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使用生成对抗网络的磁共振图像的超分辨率

João Guerreiro¹, Pedro Tomás¹, Nuno Garcia²

¹INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

|August 19, 2023

概括

生成对抗网络 (GAN) 可以将磁共振成像 (MRI) 扫描升级为4倍. 这些先进的机器学习模型保持高频细节,降低成本和患者的不适.

科学领域:

医疗成像医学成像
机器学习机器学习
人工智能的人工智能

背景情况:

磁共振成像 (MRI) 面临的局限性包括空间覆盖范围小,成本高,扫描时间长.
通过减少测量来加速MRI采集对于克服这些局限性至关重要.
机器学习 (ML) 技术,特别是超分辨率 (SR) 技术,在从低分辨率 (LR) 信号中恢复高分辨率 (HR) 图像方面表现有前途.

研究的目的:

审查基于生成对抗网络 (GAN) 的超分辨率 (SR) 方法用于磁共振成像 (MRI).
评估GAN在X4的数量上升MRI数据的能力,同时保留关键细节.
评估基于GAN的方法在降低医疗成本和改善患者体验方面的潜力.

主要方法:

对应用到MRI重建的基于GAN的SR技术现有文献的审查.
对不同GAN模型的定量和定性绩效指标的分析.
基于GAN的SR方法与其他深度学习方法进行MRI升级的比较.

主要成果:

在MRI重建和加速方面,GAN显示出显著的潜力.
在数量上,SRResCycGAN在恢复×4缩小的MRI图像方面优于其他深度学习方法.
贝比-GAN在定性评估中实现了卓越的感知质量,突出了GAN推断缺失细节的能力.

结论:

基于GAN的SR方法可以有效地将MRI数据升级到x4,保持可靠的高频细节.
这些方法为降低医疗费用,减少患者的痛苦提供了一条途径,并使新的MRI应用成为可能.
GANs为推进MRI技术提供了一个强大的工具,使其更容易获得和更有效.

关键词:

生成性的对抗性网络.磁力共振成像 (MRI) 加速加速医学成像医学成像超级分辨率的超级分辨率

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