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使用基于模型的深度神经网络与合成训练数据加速CEST成像.

Jianping Xu1, Tao Zu1, Yi-Cheng Hsu2

  • 1Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, People's Republic of China.

Magnetic resonance in medicine
|October 23, 2023
PubMed
概括
此摘要是机器生成的。

一个新的深度神经网络,CEST-VN,从低样本数据中重建高质量的化学交换和转移 (CEST) MRI 图像. 该方法的性能优于现有技术,为诊断提供准确的胺质子转移权重图.

关键词:
在CEST中,CEST是CEST.深度学习是一种深度学习.快速的核磁共振成像 (MRI).图像重建 图像重建变化网络的变化网络.

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科学领域:

  • 磁共振成像技术 磁共振成像技术
  • 生物医学工程 生物医学工程
  • 医疗成像中的人工智能

背景情况:

  • 化学交换和转移 (CEST) MRI对于评估组织生理学至关重要.
  • 在CEST MRI中采集不足的数据导致图像质量恶化.
  • 开发先进的重建方法对于高效的CEST成像是至关重要的.

研究的目的:

  • 开发基于模型的深度神经网络 (CEST-VN) 以高质量重建低采样多线圈CEST数据.
  • 为了利用深度学习先验和多线圈灵敏度编码来改进CEST图像重建.

主要方法:

  • 将 CEST 图像重建方程展开为深度神经网络 (CEST-VN),灵感来自变异网络.
  • 整合一个k空间数据共享块和3D空间频率卷积内核.
  • 通过使用多池Bloch-McConnell模拟和使用CEST特定损失函数进行训练来合成多线圈CEST数据.

主要成果:

  • 在健康和脑瘤受试者中,CEST-VN生成了高质量的CEST源图像和胺质子转移权重图.
  • 该方法始终优于GRAPPA,盲压缩传感和原始变化网络 (VN).
  • 即使增加加速因子 (3至6) 时,也可以实现准确的重建,而不会显著地损失细节或增加文物.

结论:

  • 拟议的CEST-VN方法可以从高度低采样的多线圈数据中获得高质量的CEST成像.
  • 深度学习先验和多线圈灵敏度编码模型的整合是有效的.
  • CEST-VN显示了在神经成像中改善诊断能力的巨大潜力.