Fed-NDIF：一种用于低计数全身PET去噪的嵌入噪声的联邦扩散模型
Fed-NDIF: A Noise-Embedded Federated Diffusion Model For Low-Count Whole-Body PET Denoising
AI总结：
针对低计数PET去噪的数据稀缺与隐私问题，提出嵌入肝脏NSTD噪声的联邦扩散模型Fed-NDIF，结合FedAvg聚合与本地微调，多中心验证显示其在图像质量与病灶量化上优于现有方法。
摘要：
低计数正电子发射断层扫描（LCPET）成像可减少患者的辐射暴露，但往往存在图像噪声增加、病灶可检测性下降的问题，因此需要有效的去噪技术。扩散模型在LCPET去噪、恢复受损图像质量方面已展现出应用前景。然而，训练这类模型需要大规模、多样化的数据集，这在医学领域难以实现。为解决数据稀缺和隐私保护问题，我们将扩散模型与联邦学习相结合——联邦学习是一种去中心化训练方法，模型在不同站点分别训练，其参数经多轮迭代后在中央服务器聚合。机构内部及机构间扫描仪类型的差异、图像噪声水平的不同，给LCPET去噪任务中的联邦学习带来了额外挑战。本研究中，我们提出一种新型嵌入噪声的联邦学习扩散模型Fed-NDIF，利用多中心数据集和不同计数水平来应对上述挑战。我们的方法将肝脏归一化标准差（NSTD）噪声嵌入2.5D扩散模型中，并采用联邦平均（FedAvg）算法将本地训练的模型聚合为全局模型，随后在本地数据集上微调全局模型以优化性能，得到个性化模型。我们在伯尔尼大学、上海瑞金医院、耶鲁-纽黑文医院的数据集上开展了广泛验证，结果表明本方法在提升图像质量、改善病灶量化方面性能更优。与本地扩散模型、基于联邦UNet的模型相比，Fed-NDIF模型在整个3D体数据的PSNR、SSIM、NMSE指标上均有显著提升，同时增强了病灶可检测性与量化效果。
英文摘要：
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.
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