UniPET：一種通用網絡，可在不同劑量減少因子下實現高質量PET影像去噪

摘要

现有的大多数基于深度学习的PET图像去噪方法假设低剂量PET图像具有固定且已知的剂量缩减因子（DRF）。然而，在实际应用中，当DRF超出预设范围时，这些方法会出现显著的性能下降。为应对不同DRF带来的挑战，一些初步研究聚焦于通用PET图像去噪任务，旨在训练一个能够跨DRF处理低剂量数据的通用模型。然而，这类朴素通用模型往往难以处理不同DRF数据中存在的风格不匹配问题，从而导致风格消除现象及显著的过度平滑效应。为解决这一问题，我们创新性地将域泛化引入PET图像去噪领域，并提出一种通用PET图像去噪网络（UniPET），以实现跨不同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）和区域感知学习策略（RALS）。具体而言，SAN利用源自域泛化的风格对齐技术，对不同DRF下的风格进行对齐与恢复，在有效保留风格的同时确保模型在多种DRF下的泛化能力。此外，为增强风格恢复效果，RALS区分平坦区域与风格化区域，并仅对后者进行对抗学习，从而更有效地引导模型聚焦于风格化区域的学习。实验证明，我们提出的UniPET能够自适应地恢复不同DRF风格，实现跨DRF的高质量PET图像去噪。大量实验表明，UniPET在特定DRF下展现出与单DRF专属模型相当的性能，并在定量、感知和临床评估中均达到了通用PET图像去噪的最新水平。

English

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 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.