MPGAN: Multi Pareto Generative Adversarial Network for the denoising and quantitative analysis of low-dose PET images of human brain

IF 10.7 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Medical image analysis Pub Date : 2024-08-17 DOI:10.1016/j.media.2024.103306
Yu Fu , Shunjie Dong , Yanyan Huang , Meng Niu , Chao Ni , Lequan Yu , Kuangyu Shi , Zhijun Yao , Cheng Zhuo
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Abstract

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 (GDmc) and the dynamic Pareto-efficient discriminator (DPed), both of which play a zero-sum game for n(n1,2,3) rounds to ensure the quality of synthesized F-PET images. The Pareto-efficient dynamic discrimination process is introduced in DPed 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.

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MPGAN:用于人脑低剂量 PET 图像去噪和定量分析的多帕累托生成对抗网络。
正电子发射断层扫描(PET)成像在医学成像中被广泛用于分析神经系统疾病和相关脑部疾病。通常情况下,全剂量 PET 成像可确保图像质量,但会引发辐射对健康的潜在风险。将低剂量 PET(L-PET)图像重建为与全剂量 PET(F-PET)相同的高质量图像,可以有效解决减少辐射暴露与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),以实现人脑 L-PET 图像的三维端到端去噪。MPGAN 由两个关键模块组成:扩散多轮级联生成器(GDmc)和动态帕累托效率判别器(DPed),这两个模块在 n(n∈1,2,3)轮中进行零和博弈,以确保合成的 F-PET 图像的质量。DPed 引入了帕累托效率动态判别过程,以自适应地调整子判别器的权重,从而提高判别输出。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 图像的三维端到端去噪提供了有效的解决方案,符合临床标准,并在常用指标上达到了最先进的性能。
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
期刊最新文献
Corrigendum to "Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge" [Medical Image Analysis, April 2022, Volume 77, 102333]. Editorial for Special Issue on Foundation Models for Medical Image Analysis. Few-shot medical image segmentation with high-fidelity prototypes. The Developing Human Connectome Project: A fast deep learning-based pipeline for neonatal cortical surface reconstruction. AutoFOX: An automated cross-modal 3D fusion framework of coronary X-ray angiography and OCT.
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