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Wang Y, Chen Z, Gao H, Deng Y, Zhang L. An analytical form of ring artifact correction for computed tomography based on directional gradient domain optimization. Med Phys 2024; 51:4121-4132. [PMID: 38452276 DOI: 10.1002/mp.16988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 01/03/2024] [Accepted: 01/29/2024] [Indexed: 03/09/2024] Open
Abstract
BACKGROUND Ring artifact is a common problem in Computed Tomography (CT), which can lead to inaccurate diagnoses and treatment plans. It can be caused by various factors such as detector imperfections, anti-scatter grids, or other nonuniform filters placed in the x-ray beam. Physics-based corrections for these x-ray source and detector non-uniformity, in general cannot completely get rid of the ring artifacts. Therefore, there is a need for a robust method that can effectively remove ring artifacts in the image domain while preserving details. PURPOSE This study aims to develop an effective method for removing ring artifacts from reconstructed CT images. METHODS The proposed method starts by converting the reconstructed CT image containing ring artifacts into polar coordinates, thereby transforming these artifacts into stripes. Relative Total Variation is used to extract the image's overall structural information. For the efficient restoration of intricate details, we introduce Directional Gradient Domain Optimization (DGDO) and design objective functions that make use of both the image's gradient and its overall structure. Subsequently, we present an efficient analytical algorithm to minimize these objective functions. The image obtained through DGDO is then transformed back into Cartesian coordinates, finalizing the ring artifact correction process. RESULTS Through a series of synthetic and real-world experiments, we have effectively demonstrated the prowess of our proposed method in the correction of ring artifacts while preserving intricate details in reconstructed CT images. In a direct comparison, our method has exhibited superior visual quality compared to several previous approaches. These results underscore the remarkable potential of our approach for enhancing the overall quality and clinical utility of CT imaging. CONCLUSIONS The proposed method offers an analytical solution for removing ring artifacts from CT images while preserving details. As ring artifacts are a common problem in CT imaging, this method has high practical value in the medical field. The proposed method can improve image quality and reduce the difficulty of disease diagnosis, thereby contributing to better patient care.
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Affiliation(s)
- Yuang Wang
- Department of Engineering Physics, Tsinghua University, Beijing, China
| | - Zhiqiang Chen
- Department of Engineering Physics, Tsinghua University, Beijing, China
| | - Hewei Gao
- Department of Engineering Physics, Tsinghua University, Beijing, China
| | - Yifan Deng
- Department of Engineering Physics, Tsinghua University, Beijing, China
| | - Li Zhang
- Department of Engineering Physics, Tsinghua University, Beijing, China
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Cui W, Lv H, Wang J, Zheng Y, Wu Z, Zhao H, Zheng J, Li M. Feature shared multi-decoder network using complementary learning for Photon counting CT ring artifact suppression. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:529-547. [PMID: 38669511 DOI: 10.3233/xst-230396] [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: 04/28/2024]
Abstract
BACKGROUND Photon-counting computed tomography (Photon counting CT) utilizes photon-counting detectors to precisely count incident photons and measure their energy. These detectors, compared to traditional energy integration detectors, provide better image contrast and material differentiation. However, Photon counting CT tends to show more noticeable ring artifacts due to limited photon counts and detector response variations, unlike conventional spiral CT. OBJECTIVE To comprehensively address this issue, we propose a novel feature shared multi-decoder network (FSMDN) that utilizes complementary learning to suppress ring artifacts in Photon counting CT images. METHODS Specifically, we employ a feature-sharing encoder to extract context and ring artifact features, facilitating effective feature sharing. These shared features are also independently processed by separate decoders dedicated to the context and ring artifact channels, working in parallel. Through complementary learning, this approach achieves superior performance in terms of artifact suppression while preserving tissue details. RESULTS We conducted numerous experiments on Photon counting CT images with three-intensity ring artifacts. Both qualitative and quantitative results demonstrate that our network model performs exceptionally well in correcting ring artifacts at different levels while exhibiting superior stability and robustness compared to the comparison methods. CONCLUSIONS In this paper, we have introduced a novel deep learning network designed to mitigate ring artifacts in Photon counting CT images. The results illustrate the viability and efficacy of our proposed network model as a new deep learning-based method for suppressing ring artifacts.
