PERSONALIZED ARTIFACTS MODELING AND FEDERATED LEARNING FOR MULTI-INSTITUTIONAL LOW-DOSE CT RECONSTRUCTION

The clinical utility of Computed Tomography (CT) is widely acknowledged, but its potential radiation risk has raised public concerns. In recent years, deep learning based methods emerged as a powerful tool for lowdose CT (LDCT) reconstruction. In a multi-institution centralized learning scenario, it often requires the centralized collection of a significant amount of data. Federated learning (FL) can be used to improve data privacy and efficiency in CT image reconstruction by enabling multiple institutions to collaborate without needing to aggregate local data. However, the statistical heterogeneity caused by different CT scanning protocols can substantially degrade the performance of FL models. Recent FL techniques tend to solve this by enhancing the generalization of the global model, but they ignore the domain-specific features, which may contain important information for local reconstruction. In our paper, we propose a personalized FL algorithm for CT image reconstruction (PerFed-LDCT). The core idea is to construct an imageartifact fusion network which is divided into two parts: a client-specific model for extracting the domain-specific artifacts, and a globally-shared model for generalized image reconstruction. Moreover, to further boost the convergence of our FL model when a domain shift is present, a weighted artifact regularization term is introduced to directly correct any deviation of local artifact estimation during optimization. The whole framework can be implemented in a distributed manner and various experiments show that our method achieved an improved performance compared to other state-of-the-art methods.