Positron emission tomography (PET) imaging is widely used in medical imaging for analyzing neurological disorders and related brain diseases. Usually, full-dose imaging for PET ensures image quality but raises concerns about potential health risks of radiation exposure. The contradiction between reducing radiation exposure and maintaining diagnostic performance can be effectively addressed by reconstructing low-dose PET (L-PET) images to the same high-quality as full-dose (F-PET). This paper introduces the Multi Pareto Generative Adversarial Network (MPGAN) to achieve 3D end-to-end denoising for the L-PET images of human brain. MPGAN consists of two key modules: the diffused multi-round cascade generator () and the dynamic Pareto-efficient discriminator (), both of which play a zero-sum game for rounds to ensure the quality of synthesized F-PET images. The Pareto-efficient dynamic discrimination process is introduced in to adaptively adjust the weights of sub-discriminators for improved discrimination output. We validated the performance of MPGAN using three datasets, including two independent datasets and one mixed dataset, and compared it with 12 recent competing models. Experimental results indicate that the proposed MPGAN provides an effective solution for 3D end-to-end denoising of L-PET images of the human brain, which meets clinical standards and achieves state-of-the-art performance on commonly used metrics.

中文翻译：

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MPGAN：多帕累托生成对抗网络，用于人脑低剂量 PET 图像的去噪和定量分析


正电子发射断层扫描 (PET) 成像广泛应用于医学成像，用于分析神经系统疾病和相关脑部疾病。通常，PET 的全剂量成像可以确保图像质量，但会引起人们对辐射暴露潜在健康风险的担忧。通过将低剂量 PET (L-PET) 图像重建为与全剂量 (F-PET) 相同的高质量，可以有效解决减少辐射暴露和保持诊断性能之间的矛盾。本文介绍了多帕累​​托生成对抗网络（MPGAN）来实现人脑 L-PET 图像的 3D 端到端去噪。 MPGAN由两个关键模块组成：扩散多轮级联生成器（）和动态帕累托有效判别器（），两者都进行轮次零和博弈，以保证合成的F-PET图像的质量。引入帕累托有效动态判别过程来自适应调整子判别器的权重，以提高判别输出。我们使用三个数据集（包括两个独立数据集和一个混合数据集）验证了 MPGAN 的性能，并将其与 12 个最新的竞争模型进行了比较。实验结果表明，所提出的 MPGAN 为人脑 L-PET 图像的 3D 端到端去噪提供了有效的解决方案，满足临床标准并在常用指标上实现了最先进的性能。