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Affiliation(s)
- Wei Cui
- Institute of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, China
| | - Haipeng Lv
- Institute of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, China
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Jiping Wang
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | | | - Zhongyi Wu
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Hui Zhao
- Wenzhou People's Hospital, Wenzhou, China
| | - Jian Zheng
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Ming Li
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
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Zhao Y, Ma C, Ji DJ, Peng Y, Li F, Li Y, Hu C. Structure-aware guided filtering for a ring artifact correction in synchrotron x-ray microtomography. APPLIED OPTICS 2023; 62:7400-7410. [PMID: 37855508 DOI: 10.1364/ao.501832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 09/06/2023] [Indexed: 10/20/2023]
Abstract
Synchrotron-based x-ray microtomography (S-µCT) is a powerful non-invasive three-dimensional (3D) imaging technique used for visualizing the internal structure of objects with micron-scale spatial resolution. However, in practical applications, ring artifacts often occur in S-µCT, which significantly degrades image quality and hinders interpretation. In this study, we propose a ring artifact correction method based on guided image filtering (GIF). The method first extracts structural prior from the input S-µCT images and then uses it as the guidance image to correct the ring artifacts. Finally, GIF with a self-guidance image is employed to further enhance image quality. Extensive comparisons and analyses on simulations and real data experiments demonstrate that the proposed method is capable of effectively correcting ring artifacts, accompanied by low-dose noise suppression and sparse-view artifact reduction. These findings suggest that the proposed method has great potential to promote the wider applications of S-µCT in the future.
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Li Y, Han S, Zhao Y, Li F, Ji D, Zhao X, Liu D, Jian J, Hu C. Synchrotron microtomography image restoration via regularization representation and deep CNN prior. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107181. [PMID: 36257200 DOI: 10.1016/j.cmpb.2022.107181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 09/29/2022] [Accepted: 10/08/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Synchrotron-based X-ray microtomography (S-µCT) is a promising imaging technique that plays an important role in modern medical science. S-µCT systems often cause various artifacts and noises in the reconstructed CT images, such as ring artifacts, quantum noise, and electronic noise. In most situations, such noise and artifacts occur simultaneously, which results in a deterioration in the image quality and affects subsequent research. Due to the complexity of the distribution of these mixed artifacts and noise, it is difficult to restore the corrupted images. To address this issue, we propose a novel algorithm to remove mixed artifacts and noise from S-µCT images simultaneously. METHODS There are two important aspects of our method. Regarding ring artifacts, because of their specific structural characteristics, regularization-based methods are more suitable; thus, low-rank tensor decomposition and total variation are utilized to represent their directional and locally piecewise smoothness properties. Moreover, to determine the implicit prior of the random noise, a convolutional neural network (CNN) based method is used. The advantages of traditional regularization and the deep CNN are then combined and embedded in a plug-and-play framework. Hence, an efficient image restoration algorithm is proposed to address the problem of mixed artifacts and noise in S-µCT images. RESULTS Our proposed method was assessed by utilizing simulations and real data experiments. The qualitative results showed that the proposed method could effectively remove ring artifacts as well as random noise. The quantitative results demonstrated that the proposed method achieved almost the best results in terms of PSNR, SSIM and MAE compared to other methods. CONCLUSIONS The proposed method can serve as an effective tool for restoring corrupted S-µCT images, and it has the potential to promote the application of S-µCT.
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Affiliation(s)
- Yimin Li
- 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
| | - Yuqing Zhao
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin 300070, China
| | - Fangzhi Li
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin 300070, China
| | - Dongjiang Ji
- School of Science, Tianjin University of Technology and Education, Tianjin 300222, China
| | - Xinyan Zhao
- Liver Research Center, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China; Beijing Key Laboratory of Translational Medicine in Liver Cirrhosis and National Clinical Research Center of Digestive Disease, Beijing 100050, China
| | - Dayong Liu
- Tianjin Medical University school of stomatology, Tianjin 300070, China
| | - Jianbo Jian
- Department of Radiation Oncology, Tianjin Medical University General Hospital, Tianjin 300070, China
| | - Chunhong Hu
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin 300070, China.
