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Updated: Jan 16, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Inter-slice complementarity enhanced ring artifact removal using central region reinforced neural network.

Yikun Zhang^1,2, Guannan Liu^1,2, Zhanghao Chen^1,2

¹Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing 210096, People's Republic of China.

Physics in Medicine and Biology

|September 30, 2025

Summary

Photon-counting detector computed tomography (CT) systems face ring artifacts due to complex detector responses. The novel inter-slice complementarity enhanced ring artifact removal (ICE-RAR) algorithm effectively reduces these artifacts, improving image quality.

Keywords:

central region reinforced neural network computed tomography deep learning inter-slice complementarity ring artifact removal

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Area of Science:

Medical Imaging
Computational Imaging
Photon-Counting Detectors

Background:

Non-uniform detector responses in computed tomography (CT) cause ring artifacts.
Standard calibration methods are insufficient for photon-counting detectors (PCDs).
Effective ring artifact removal (RAR) is crucial for PCD-based CT systems.

Purpose of the Study:

To develop a high-performance RAR algorithm for PCD-based CT.
To address challenges in central region artifact removal.
To leverage inter-slice information for enhanced artifact elimination.

Main Methods:

Proposed the inter-slice complementarity enhanced RAR (ICE-RAR) algorithm.
Utilized a dual-branch neural network for global and central region restoration.
Incorporated inter-slice complementarity to address vertical detector non-uniformity.

Main Results:

ICE-RAR effectively reduced ring artifacts in simulated and real PCD CT datasets.
The algorithm preserved structural details in reconstructed images.
Models trained on simulated data generalized to real-world PCD CT data.

Conclusions:

ICE-RAR demonstrates significant potential for practical ring artifact removal in PCD-based CT systems.
The method shows effectiveness in both artifact reduction and image detail preservation.
The algorithm's ability to generalize from simulated to real data highlights its practical applicability.