南方医科大学学报2025，Vol.45Issue(4)：844-852,9.DOI:10.12122/j.issn.1673-4254.2025.04.20
基于中心指导与交替优化的低剂量CT图像恢复方法
A low-dose CT image restoration method based on central guidance and alternating optimization
摘要
Abstract
Objective 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.关键词
计算机断层成像/联邦学习/图像恢复/数据异质性Key words
computed tomography/federated learning/image restoration/data heterogeneity引用本文复制引用
张晓瑜,王昊,曾栋,边兆英..基于中心指导与交替优化的低剂量CT图像恢复方法[J].南方医科大学学报,2025,45(4):844-852,9.基金项目
国家自然科学基金(U21A6005) Supported by National Natural Science Fundation of China(U21A6005). （U21A6005）
