Integration of 2D iteration and a 3D CNN-based model for multi-type artifact suppression in C-arm cone-beam CT

Dahim Choi, Wonjin Kim, Jiyeon Lee, Mina Han, Jongduk Baek, Jang Hwan Choi

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number116
JournalMachine Vision and Applications
Volume32
Issue number6
DOIs
StatePublished - Nov 2021

Keywords

  • C-arm cone-beam CT
  • CT artifact reduction
  • Deep neural networks
  • Low-dose CT
  • Noise reduction
  • Numerical observer
  • Structural fidelity

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