K-UNN:使用未经训练的神经网络进行k空间插曲
Zhuo-Xu Cui1, Sen Jia2, Chentao Cao2
1Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Medical image analysis
|July 3, 2023
概括
这项研究引入了一种使用未经训练的神经网络 (UNN) 的新型MRI重建方法,该方法结合了物理先验,以提高准确性. 保护的k空间插值方法在诸如部分里埃图像等场景中提高了性能.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 未训练的神经网络 (UNN) 在没有训练数据的情况下显示出对MR图像重建的希望.
- 现有的UNN缺乏物理预先建模,限制了部分里埃 (PF) 和定期采样等场景中的性能,并且缺乏理论保证.
研究的目的:
- 开发一种安全的k空间插值方法,用于MRI重建.
- 将物理先验集成到基于UNN的MRI重建中,以提高准确性和理论保证.
主要方法:
- 开发了一个专门设计的UNN,具有三重架构.
- UNN是由三个物理先验驱动的:转换稀疏性,线圈灵敏度光滑性和相位光滑性.
- 采用了一个安全的k空间插值技术.
主要成果:
- 拟议的方法有效地描述了MRI图像中的物理先验.
- 证实了对插入的k空间数据准确性的保证严格界限.
- 该方法始终优于传统的并行成像和现有的UNNs.
- 绩效与PF中的监督深度学习方法和定期低样本化具有竞争力.
结论:
- 拟议的保护k空间插值方法与基于物理的UNN显著改善了MRI重建.
- 这种方法在各种低样本场景中提供理论保证和强大的性能,优于现有方法.
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