基于卷积神经网络的血管内血液流速反演方法研究  

Research on inversion method of intravascular blood flow velocity based on convolutional neural network

作　　者：王雨忱 杨丹[1,2] 徐彬 张欣宇 王旭[2] WANG Yuchen;YANG Dan;XU Bin;ZHANG Xinyu;WANG Xu(Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education,Northeastern University,Shenyang 110819,P.R.China;School of Information Science and Engineering,Northeastern University,Shenyang 110819,P.R.China;School of Computer Science and Engineering,Northeastern University,Shenyang 110169,P.R.China)

机构地区：[1]东北大学智能工业数据解析与优化教育部重点实验室,沈阳110819 [2]东北大学信息科学与工程学院,沈阳110819 [3]东北大学计算机科学与工程学院,沈阳110819

出　　处：《生物医学工程学杂志》2022年第3期561-569,共9页Journal of Biomedical Engineering

基　　金：国家自然科学基金(71790614,51607029,61836011);中央高校基础科研业务费(2020GFZD008,2020GFYD011);111项目(B16009);辽宁省自然科学基金(2021-MS093);辽宁省教育厅基础科学研究项目2021(LJKZ0014)。

摘　　要：基于磁电效应的血液流速反演有助于血管狭窄病变日常监测的发展,但血液流速反演准确率和成像分辨率仍有待提高。因此,本文提出一种基于卷积神经网络(CNN)的血管内血液流速反演方法。首先,构建非监督学习CNN提取权重矩阵表征信息对电压数据预处理;再将预处理结果输入至有监督学习CNN,经非线性映射输出血液流速值;最终获得血管断层图像。本文通过构建数据检验所提方法的有效性,结果显示,所提方法在血管位置和血管狭窄实验中的血液流速反演相关系数分别达到0.884 4和0.972 1。以上研究表明,本文所提方法有效减少反演过程中信息的丢失,并提高反演准确率和成像分辨率,有望辅助临床诊断。Blood velocity inversion based on magnetoelectric effect is helpful for the development of daily monitoring of vascular stenosis, but the accuracy of blood velocity inversion and imaging resolution still need to be improved. Therefore, a convolutional neural network(CNN) based inversion imaging method for intravascular blood flow velocity was proposed in this paper. Firstly, unsupervised learning CNN is constructed to extract weight matrix representation information to preprocess voltage data. Then the preprocessing results are input to supervised learning CNN, and the blood flow velocity value is output by nonlinear mapping. Finally, angiographic images are obtained. In this paper, the validity of the proposed method is verified by constructing data set. The results show that the correlation coefficients of blood velocity inversion in vessel location and stenosis test are 0.884 4 and 0.972 1, respectively. The above research shows that the proposed method can effectively reduce the information loss during the inversion process and improve the inversion accuracy and imaging resolution, which is expected to assist clinical diagnosis.

关 键 词：磁电效应 血管狭窄病变 卷积神经网络 血液流速反演 非监督学习 

分 类 号：R318[医药卫生—生物医学工程] TP183[医药卫生—基础医学] O441[自动化与计算机技术—控制理论与控制工程]