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Zhu M, Zhu Q, Song Y, Guo Y, Zeng D, Bian Z, Wang Y, Ma J. Physics-informed sinogram completion for metal artifact reduction in CT imaging. Phys Med Biol 2023; 68. [PMID: 36808913 DOI: 10.1088/1361-6560/acbddf] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 02/21/2023] [Indexed: 02/23/2023]
Abstract
Objective.Metal artifacts in the computed tomography (CT) imaging are unavoidably adverse to the clinical diagnosis and treatment outcomes. Most metal artifact reduction (MAR) methods easily result in the over-smoothing problem and loss of structure details near the metal implants, especially for these metal implants with irregular elongated shapes. To address this problem, we present the physics-informed sinogram completion (PISC) method for MAR in CT imaging, to reduce metal artifacts and recover more structural textures.Approach.Specifically, the original uncorrected sinogram is firstly completed by the normalized linear interpolation algorithm to reduce metal artifacts. Simultaneously, the uncorrected sinogram is also corrected based on the beam-hardening correction physical model, to recover the latent structure information in metal trajectory region by leveraging the attenuation characteristics of different materials. Both corrected sinograms are fused with the pixel-wise adaptive weights, which are manually designed according to the shape and material information of metal implants. To furtherly reduce artifacts and improve the CT image quality, a post-processing frequency split algorithm is adopted to yield the final corrected CT image after reconstructing the fused sinogram.Main results.We qualitatively and quantitatively evaluated the presented PISC method on two simulated datasets and three real datasets. All results demonstrate that the presented PISC method can effectively correct the metal implants with various shapes and materials, in terms of artifact suppression and structure preservation.Significance.We proposed a sinogram-domain MAR method to compensate for the over-smoothing problem existing in most MAR methods by taking advantage of the physical prior knowledge, which has the potential to improve the performance of the deep learning based MAR approaches.
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Affiliation(s)
- Manman Zhu
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China.,Pazhou Lab (Huangpu), Guangzhou 510700, People's Republic of China
| | - Qisen Zhu
- Pazhou Lab (Huangpu), Guangzhou 510700, People's Republic of China
| | - Yuyan Song
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China.,Pazhou Lab (Huangpu), Guangzhou 510700, People's Republic of China
| | - Yi Guo
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China.,Pazhou Lab (Huangpu), Guangzhou 510700, People's Republic of China
| | - Dong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China.,Pazhou Lab (Huangpu), Guangzhou 510700, People's Republic of China
| | - Zhaoying Bian
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China.,Pazhou Lab (Huangpu), Guangzhou 510700, People's Republic of China
| | - Yongbo Wang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China.,Pazhou Lab (Huangpu), Guangzhou 510700, People's Republic of China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China.,Pazhou Lab (Huangpu), Guangzhou 510700, People's Republic of China
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Zhu Q, Wang Y, Zhu M, Tao X, Bian Z, Ma J. [An adaptive CT metal artifact reduction algorithm that combines projection interpolation and physical correction]. Nan Fang Yi Ke Da Xue Xue Bao 2022; 42:832-839. [PMID: 35790433 DOI: 10.12122/j.issn.1673-4254.2022.06.06] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
OBJECTIVE To propose an adaptive weighted CT metal artifact reduce algorithm that combines projection interpolation and physical correction. METHODS A normalized metal projection interpolation algorithm was used to obtain the initial corrected projection data. A metal physical correction model was then introduced to obtain the physically corrected projection data. To verify the effectiveness of the method, we conducted experiments using simulation data and clinical data. For the simulation data, the quantitative indicators PSNR and SSIM were used for evaluation, while for the clinical data, the resultant images were evaluated by imaging experts to compare the artifact-reducing performance of different methods. RESULTS For the simulation data, the proposed method improved the PSNR value by at least 0.2 dB and resulted in the highest SSIM value among the methods for comparison. The experiment with the clinical data showed that the imaging experts gave the highest scores of 3.616±0.338 (in a 5-point scale) to the images processed using the proposed method, which had significant better artifact-reducing performance than the other methods (P < 0.001). CONCLUSION The metal artifact reduction algorithm proposed herein can effectively reduce metal artifacts while preserving the tissue structure information and reducing the generation of new artifacts.
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Affiliation(s)
- Q Zhu
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.,Pazhou Lab, Guangzhou 510330, China
| | - Y Wang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.,Pazhou Lab, Guangzhou 510330, China
| | - M Zhu
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.,Pazhou Lab, Guangzhou 510330, China
| | - X Tao
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.,Pazhou Lab, Guangzhou 510330, China
| | - Z Bian
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - J Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
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