通过使用动态对比增强MRI的动脉输入函数来提高动态敏感度对比MRI中 perfusion 估计的可靠性
Chih-Hsien Tseng1,2,3, Jaap Jaspers3,4, Alejandra Mendez Romero3,4
1Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands.
NMR in biomedicine
|September 15, 2023
概括
从动态对比增强 (DCE) MRI中导出动脉输入函数 (AIF),与动态感应对比 (DSC) MRI相比,可以提供更稳定和更现实的脑血流 (CBF) 测量. 基于DCE的AIF可以改善DSCMM的定量 perfusion 估计.
科学领域:
- 医疗成像医学成像
- 神经科学是一个神经科学.
- 放射学 放射学是一门学科.
背景情况:
- 动脉输入函数 (AIF) 在动态灵敏度对比 (DSC) MRI 中对定量 perfusion 估计至关重要.
- 由于信号不确定性和部分体积效应,在DSCMRI中准确测量AIF具有挑战性.
- 动态对比增强 (DCE) MRI为AIF确定提供了一个潜在的替代来源.
研究的目的:
- 为了比较来自DCE MRI的AIF与来自DSC MRI的AIF.
- 评估DCE衍生AIF对基于DSC的透系数估计的影响.
- 用不同的AIF方法评估大脑血流 (CBF) 测量的稳定性和准确性.
主要方法:
- 在中脑动脉 (MCA) 中,从DCE和DSC数据中手动选择了AIF.
- 一个半自动的 AIF 选择算法被应用到 DSC 数据.
- 对AIF参数 (幅度,宽度) 和衍生CBF值的统计比较.
主要成果:
- 从DSC衍生出的AIF在患者的峰值值中显示出很高的变化.
- 半自动的DSC-AIF比手动的DSC-AIF有更窄的宽度和更大的峰值.
- 基于DCE的AIF提供了更稳定的相对和绝对CBF测量,与之前的文献相一致.
结论:
- 与基于DCE的AIF相比,基于DSC的AIF显示出更高的可重复性和更现实的配置文件.
- 从DCE衍生出的AIF导致在DSCMRI中更稳定和合理的定量 perfusion 估计.
- 基于DCE的AIF是DSCMRI中准确的AIF采购的可行替代方案.
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