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Low-count PET is an efficient way to reduce radiation exposure andacquisition time, but the reconstructed images often suffer from lowsignal-to-noise ratio (SNR), thus affecting diagnosis and other downstreamtasks. Recent advances in deep learning have shown great potential in improvinglow-count PET image quality, but acquiring a large, centralized, and diversedataset from multiple institutions for training a robust model is difficult dueto privacy and security concerns of patient data. Moreover, low-count PET dataat different institutions may have different data distribution, thus requiringpersonalized models. While previous federated learning (FL) algorithms enablemulti-institution collaborative training without the need of aggregating localdata, addressing the large domain shift in the application ofmulti-institutional low-count PET denoising remains a challenge and is stillhighly under-explored. In this work, we propose FedFTN, a personalizedfederated learning strategy that addresses these challenges. FedFTN uses alocal deep feature transformation network (FTN) to modulate the feature outputsof a globally shared denoising network, enabling personalized low-count PETdenoising for each institution. During the federated learning process, only thedenoising network's weights are communicated and aggregated, while the FTNremains at the local institutions for feature transformation. We evaluated ourmethod using a large-scale dataset of multi-institutional low-count PET imagingdata from three medical centers located across three continents, and showedthat FedFTN provides high-quality low-count PET images, outperforming previousbaseline FL reconstruction methods across all low-count levels at all threeinstitutions.

FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising