在扩散相关谱中量化血液流量指数,使用强大的深度学习方法
Quan Wang1, Mingliang Pan1, Zhenya Zang1
1University of Strathclyde, Department of Biomedical Engineering, Faculty of Engineering, Glasgow, United Kingdom.
Journal of biomedical optics
|January 29, 2024
概括
一种新的深度学习方法,DCS-NET,快速而可靠地分析分散相关谱 (DCS) 数据以估计血流指数 (BFi). 与传统的装配方法相比,这种AI方法显著提高了准确性和速度.
科学领域:
- 生物医学光学 生物医学光学
- 医疗成像医学成像
- 光学光谱学是指光学光谱学.
背景情况:
- 扩散相关谱学 (DCS) 是一种用于测量血液流量的非侵入性光学技术.
- 计算血流指数 (BFi) 的传统方法依赖于自相关函数 (ACF) 的计算密集的非线性最小平方拟合.
- 这些传统的装配方法容易受到噪音和光学特性和组织层厚度的变化的影响.
研究的目的:
- 开发一个数据驱动的深度学习方法,用于快速和可靠地分析DCS时间ACF.
- 为了使BFi估计在源-探测器距离的范围,克服固定距离方法的局限性.
- 在各种生理条件下,将深度学习方法的性能与传统的拟合模型进行比较.
主要方法:
- 一个深度学习架构,DCS神经网络 (DCS-NET),利用1D卷积神经网络被开发用于BFi和连贯因子估计.
- 基于三层大脑模型的模拟DCS数据被用于训练和验证DCS-NET.
- 该研究量化了光学特性,层厚度,噪声和源探测器距离对DCS-NET和传统安装模型的影响.
主要成果:
- DCS-NET显示了显著更快的分析速度 (分别比三层和半无限模型快17000倍和32倍).
- 深度学习方法显示了对深层组织的提高敏感性和出色的抗噪声能力.
- 使用DCS-NET的相对BFi (rBFi) 提取与传统方法相比 (43.76%和19.66%) 的误差很低 (8.35%).
结论:
- DCS-NET在扩展的源探测器距离上稳定量化了血液流量测量,从而实现了更深入的组织分析.
- 该方法显示了硬件实现的潜力,促进了持续的实时血流监测.
- 深度学习为克服传统DCS分析的局限性提供了一个有希望的途径.
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