Integration of 2D iteration and a 3D CNN-based model for multi-type artifact suppression in C-arm cone-beam CT
- 주제(키워드) Noise reduction , CT artifact reduction , Low-dose CT , Numerical observer , Structural fidelity , Deep neural networks , C-arm cone-beam CT
- 주제(기타) Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Engineering, Electrical & Electronic
- 설명문(일반) [Choi, Dahim] Georgia Inst Technol, Wallace H Coulter Dept Biomed Engn, Atlanta, GA 30332 USA; [Choi, Dahim] Emory Univ, Atlanta, GA 30332 USA; [Kim, Wonjin; Lee, Jiyeon; Choi, Jang-Hwan] Ewha Womans Univ, Div Mech & Biomed Engn, Grad Program Syst Hlth Sci & Engn, Seoul 03760, South Korea; [Han, Mina; Baek, Jongduk] Yonsei Univ, Sch Integrated Technol, Incheon 406840, South Korea
- 관리정보기술 faculty
- 등재 SCIE, SCOPUS
- 발행기관 SPRINGER
- 발행년도 2021
- 세부유형 Article
- URI http://www.dcollection.net/handler/ewha/000000183552
- 본문언어 영어
- Published As http://dx.doi.org/10.1007/s00138-021-01240-3
초록/요약
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.
more