【摘要】 不同剂量水平的正电子发射断层扫描(PET)图像存在显著差异,导致模型性能严重下滑。目前的多剂量PET图像合成方法主要关注提取不同剂量PET图像的差异性信息以及空间域特征,而忽略了它们之间的共享信息和频域特征信息。为了解决这一问题,本文提出了一种基于双域信息自适应学习的多剂量PET图像合成方法(SFMdose)。旨在设计一种统一的模型,同时提取不同剂量的PET图像之间共享知识和判别性信息。此外,本文设计了一个空间域信息引导模块,用以学习剂量相关指令,自适应地选择传播路由,抑制潜在的剂量干扰问题。同时为了学习频域相关剂量分布信息,提出了频域信息引导模块,使模型能够更好地学习不同剂量图像的特征,提高图像合成质量。本文在UDPET数据集和真实人脑数据集上进行的系列实验表明,该方法在多剂量水平合成高质量PET图像方面展现出显著的性能优势。更多还原

【Abstract】 Significant variations exist among positron emission tomography(PET) images acquired at different dose levels,leading to severe performance degradation in deep learning models.Current multidose PET image synthesis methods primarily focus on extracting differential information and spatialdomain features from varying-dose PET images,while neglecting their shared information and frequencydomain features.To address these limitations,we propose a dual-domain information adaptive learning approach for multi-dose PET image synthesis(SFMdose).The proposed method aims to develop a unified model capable of simultaneously extracting both shared knowledge and discriminative information across PET images with different doses.Moreover,we design a spatial-domain information guidance module to learn dose-related patterns and adaptively selects propagation routes to suppress dose-specific interference.Additionally,a frequency-domain guidance module is proposed to capture dose distribution characteristics in the frequency domain,enabling the model to better learn features from images with different doses and improve synthesis quality.Extensive experiments on both the UDPET dataset and real human brain datasets demonstrate that our SFMdose method achieves superior performance in synthesizing highquality PET images across multiple dose levels.更多还原