  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.
