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Inter-slice Complementarity Enhanced Ring Artifact Removal using Central Region Reinforced Neural Network.

Authors

Zhang Y,Liu G,Chen Z,Huang Z,Kan S,Ji X,Luo S,Zhu S,Yang J,Chen Y

Affiliations (8)

  • Southeast University, Southeast University, Nanjing, 210096, China, Nanjing, 210096, CHINA.
  • Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China, Southeast University, Nanjing, 210096, China, Nanjing, 210096, CHINA.
  • Xidian University, 266 Xinglong Section of Xifeng Road, Xi'an, Shaanxi, 710126, CHINA.
  • School of Computer Science and Engineering, Southeast University, Southeast University, Nanjing, 210096, China, Nanjing, 210096, CHINA.
  • School of Biological Science and Medical Engineering, Southeast University, Southeast University, Nanjing, 210096, China, Nanjing, Jiangsu, 210096, CHINA.
  • School of Life Science and Technology, Xidian University, Xidian University, School of Life Science and Technology Xian, CN 710071, Xian, 710071, CHINA.
  • School of Optics and Photonics, Beijing Institute of Technology, NO.5 Zhongguancun south street, Beijing, 100081, CHINA.
  • Laboratory of Image Science and Technology, Southeast University, Southeast University, Nanjing, 210096, China, nanjing, 210096, CHINA.

Abstract

In computed tomography (CT), non-uniform detector responses often lead to ring artifacts in reconstructed images. For conventional energy-integrating detectors (EIDs), such artifacts can be effectively addressed through dead-pixel correction and flat-dark field calibration. However, the response characteristics of photon-counting detectors (PCDs) are more complex, and standard calibration procedures can only partially mitigate ring artifacts. Consequently, developing high-performance ring artifact removal algorithms is essential for PCD-based CT systems. To this end, we propose the Inter-slice Complementarity Enhanced Ring Artifact Removal (ICE-RAR) algorithm. Since artifact removal in the central region is particularly challenging, ICE-RAR utilizes a dual-branch neural network that could simultaneously perform global artifact removal and enhance the central region restoration. Moreover, recognizing that the detector response is also non-uniform in the vertical direction, ICE-RAR suggests extracting and utilizing inter-slice complementarity to enhance its performance in artifact elimination and image restoration. Experiments on simulated data and two real datasets acquired from PCD-based CT systems demonstrate the effectiveness of ICE-RAR in reducing ring artifacts while preserving structural details. More importantly, since the system-specific characteristics are incorporated into the data simulation process, models trained on the simulated data can be directly applied to unseen real data from the target PCD-based CT system, demonstrating ICE-RAR's potential to address the ring artifact removal problem in practical CT systems. The implementation is publicly available at https://github.com/DarkBreakerZero/ICE-RAR.

Topics

Journal Article

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