改善微结构完整性,间歇性液体和血液微循环图像从多b值扩散MRI使用物理信息的神经网络在脑血管疾病中
Paulien H M Voorter1,2, Walter H Backes1,2,3, Oliver J Gurney-Champion4
1Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.
Magnetic resonance in medicine
|June 15, 2023
概括
基于物理学的神经网络 (PINNs) 改善了对脑血管疾病的扩散MRI分析. 这种方法提高了图像质量,可重复性和准确性,以获得更好的微观结构和微血管洞察力.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 神经科学是一个神经科学.
背景情况:
- 扩散MRI提供了对组织微观结构和微血管结构的洞察.
- 内素不连贯运动 (IVIM) 分析量化了扩散组件,但面临着传统安装方法的挑战.
- 脑血管疾病会影响大脑组织的完整性和功能.
研究的目的:
- 评估物理信息神经网络 (PINN) 方法,以适应多b值扩散MRI数据.
- 改进微观结构完整性,间歇液体和微血管参数的估计.
- 为了比较PINN配件与常规方法进行intravoxel不连贯运动 (IVIM) 分析.
主要方法:
- 获得了来自16名脑血管疾病患者的多个b值的全脑逆转恢复扩散权重图像.
- 与PINN三元件IVIM (3C-IVIM) 模型进行了比较,采用非负最小正方形和两步最小正方形.
- 通过模拟评估参数图质量 (PCNR),测试重复性 (CV,ICC) 和voxel精度.
主要成果:
- 与传统方法相比,PINN衍生的3C-IVIM参数图显示出更高的质量和可重复性.
- 通过PINN方法,在估计扩散组件时,可以获得更高的voxel精度.
- 通过PINNs观察到增强的对比度和噪声比率 (PCNR) 和降低的变化系数 (CV).
结论:
- 基于物理学的神经网络能够从扩散权重信号中对扩散组件进行可靠的voxel-wise估计.
- 由PINN生成的参数图为评估脑血管疾病中的病理生理过程提供了高质量和可重复性.
- 这种方法促进了对微结构和微血管变化的视觉评估的改进.
关键词:
免费的水免费的水在intravoxel不连贯的运动.微血管 perfusion 微血管 perfusion 微血管 perfusion 微血管 perfusion 微血管 perfusion 微血管 perfusion 微血管 perfusion 微血管 perfusion 微血管 perfusion 微血管多个b值扩散MRI的MRI.帕伦基马扩散的发生.基于物理的深度学习.更多相关视频
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