FedFTN：面向多机构低计数PET去噪的基于深度特征变换网络的个性化联邦学习
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising
AI总结：
针对多机构低计数PET去噪的域偏移与数据隐私问题，提出个性化联邦学习方法FedFTN，通过本地特征变换网络适配全局共享去噪模型，在跨洲多中心数据集上全面优于基线方法。
摘要：
低计数PET是减少辐射暴露和采集时间的有效途径，但重建后的图像通常信噪比（SNR）较低，进而影响诊断及其他下游任务。近年来深度学习的发展在提升低计数PET图像质量方面展现出巨大潜力，但由于患者数据的隐私和安全顾虑，从多家机构获取大规模、集中式且多样化的数据集以训练鲁棒模型十分困难。此外，不同机构的低计数PET数据可能存在不同的数据分布，因此需要个性化模型。尽管此前的联邦学习（FL）算法无需聚合本地数据即可实现多机构协作训练，但在多机构低计数PET去噪应用中应对大幅域偏移仍是一项挑战，且相关研究仍非常不足。在本研究中，我们提出了FedFTN，一种可应对上述挑战的个性化联邦学习策略。FedFTN利用本地深度特征变换网络（FTN）对全局共享去噪网络的特征输出进行调制，为每家机构实现个性化的低计数PET去噪。在联邦学习过程中，仅传输和聚合去噪网络的权重，而FTN保留在本地机构执行特征变换。我们利用来自三大洲三家医疗中心的大规模多机构低计数PET成像数据集评估了所提方法，结果表明FedFTN可生成高质量的低计数PET图像，在三家机构的所有低计数水平下，性能均优于此前的基线FL重建方法。
英文摘要：
Low-count PET is an efficient way to reduce radiation exposure and acquisition time, but the reconstructed images often suffer from low signal-to-noise ratio (SNR), thus affecting diagnosis and other downstream tasks. Recent advances in deep learning have shown great potential in improving low-count PET image quality, but acquiring a large, centralized, and diverse dataset from multiple institutions for training a robust model is difficult due to privacy and security concerns of patient data. Moreover, low-count PET data at different institutions may have different data distribution, thus requiring personalized models. While previous federated learning (FL) algorithms enable multi-institution collaborative training without the need of aggregating local data, addressing the large domain shift in the application of multi-institutional low-count PET denoising remains a challenge and is still highly under-explored. In this work, we propose FedFTN, a personalized federated learning strategy that addresses these challenges. FedFTN uses a local deep feature transformation network (FTN) to modulate the feature outputs of a globally shared denoising network, enabling personalized low-count PET denoising for each institution. During the federated learning process, only the denoising network's weights are communicated and aggregated, while the FTN remains at the local institutions for feature transformation. We evaluated our method using a large-scale dataset of multi-institutional low-count PET imaging data from three medical centers located across three continents, and showed that FedFTN provides high-quality low-count PET images, outperforming previous baseline FL reconstruction methods across all low-count levels at all three institutions.
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