从呼吸带记录中生成动态二氧化碳痕迹:使用神经网络的可行性和功能磁共振成像中的应用
Vismay Agrawal1, Xiaole Z Zhong1,2, J Jean Chen1,2,3
1Baycrest Centre for Geriatric Care, Rotman Research Institute, Toronto, ON, Canada.
Frontiers in neuroimaging
|August 9, 2023
概括
这项研究引入了一种深度学习方法,用于在功能性MRI期间从呼吸数据中估计二氧化碳 (CO2) 和潮尾CO2. 这种方法增强了休息状态fMRI研究中的生理信号分析.
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.
- 机器学习 机器学习
背景情况:
- 二氧化碳 (CO2) 对血管生理学至关重要,也是静止状态fMRI (rs-fMRI) 中的主要噪音来源.
- 目前的rs-fMRI研究往往缺乏二氧化碳记录,依赖于有限的生理数据,如心率和呼吸.
- 现有的将呼吸与二氧化碳联系起来的分析模型未得到充分利用.
研究的目的:
- 开发和验证一种深度学习 (DL) 模型,用于从呼吸波形中重建CO2和终潮CO2 (PETCO2).
- 为了证明DL在预测CO2水平的有效性,从静止状态下的呼吸数据.
- 通过结合二氧化碳动态来提供一种新的方法来增强rs-fMRI信号分析.
主要方法:
- 使用完全卷积网络 (FCN) 的深度学习方法用于从呼吸数据中预测CO2.
- 该模型通过同时记录呼吸和CO2进行了训练和验证.
- 通过将预测的CO2和PETCO2与使用Pearson相关系数的地面真相测量进行比较来评估性能.
主要成果:
- FCN模型准确地预测了来自呼吸的二氧化碳,达到0.946 ± 0.056.5的皮尔森相关系数 (r).
- 动态PETCO2成功地从预测的CO2中导出,r = 0.512 ± 0.269.
- 基于DL的FCN方法在二氧化碳预测方面表现优于传统的分析方法.
结论:
- 深度学习可以从呼吸数据中准确估计动态CO2和PETCO2.
- 这种方法为缺乏直接二氧化碳测量的rs-fMRI研究提供了有价值的工具.
- 这些发现支持DL在处理生理fMRI信号方面的进步.
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