基于深度学习的浅层和深层血流率的分离在扩散相关谱学中
Mikie Nakabayashi1, Siwei Liu1, Nawara Mahmood Broti1
1Electrical Engineering Program, Graduate School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa, 2148571, Japan.
Biomedical optics express
|October 19, 2023
概括
这项研究引入了一种新的长期短期记忆网络,以准确测量浅层和深层组织的血流率. 这种方法在扩散相关谱学上得到了改进,用于诸如在运动期间监测肌肉血流等应用.
科学领域:
- 生物医学光学 生物医学光学
- 生理测量生理测量
- 医疗保健中的机器学习
背景情况:
- 扩散相关谱法 (DCS) 难以区分皮肤和深层组织的血流.
- 精确量化不同组织层的血液流量对于生理监测至关重要.
研究的目的:
- 开发一种直接量化浅层和深层组织血流率的方法.
- 克服传统的扩散相关谱的局限性,以区分血流贡献.
主要方法:
- 利用长期短期记忆 (LSTM) 网络,一种循环神经网络.
- 利用浅层和深层流速对自相关函数的差异性贡献.
- 使用双层流幻体实验验验证了模型.
主要成果:
- 该LSTM网络准确地预测了浅层和深层的流量.
- 实现了高精度,RMSE值为0.047和0.034毫升/分钟/100克,两个层的R2值为0.99.
- 在模拟的双层组织中证明了血流的成功分化.
结论:
- 拟议的LSTM网络为分离和量化皮肤和深组织血流提供了强大的解决方案.
- 这种技术对于在运动等生理变化期间评估活跃肌肉中的血流动力学是有价值的.
- 能够更精确地评估复杂生物组织中局部血流反应.
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