TRACC-PHYSIO:时间域分辨率调整的交叉相关性,以估计动态MRI中的物理逻辑合和时间延迟
Adam M Wright1,2, Jianing Zhang1, Yunjie Tong2
1Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.
在没有快速成像的情况下,TRACC-PHYSIO从MRI数据中量化大脑脉冲. 这种方法准确地测量心脏和呼吸系统的合和时间延迟,即使扫描时间缓慢.
科学领域:
- 神经成像是一种神经成像.
- 生理监测是指对身体进行生理监测.
- 医学物理 医学物理
背景情况:
- 物理脑脉冲对于神经流体循环和废物清除至关重要.
- 标准的MRI技术往往缺乏时间分辨率来捕捉这些动态.
- 这限制了与大脑脉相关的生理信息的理解.
研究的目的:
- 介绍TRACC-PHYSIO,这是一个新的时间域框架,用于分析动态MRI中的生理学合.
- 验证 TRACC-PHYSIO 测量心脏和呼吸脉冲时间延迟和合强度的能力.
- 评估各种模拟条件的性能,包括不同的生理成分和MRI获取参数.
主要方法:
- 开发了TRACC-PHYSIO,这是一个基于交叉相关性的分析框架.
- 模拟现实的动态MR信号与混合的心脏和呼吸系统生理成分.
- 通过使用像峰值合系数 (峰值CorrCoeff) 和时间延迟等指标在不同的TR和采购时间下评估性能.
主要成果:
- TRACC-PHYSIO稳定量化了心脏 (TRACC-心脏) 和呼吸道 (TRACC-呼吸道) 脉冲的合强度和时间延迟.
- 该方法表现出可靠的性能,即使重复时间长 (TRs) 高达3秒.
- 系统模拟证实了该框架在各种生理和获取场景中的准确性.
结论:
- 在动态MRI中,TRACC-PHYSIO能够在没有快速获取的情况下,对生理学合进行可靠的时间域分析.
- 该框架为脉冲时间延迟提供了毫秒级的时间分辨率,揭示了隐藏的生理信息.
- 在健康和疾病中,TRACC-PHYSIO为研究大脑脉动机制和神经流体动力学提供了新的可能性.
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