Abstract: 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 multi-protocol 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.

Key words: computed tomography, X-ray; low-dose; federated learning; image reconstruction