基于联邦特征学习的多机型低剂量CT重建算法

A low-dose CT reconstruction algorithm across different scanners based on federated feature learning

目的提出一种基于联邦特征学习的多机型低剂量CT重建算法(FedCT),以提升深度学习模型对多CT机型的泛化能力并保护数据隐私。方法FedCT框架在每个协作学习的客户端中设置了一个基于数据-解析模型耦合驱动的Radon反变换智能重建模型作为局部网络模型,并采用投影域特异性学习策略,在局部投影域保留成像几何特异性。同时,引入联邦特征学习,使用条件特征参数标记局部数据并馈入网络模型进行编码以在图像域提升网络模型的泛化性。结果在跨站点的多机型、多协议低剂量CT重建实验中,FedCT的重建结果在所有对比联邦学习方法中获得了最高的PSNR(高于次优的联邦学习方法+2.8048、+2.7301、+2.7263)、最高的SSIM指标(高于次优的联邦学习方法+0.0009、+0.0165、+0.0131)以及最低的RMSE指标(低于次优的对比联邦学习方法-0.6687、-1.5956、-0.9962)。在消融实验中,相较于一般联邦学习策略,采用投影特异学习策略的模型在测试集上的PSNR指标的Q1平均提升1.18,RMSE指标的Q3平均降低1.36。在引入联邦特征学习后,FedCT在测试集上的PSNR指标的Q1进一步提升3.56,RMSE指标的Q3进一步降低1.80。结论FedCT为协作构建CT智能重建网络模型提供了有效解决方案,能够在保护数据隐私的基础上,增强网络模型泛化性,进一步地提升网络模型在全局数据上的重建性能。

Objective To propose a low-dose CT reconstruction algorithm across different scanners based on federated feature learning(FedCT)to improve the generalization of deep learning models for multiple CT scanners and protect data privacy.Methods In the proposed FedCT framework,each client is assigned an inverse Radon transform-based reconstruction model to serve as a local network model that participates in federated learning.A projection-domain specific learning strategy is adopted to preserve the geometry specificity in the local projection domain.Federated feature learning is introduced in the model,which utilizes conditional parameters to mark the local data and feed the conditional parameters into the network for encoding to enhance the generalization of the model in the image domain.Results In the cross-client,multi-scanner,and multiprotocol low-dose CT reconstruction experiments,FedCT achieved the highest PSNR(+2.8048,+2.7301,and+2.7263 compared to the second best federated learning method),the highest SSIM(+0.0009,+0.0165,and+0.0131 in the same comparison),and the lowest RMSE(-0.6687,-1.5956,and-0.9962).In the ablation experiment,compared with the general federated learning strategy,the model with projection-specific learning strategy showed an average improvement by 1.18 on Q1 of the PSNR and an average decrease by 1.36 on Q3 of the RMSE on the test set.The introduction of federated feature learning in FedCT further improved the Q1 of the PSNR on the test set by 3.56 and reduced the Q3 of the RMSE by 1.80.Conclusion FedCT provides an effective solution for collaborative construction of CT reconstruction models,which can enhance model generalization and further improve the reconstruction performance on global data while protecting data privacy.