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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, DahimKim, WonjinLee, JiyeonHan, MinaBaek, JongdukChoi, Jang-Hwan
Ewha Authors
최장환
SCOPUS Author ID
최장환scopus
Issue Date
2021
Journal Title
MACHINE VISION AND APPLICATIONS
ISSN
0932-8092JCR Link

1432-1769JCR Link
Citation
MACHINE VISION AND APPLICATIONS vol. 32, no. 6
Keywords
Noise reductionCT artifact reductionLow-dose CTNumerical observerStructural fidelityDeep neural networksC-arm cone-beam CT
Publisher
SPRINGER
Indexed
SCIE; SCOPUS WOS 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:
엘텍공과대학 > 휴먼기계바이오공학부 > Journal papers
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