Department of Electronic Engineering, Information School, Yunnan University, Kunming 650091, China.
Department of Electronic Engineering, Information School, Yunnan University, Kunming 650091, China.
Artif Intell Med. 2023 Oct;144:102664. doi: 10.1016/j.artmed.2023.102664. Epub 2023 Sep 20.
Accurate measurement of blood flow velocity is important for the prevention and early diagnosis of atherosclerosis. However, due to the uncertainty of parameter settings, the autocorrelation velocimetry methods based on clutter filtering are prone to incorrectly filter out the near-wall blood flow signal, resulting in poor velocimetric accuracy. In addition, the Doppler coherent compounding acts as a low-pass filter, which also leads to low values of blood flow velocity estimated by the above methods. Motivated by this status quo, here we propose a deep learning estimator that combines clutter filtering and blood flow velocimetry based on the adaptive property of one-dimensional convolutional neural network (1DCNN). The estimator is operated by first extracting the blood flow signal from the original Doppler echo signal through an affine transformation of the 1D convolution, and then converting the extracted signal into the desired blood flow velocity using a linear transformation function. The effectiveness of the proposed method is verified by simulation as well as in vivo carotid artery data. Compared with typical velocimetry methods such as high-pass filtering (HPF) and singular value decomposition (SVD), the results show that the normalized root means square error (NRMSE) obtained by 1DCNN is reduced by 54.99 % and 53.50 % for forward blood flow velocimetry, and 70.99 % and 69.50 % for reverse blood flow velocimetry, respectively. Consistently, the in vivo measurements demonstrate that the goodness-of-fit of the proposed estimator is improved by 8.72 % and 4.74 % for five subjects. Moreover, the estimation time consumed by 1DCNN is greatly reduced, which costs only 2.91 % of the time of HPF and 12.83 % of the time of SVD. In conclusion, the proposed estimator is a better alternative to the current blood flow velocimetry, and is capable of providing more accurate diagnosis information for vascular diseases in clinical applications.
准确测量血流速度对于动脉粥样硬化的预防和早期诊断非常重要。然而，由于参数设置的不确定性，基于杂波滤波的自相关速度测量方法容易错误地滤除近壁血流信号，导致速度测量精度较差。此外，多普勒相干复合作用类似于低通滤波器，这也导致上述方法估计的血流速度值较低。鉴于这种现状，我们提出了一种基于一维卷积神经网络（1DCNN）自适应特性的结合杂波滤波和血流速度测量的深度学习估计器。该估计器通过对一维卷积的仿射变换从原始多普勒回波信号中提取血流信号，然后使用线性变换函数将提取的信号转换为所需的血流速度。通过仿真和体内颈动脉数据验证了该方法的有效性。与典型的速度测量方法（如高通滤波（HPF）和奇异值分解（SVD））相比，结果表明，1DCNN 获得的归一化均方根误差（NRMSE）在正向血流速度方面分别降低了 54.99%和 53.50%，在反向血流速度方面分别降低了 70.99%和 69.50%。一致地，体内测量表明，对于五个受试者，所提出的估计器的拟合优度提高了 8.72%和 4.74%。此外，1DCNN 消耗的估计时间大大减少，仅消耗 HPF 时间的 2.91%和 SVD 时间的 12.83%。总之，所提出的估计器是当前血流速度测量的更好选择，能够为临床应用中的血管疾病提供更准确的诊断信息。
