from an overcomplete dictionary. To obtain similarity of the sparse representations, two dictionaries of low- and high-resolution image patches are jointly trained. Then, sparse representation coefficients extracted from the low-resolution input patches are used to reconstruct the high-resolution output. Sparse representation is used such that the trained dictionary pair can reduce computational costs. Combined with several appropriate iteration operations, the reconstructed high-resolution image can attain better image quality. The effectiveness of the proposed method is demonstrated using both clinical CT data and simulation image data. Image quality evaluation indexes (root mean squared error (RMSE) and peak signal-to-noise ratio (PSNR)) indicate that the proposed method can effectively improve the resolution of a single CT image.
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Affiliation(s)
- Changhui Jiang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.,Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Qiyang Zhang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.,Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Rui Fan
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Zhanli Hu
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
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17
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Ji D, Qu G, Hu C, Zhao Y, Chen X. Combine TV-L1 model with guided image filtering for wide and faint ring artifacts correction of in-line x-ray phase contrast computed tomography. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2018; 26:51-70. [PMID: 28854528 DOI: 10.3233/xst-17276] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In practice, mis-calibrated detector pixels give rise to wide and faint ring artifacts in the reconstruction image of the In-line phase-contrast computed tomography (IL-PC-CT). Ring artifacts correction is essential in IL-PC-CT. In this study, a novel method of wide and faint ring artifacts correction was presented based on combining TV-L1 model with guided image filtering (GIF) in the reconstruction image domain. The new correction method includes two main steps namely, the GIF step and the TV-L1 step. To validate the performance of this method, simulation data and real experimental synchrotron data are provided. The results demonstrate that TV-L1 model with GIF step can effectively correct the wide and faint ring artifacts for IL-PC-CT.
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Affiliation(s)
- Dongjiang Ji
- School of Science, Beijing Jiaotong University, Beijing, China
- School of Science, Tianjin University of Technology and Education, Tianjin, China
| | - Gangrong Qu
- School of Science, Beijing Jiaotong University, Beijing, China
- Beijing Center for Mathematics and Information Interdisciplinary Sciences, Beijing, China
| | - Chunhong Hu
- College of Biomedical Engineering, Tianjin Medical University, Tianjin, China
| | - Yuqing Zhao
- College of Biomedical Engineering, Tianjin Medical University, Tianjin, China
| | - Xiaodong Chen
- Key laboratory of Opto-electronic Information Technology, Ministry of Education (Tianjin University), Tianjin, China
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18
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Peng C, Qiu B, Li M, Yang Y, Zhang C, Gong L, Zheng J. GPU-Accelerated Dynamic Wavelet Thresholding Algorithm for X-Ray CT Metal Artifact Reduction. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2018. [DOI: 10.1109/trpms.2017.2776970] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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19
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Yu W, Wang C, Huang M. Edge-preserving reconstruction from sparse projections of limited-angle computed tomography using ℓ 0-regularized gradient prior. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2017; 88:043703. [PMID: 28456252 DOI: 10.1063/1.4981132] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Accurate images reconstructed from limited computed tomography (CT) data are desired when reducing the X-ray radiation exposure imposed on patients. The total variation (TV), known as the l1-norm of the image gradient magnitudes, is popular in CT reconstruction from incomplete projection data. However, as the projection data collected are from a sparse-view of the limited scanning angular range, the results reconstructed by a TV-based method suffer from blocky artifact and gradual changed artifacts near the edges, which in turn make the reconstruction images degraded. Different from the TV, the ℓ0-norm of an image gradient counts the number of its non-zero coefficients of the image gradient. Since the regularization based on the ℓ0-norm of the image gradient will not penalize the large gradient magnitudes, the edge can be effectively retained. In this work, an edge-preserving image reconstruction method based on l0-regularized gradient prior was investigated for limited-angle computed tomography from sparse projections. To solve the optimization model effectively, the variable splitting and the alternating direction method (ADM) were utilized. Experiments demonstrated that the ADM-like method used for the non-convex optimization problem has better performance than other classical iterative reconstruction algorithms in terms of edge preservation and artifact reduction.
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Affiliation(s)
- Wei Yu
- School of Biomedical Engineering, Hubei University of Science and Technology, Xianning 437100, China
| | - Chengxiang Wang
- School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Min Huang
- School of Biomedical Engineering, South-Central University for Nationalities, Wuhan 430074, China
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