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Selim M, Rashed EA, Atiea MA, Kudo H. Sparsity-based method for ring artifact elimination in computed tomography. PLoS One 2022; 17:e0268410. [PMID: 35763462 PMCID: PMC9239489 DOI: 10.1371/journal.pone.0268410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 04/29/2022] [Indexed: 11/18/2022] Open
Abstract
Ring artifact elimination is one of the popular problems in computed tomography (CT). It appears in the reconstructed image in the form of bright or dark patterns of concentric circles. In this paper, based on the compressed sensing theory, we propose a method for eliminating the ring artifact during the image reconstruction. The proposed method is based on representing the projection data by a sum of two components. The first component contains ideal correct values, while the latter contains imperfect error values causing the ring artifact. We propose to minimize some sparsity-induced norms corresponding to the imperfect error components to effectively eliminate the ring artifact. In particular, we investigate the effect of using different sparse models, i.e. different sparsity-induced norms, on the accuracy of the ring artifact correction. The proposed cost function is optimized using an iterative algorithm derived from the alternative direction method of multipliers. Moreover, we propose improved versions of the proposed algorithms by incorporating a smoothing penalty function into the cost function. We also introduce angular constrained forms of the proposed algorithms by considering a special case as follows. The imperfect error values are constant over all the projection angles, as in the case where the source of ring artifact is the non-uniform sensitivity of the detector. Real data and simulation studies were performed to evaluate the proposed algorithms. Results demonstrate that the proposed algorithms with incorporating smoothing penalty and their angular constrained forms are effective in ring artifact elimination.
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Affiliation(s)
- Mona Selim
- Department of Computer Science, Faculty of Computers and Information, Suez University, Suez, Egypt
- Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia, Egypt
- * E-mail:
| | - Essam A. Rashed
- Graduate School of Information Science, University of Hyogo, Kobe, Japan
| | - Mohammed A. Atiea
- Department of Computer Science, Faculty of Computers and Information, Suez University, Suez, Egypt
| | - Hiroyuki Kudo
- Division of Information Engineering, Faculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba, Japan
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Li Y, Zhao Y, Ji D, Lv W, Xin X, Zhao X, Liu D, Ouyang Z, Hu C. Sparse-domain regularized stripe decomposition combined with guided-image filtering for ring artifact removal in propagation-based x-ray phase-contrast CT. Phys Med Biol 2021; 66. [PMID: 33878737 DOI: 10.1088/1361-6560/abf9de] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 04/20/2021] [Indexed: 11/11/2022]
Abstract
Propagation-based x-ray phase-contrast computed tomography (PB-PCCT) images often suffer from severe ring artifacts. Ring artifacts are mainly caused by the nonuniform response of detector elements, and they can degrade image quality and affect the subsequent image processing and quantitative analyses. To remove ring artifacts in PB-PCCT images, a novel method combined sparse-domain regularized stripe decomposition (SDRSD) method with guided image filtering (GIF) was proposed. In this method, polar coordinate transformation was utilized to convert the ring artifacts to stripe artifacts. And then considering the directional and sparse properties of the stripe artifacts and the continuity characteristics of the sample, the SDRSD method was designed to remove stripe artifacts. However, for the SDRSD method, the presence of noise may destroy the edges of the stripe artifacts and lead to incomplete decomposition. Hence, a simple and efficient smoothing technique, namely GIF, was employed to overcome this issue. The simulations and real experiments demonstrated that the proposed method could effectively remove ring artifacts as well as preserve the structures and edges of the samples. In conclusion, the proposed method can serve as an effective tool to remove ring artifacts in PB-PCCT images, and it has high potential for promoting the biomedical and preclinical applications of PB-PCCT.
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Affiliation(s)
- Yimin Li
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, 300070, People's Republic of China
| | - Yuqing Zhao
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, 300070, People's Republic of China
| | - Dongjiang Ji
- School of Science, Tianjin University of Technology and Education, Tianjin 300222, People's Republic of China
| | - Wenjuan Lv
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, 300070, People's Republic of China
| | - Xiaohong Xin
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, 300070, People's Republic of China
| | - Xinyan Zhao
- Liver Research Center, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, People's Republic of China.,Beijing Key Laboratory of Translational Medicine in Liver Cirrhosis and National Clinical Research Center of Digestive Disease, Beijing 100050, People's Republic of China
| | - Dayong Liu
- Tianjin Medical University School of Stomatology, Tianjin, 300070, People's Republic of China
| | - Zhaoguang Ouyang
- Tianjin Medical University School of Stomatology, Tianjin, 300070, People's Republic of China
| | - Chunhong Hu
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, 300070, People's Republic of China
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