A low-dose CT image restoration method based on central guidance and alternating optimization.
10.12122/j.issn.1673-4254.2025.04.20
- Author:
Xiaoyu ZHANG
1
;
Hao WANG
1
;
Dong ZENG
1
;
Zhaoying BIAN
1
Author Information
1. School of Biomedical Engineering, Southern Medical University/ Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou 510515, China.
- Publication Type:Journal Article
- Keywords:
computed tomography;
data heterogeneity;
federated learning;
image restoration
- MeSH:
Tomography, X-Ray Computed/methods*;
Humans;
Radiation Dosage;
Image Processing, Computer-Assisted/methods*;
Algorithms
- From:
Journal of Southern Medical University
2025;45(4):844-852
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVES:We propose a low-dose CT image restoration method based on central guidance and alternating optimization (FedGP).
METHODS:The FedGP framework revolutionizes the traditional federated learning model by adopting a structure without a fixed central server, where each institution alternatively serves as the central server. This method uses an institution-modulated CT image restoration network as the core of client-side local training. Through a federated learning approach of central guidance and alternating optimization, the central server leverages local labeled data to guide client-side network training to enhance the generalization capability of the CT imaging model across multiple institutions.
RESULTS:In the low-dose and sparse-view CT image restoration tasks, the FedGP method showed significant advantages in both visual and quantitative evaluation and achieved the highest PSNR (40.25 and 38.84), the highest SSIM (0.95 and 0.92), and the lowest RMSE (2.39 and 2.56). Ablation study of FedGP demonstrated that compared with FedGP(w/o GP) without central guidance, the FedGP method better adapted to data heterogeneity across institutions, thus ensuring robustness and generalization capability of the model in different imaging conditions.
CONCLUSIONS:FedGP provides a more flexible FL framework to solve the problem of CT imaging heterogeneity and well adapts to multi-institutional data characteristics to improve generalization ability of the model under diverse imaging geometric configurations.
