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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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Yao Y, Li L, Chen Z. Iterative dynamic dual-energy CT algorithm in reducing statistical noise in multi-energy CT imaging. Phys Med Biol 2021; 67. [PMID: 34937002 DOI: 10.1088/1361-6560/ac459d] [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: 10/09/2021] [Accepted: 12/22/2021] [Indexed: 11/11/2022]
Abstract
Multi-energy spectral CT has a broader range of applications with the recent development of photon-counting detectors. However, the photons counted in each energy bin decrease when the number of energy bins increases, which causes a higher statistical noise level of the CT image. In this work, we propose a novel iterative dynamic dual-energy CT algorithm to reduce the statistical noise. In the proposed algorithm, the multi-energy projections are estimated from the dynamic dual-energy CT data during the iterative process. The proposed algorithm is verified on sufficient numerical simulations and a laboratory two-energy-threshold PCD system. By applying the same reconstruction algorithm, the dynamic dual-energy CT's final reconstruction results have a much lower statistical noise level than the conventional multi-energy CT. Moreover, based on the analysis of the simulation results, we explain why the dynamic dual-energy CT has a lower statistical noise level than the conventional multi-energy CT. The reason is that: the statistical noise level of multi-energy projection estimated with the proposed algorithm is much lower than that of the conventional multi-energy CT, which leads to less statistical noise of the dynamic dual-energy CT imaging.
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Affiliation(s)
- Yidi Yao
- Department of Engineering Physics, Tsinghua University, 30 Shuangqing Rd, Hai Dian Qu, Beijing, 100084, CHINA
| | - Liang Li
- Department of Engineering Physics, Tsinghua University, 30 Shuangqing Rd, Hai Dian Qu, Beijing, 100084, CHINA
| | - Zhiqiang Chen
- Department of Engineering Physics, Tsinghua University, 30 Shuangqing Rd, Hai Dian Qu, Beijing, 100084, CHINA
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He J, Chen S, Zhang H, Tao X, Lin W, Zhang S, Zeng D, Ma J. Downsampled Imaging Geometric Modeling for Accurate CT Reconstruction via Deep Learning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:2976-2985. [PMID: 33881992 DOI: 10.1109/tmi.2021.3074783] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
X-ray computed tomography (CT) is widely used clinically to diagnose a variety of diseases by reconstructing the tomographic images of a living subject using penetrating X-rays. For accurate CT image reconstruction, a precise imaging geometric model for the radiation attenuation process is usually required to solve the inversion problem of CT scanning, which encodes the subject into a set of intermediate representations in different angular positions. Here, we show that accurate CT image reconstruction can be subsequently achieved by downsampled imaging geometric modeling via deep-learning techniques. Specifically, we first propose a downsampled imaging geometric modeling approach for the data acquisition process and then incorporate it into a hierarchical neural network, which simultaneously combines both geometric modeling knowledge of the CT imaging system and prior knowledge gained from a data-driven training process for accurate CT image reconstruction. The proposed neural network is denoted as DSigNet, i.e., downsampled-imaging-geometry-based network for CT image reconstruction. We demonstrate the feasibility of the proposed DSigNet for accurate CT image reconstruction with clinical patient data. In addition to improving the CT image quality, the proposed DSigNet might help reduce the computational complexity and accelerate the reconstruction speed for modern CT imaging systems.
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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: 17] [Impact Index Per Article: 5.7] [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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Abstract
The introduction of photon-counting detectors is expected to be the next major breakthrough in clinical x-ray computed tomography (CT). During the last decade, there has been considerable research activity in the field of photon-counting CT, in terms of both hardware development and theoretical understanding of the factors affecting image quality. In this article, we review the recent progress in this field with the intent of highlighting the relationship between detector design considerations and the resulting image quality. We discuss detector design choices such as converter material, pixel size, and readout electronics design, and then elucidate their impact on detector performance in terms of dose efficiency, spatial resolution, and energy resolution. Furthermore, we give an overview of data processing, reconstruction methods and metrics of imaging performance; outline clinical applications; and discuss potential future developments.
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Affiliation(s)
- Mats Danielsson
- Department of Physics, KTH Royal Institute of Technology, AlbaNova University Center, SE-106 91 Stockholm, Sweden. Prismatic Sensors AB, AlbaNova University Center, SE-106 91 Stockholm, Sweden
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