基于数值整合的血流估计方法在扩散相关谱法中的比较
1Research Center for Intelligent Information Technology, Nantong University, Nantong 226019, China; Department of Mechatronics and Robotics, School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China.
Computer methods and programs in biomedicine
|August 30, 2023
概括
原来的INISg1方法用于扩散相关谱 (DCS) 血流监测在大多数条件下都是稳健的. 变体在极端场景中表现更好,指导研究人员选择合适的信号处理技术.
科学领域:
- 生物医学光学 生物医学光学
- 光学传感传感器是什么?
- 血流监测 血液流量监测
背景情况:
- 扩散相关谱 (DCS) 是光学血流监测的一个关键技术.
- 在DCS中的信号处理,特别是非线性装配,可能会限制实时应用.
- INISg1方法以前被引入以加速DCS信号处理.
研究的目的:
- 介绍和比较用于DCS信号处理的INISg1方法的变体.
- 在各种条件下评估INISg1及其变体的稳定性和速度.
- 为在各种DCS应用中实施INISg1的研究人员提供指导.
主要方法:
- 开发了INISg1变体,使用右利曼和 (INISg1_RR) 和梯形规则 (INISg1_TR) 进行数值集成.
- 进行基于模型的模拟来控制实验参数,如集成时间,β和光子计数率.
- 将原始INISg1的性能和处理速度与其变体进行了比较.
主要成果:
- 原来的INISg1在大多数模拟条件下表现出强的性能.
- 在极端实验条件下,INISg1变种 (INISg1_RR,INISg1_TR) 在极端实验条件下表现出优异的性能.
- INISg1实现了比INISg1_RR和INISg1_TR分别快1.63倍和1.98倍的信号处理速度.
结论:
- 在典型的场景中,INISg1是用于DCS信号处理的可靠方法.
- 该研究为根据实验条件选择合适的INISg1变体提供了有价值的见解.
- 这项研究作为一个指南,优化实时血流监测使用DCS.
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