Ultrafast Ultrasound Color Blood Flow Imaging Based on the DCNN

CUI Wang; HE Bing-bing; ZOU Liang-chen; WANG Ting-ting; LI Hai-yan

doi:10.12263/DZXB.20241148

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Ultrafast Ultrasound Color Blood Flow Imaging Based on the DCNN

PAPERS | 更新时间：2025-12-27

- Ultrafast Ultrasound Color Blood Flow Imaging Based on the DCNN
- ACTA ELECTRONICA SINICA Vol. 53, Issue 8, Pages: 2843-2853(2025)
- 作者机构：
  
  云南大学信息学院电子工程系，云南昆明 650091
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62201495)
- DOI：10.12263/DZXB.20241148
  CLC： TP391.9;
- Received：20 December 2024，
  
  Accepted：09 June 2025，
  
  Published：25 August 2025
- 稿件说明：
移动端阅览
崔旺, 何冰冰, 邹良辰, 等. 基于DCNN的超快超声彩色血流成像[J]. 电子学报, 2025, 53(08): 2843-2853.

CUI Wang, HE Bing-bing, ZOU Liang-chen, et al. Ultrafast Ultrasound Color Blood Flow Imaging Based on the DCNN[J]. Acta Electronica Sinica, 2025, 53(08): 2843-2853.
崔旺, 何冰冰, 邹良辰, 等. 基于DCNN的超快超声彩色血流成像[J]. 电子学报, 2025, 53(08): 2843-2853. DOI：10.12263/DZXB.20241148

CUI Wang, HE Bing-bing, ZOU Liang-chen, et al. Ultrafast Ultrasound Color Blood Flow Imaging Based on the DCNN[J]. Acta Electronica Sinica, 2025, 53(08): 2843-2853. DOI：10.12263/DZXB.20241148

摘要

多角度平面波相干复合（Multi-angle Plane Wave Coherent Compounding，MPWCC）实现了高帧率超声扫描，有助于彩色血流成像技术提供更准确的血流信息和组织图像.然而，MPWCC的低通效应会导致血流速度估计值偏低，且无法通过计算的方式确定杂波抑制滤波器的最佳阈值.对此，本文提出一种基于深度卷积神经网络（Deep Convolutional Neural Network，DCNN）的超快超声彩色血流成像方法.基于Field II平台搭建颈动脉仿真模型，以获得不同速度的血流超声多普勒信号，该信号经过奇异值分解（Singular Value Decomposition，SVD）、归一化等处理转换为训练数据.DCNN模型通过对训练数据进行有监督学习实现对不同速度多普勒信号的特征学习和杂波抑制，随后将特征信息转化为速度信息用于彩色成像.与高通滤波（High Pass Filtering，HPF）或SVD相结合的自相关测速法相比，本文方法在仿真、人体颈动脉的测试数据上均表现出了更好的性能：在估计正反向的仿真血流速度剖面时，该方法的归一化均方根误差（Normalized Root Mean Square Error，NRMSE）比HPF和SVD平均降低了45.65%和41.95%；在仿真及人体数据的彩色成像结果中，该方法呈现出最好的杂波抑制效果和血管完整度.综上，该方法能够实现超快超声彩色血流成像，在血流流态可视化方面具有应用价值.

Abstract

Multi-angle plane wave coherent compounding (MPWCC) can achieve high frame rate ultrasound scanning

which aids color flow imaging technology in providing more accurate blood flow information and tissue images. However

the low-pass effect of the MPWCC results in underestimated blood flow velocities and the optimal threshold for clutter suppression filters cannot be determined computationally. To address this

this paper proposes an ultra fast ultrasound color blood flow imaging based on deep convolutional neural networks (DCNN). Based on the Field II ultrasound simulator

the carotid artery model is built to acquire ultrasound doppler signals with different blood flow velocities. These signals are processed with the singular value decomposition (SVD) and then normalized to generate training dataset. The DCNN model learns the characteristics of Doppler signals with different velocities through supervised learning

enabling clutter suppression and conversion of feature information into velocity information for color flow imaging. Compared to the autocorrelation velocimetry by combining high pass filtering (HPF) or SVD

the superior performance of the proposed method has been demonstrated in both simulation and human carotid artery test dataset. When blood flow velocity profiles in both forward and reverse directions are estimated

the normalized root mean square error (NRMSE) of the proposed method is reduced by an average of 45.65% and 41.95% than these of the HPF and SVD

respectively. In the results of color flow imaging in simulation and human data

the proposed method shows the best clutter suppression effect and vessel integrity. In summary

this method achieves ultrafast ultrasound color blood flow imaging and is applicable for visualizing blood flow dynamics.

关键词

Keywords

references

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