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Integration of 2D iteration and a 3D CNN-based model for multi-type artifact suppression in C-arm cone-beam CT
- Title
- Integration of 2D iteration and a 3D CNN-based model for multi-type artifact suppression in C-arm cone-beam CT
- Authors
- Choi, Dahim; Kim, Wonjin; Lee, Jiyeon; Han, Mina; Baek, Jongduk; Choi, Jang-Hwan
- Ewha Authors
- 최장환
- SCOPUS Author ID
- 최장환
- Issue Date
- 2021
- Journal Title
- MACHINE VISION AND APPLICATIONS
- ISSN
- 0932-8092
1432-1769
- Citation
- MACHINE VISION AND APPLICATIONS vol. 32, no. 6
- Keywords
- Noise reduction; CT artifact reduction; Low-dose CT; Numerical observer; Structural fidelity; Deep neural networks; C-arm cone-beam CT
- Publisher
- SPRINGER
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- Abstract
- Limiting the potential risks associated with radiation exposure is critically important when obtaining a diagnostic image. However, lowering the level of radiation may cause excessive noise and artifacts in computed tomography (CT) scans. In this study, we implemented and tested the performance of patch-based and block-based REDCNN models and revealed that a 3D kernel is efficient in removing 3D noise and artifacts. Additionally, we applied a 3D bilateral filter and a 2D-based Landweber iteration method to remove any remaining noise and to prevent the edges from blurring, which are limitations of a deep learning-based noise reduction system. For the 2D-based Landweber iteration, we examined the requisite step size and the number of iterations. The representative CT noise and artifacts, which were Gaussian noise and view aliasing artifacts, respectively, were simulated on XCAT and reproduced in vivo to verify that the proposed method could be used in an analogous clinical setting. Lastly, the performance of the proposed algorithm was evaluated on in vivo data with real low-dose noise. Our proposed method effectively suppressed complex noise without losing diagnostic features in both the simulation study and experimental evaluation. Furthermore, for the simulation study, we adopted a numerical observer model to evaluate the structural fidelity of the image quality more appropriately than existing image quality assessment methods.
- DOI
- 10.1007/s00138-021-01240-3
- Appears in Collections:
- 인공지능대학 > 인공지능학과 > Journal papers
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