AgGAN:解剖导向生成对抗网络合成动脉自旋标记图像用于模拟微重力下脑血流测量。

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Linkun Cai, Yawen Liu, Haijun Niu, Wei Zheng, Hao Wang, Han Lv, Pengling Ren, Zhenchang Wang
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引用次数: 0

摘要

微重力诱导的脑血流(CBF)改变可能导致宇航员认知能力下降和神经变性。在微重力条件下精确量化脑血流是维持宇航员健康和确保人类太空任务成功的基础。动脉自旋标记(ASL)灌注磁共振成像(MRI)是目前唯一一种定量评估全身和局部脑血流的无创、无放射性技术。然而,由于技术、后勤和有效载荷的限制,在空间站上部署核磁共振扫描仪仍然具有挑战性。为了解决这一挑战,我们提出了一种端到端的解剖导向生成对抗网络(AgGAN),作为一种无创、经济高效、准确的工具,通过从相应的基线图像合成模拟微重力条件下的ASL图像来估计CBF。具体而言,受放射科医生诊断模式的启发,我们开发了一个位置感知模块来结合大脑解剖先验,以及一个区域自适应特征提取模块来捕获不规则大脑区域的特征。我们还引入了区域感知的焦点损失,以提高解剖复杂区域的合成质量。此外,我们提出了结构边界感知损失,以鼓励合成网络学习边界细节,有效避免了部分体积效应的加剧,提高了CBF量化的准确性。实验结果表明,所提出的AgGAN在模拟微重力条件下的ASL图像合成中具有优势,并具有良好的主观图像质量评价。这些发现突出了我们的模型在太空飞行中预测宇航员脑血流的潜力。我们的数据集和代码可在https://github.com/Cai-Linkun/AgGAN上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AgGAN: Anatomy-guided generative adversarial network to synthesize arterial spin labeling images for cerebral blood flow measurement under simulated microgravity.

Microgravity-induced alterations in cerebral blood flow (CBF) may contribute to cognitive decline and neurodegeneration in astronauts. Accurate CBF quantification under microgravity conditions is fundamental for maintaining astronaut health and ensuring the success of human space missions. Arterial spin labeling (ASL) perfusion magnetic resonance imaging (MRI) is currently the only non-invasive, non-radioactive technique to quantitatively assessing global and regional CBF. However, deploying MRI scanners aboard space station remains challenging due to technical, logistical and payload limitations. To address this challenge, we propose an end-to-end Anatomy-guided Generative Adversarial Network (AgGAN) as non-invasive, cost-effective, and accurate tool for estimating CBF by synthesizing ASL images under simulated microgravity conditions from corresponding baseline images. Specifically, inspired by radiologists' diagnostic pattern, we develop a position-aware module to incorporate brain anatomical prior, and a region-adaptive feature extraction module to capture features of irregular brain regions. We also introduce a region-aware focal loss to enhance the synthesis quality of anatomically complex regions. Furthermore, we propose structure boundary-aware loss to encourage the synthesis network to learn boundary details, effectively avoiding exacerbation of partial volume effect and improving the accuracy of CBF quantification. Experimental results demonstrate the superiority of the proposed AgGAN in ASL image synthesis under simulated microgravity and show excellent subjective image quality evaluation. These findings highlight the potential of our model for CBF prediction in astronauts during spaceflight. Our dataset and code are available at https://github.com/Cai-Linkun/AgGAN.

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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.
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