Most existing deep learning-based PET image denoising methods assume a fixed and known dose reduction factor (DRF) for low-dose PET images. However, these methods encounter significant performance degradation when the DRF varies beyond the assumed one in practical applications. To address the challenge posed by varied DRFs, several preliminary studies focus on the task of universal PET image denoising, aiming to train a universal model over low-dose data across DRFs. Nonetheless, these vanilla universal models often struggle with misaligned styles present in different DRF data, leading to the \textit{style elimination issue} with a significant over-smoothing effect. To deal with this issue, we innovatively introduce domain generalization to PET image denoising and propose a universal PET image denoising network (UniPET) to achieve high-quality PET image denoising across diverse DRFs. UniPET comprises two primary innovations: a style alignment network (SAN) and a region-aware learning strategy (RALS). Specifically, SAN utilizes style alignment techniques derived from domain generalization to align and recover styles across different DRFs, ensuring the model's generalizability across various DRFs while effectively preserving styles. Furthermore, to enhance style recovery, RALS distinguishes between flat and stylized regions, exclusively conducting adversarial learning on the latter, thereby more effectively guiding the model's focus towards learning stylized regions. It is demonstrated that our proposed UniPET can adaptively recover different DRF styles and achieve high-quality PET image denoising across DRFs. Comprehensive experiments show that UniPET exhibits comparable performance to individual DRF-specific models at specific DRFs and realizes state-of-the-art performance in universal PET image denoising quantitatively, perceptually, and clinically.

翻译：大多数现有的基于深度学习的PET图像去噪方法假设低剂量PET图像具有固定且已知的降剂量因子（DRF）。然而，在实际应用中，当DRF偏离假设值时，这些方法会出现显著的性能下降。为应对DRF变化带来的挑战，一些初步研究聚焦于通用PET图像去噪任务，旨在训练一个能够处理不同DRF低剂量数据的通用模型。然而，这些基础通用模型常常难以适应不同DRF数据中的风格错配问题，导致出现“风格消除问题”，并伴有严重的过平滑效应。针对这一问题，我们创新性地将域泛化引入PET图像去噪领域，并提出了一种通用PET图像去噪网络（UniPET），以实现跨多种DRF的高质量PET图像去噪。UniPET包含两大核心创新：风格对齐网络（SAN）与区域感知学习策略（RALS）。具体而言，SAN利用源自域泛化的风格对齐技术，对齐并恢复不同DRF数据的风格，从而在保证模型跨DRF泛化能力的同时，有效保留风格特征。此外，为增强风格恢复效果，RALS将图像区分为平坦区域与风格化区域，并仅在风格化区域上开展对抗学习，从而更有效引导模型聚焦于学习风格化区域。实验证明，我们提出的UniPET能够自适应恢复不同DRF风格，并实现跨DRF的高质量PET图像去噪。全面实验表明，UniPET在特定DRF下展现出与专用DRF模型相当的性能，并在通用PET图像去噪的定量、感知及临床评估中达到目前最优水平。