Fed-NDIF: A Noise-Embedded Federated Diffusion Model For Low-Count Whole-Body PET Denoising
Journal:
arXiv
Published Date:
Mar 20, 2025
Abstract
Low-count positron emission tomography (LCPET) imaging can reduce patients'
exposure to radiation but often suffers from increased image noise and reduced
lesion detectability, necessitating effective denoising techniques. Diffusion
models have shown promise in LCPET denoising for recovering degraded image
quality. However, training such models requires large and diverse datasets,
which are challenging to obtain in the medical domain. To address data scarcity
and privacy concerns, we combine diffusion models with federated learning -- a
decentralized training approach where models are trained individually at
different sites, and their parameters are aggregated on a central server over
multiple iterations. The variation in scanner types and image noise levels
within and across institutions poses additional challenges for federated
learning in LCPET denoising. In this study, we propose a novel noise-embedded
federated learning diffusion model (Fed-NDIF) to address these challenges,
leveraging a multicenter dataset and varying count levels. Our approach
incorporates liver normalized standard deviation (NSTD) noise embedding into a
2.5D diffusion model and utilizes the Federated Averaging (FedAvg) algorithm to
aggregate locally trained models into a global model, which is subsequently
fine-tuned on local datasets to optimize performance and obtain personalized
models. Extensive validation on datasets from the University of Bern, Ruijin
Hospital in Shanghai, and Yale-New Haven Hospital demonstrates the superior
performance of our method in enhancing image quality and improving lesion
quantification. The Fed-NDIF model shows significant improvements in PSNR,
SSIM, and NMSE of the entire 3D volume, as well as enhanced lesion
detectability and quantification, compared to local diffusion models and
federated UNet-based models.
