Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Ultrasound Med Biol. 2011 Aug;37(8):1280-91. doi: 10.1016/j.ultrasmedbio.2011.05.006. Epub 2011 Jun 17.
Many recent studies on ultrasonic particle image velocimetry (Echo PIV) showed that the accuracy of two-dimensional (2-D) flow velocity measured depends largely on the concentration of ultrasound contrast agents (UCAs) during imaging. This article presents a texture-based method for identifying the optimum microbubble concentration for Echo PIV measurements in real-time. The texture features, standard deviation of gray level, and contrast, energy and homogeneity of gray level co-occurrence matrix were extracted from ultrasound contrast images of rotational and pulsatile flow (10 MHz) in vitro and in vivo mouse common carotid arterial flow (40 MHz) with UCAs at various concentrations. The results showed that, at concentration of 0.8∼2 × 10³ bubbles/mL in vitro and 1∼5 × 10⁵ bubbles/mL in vivo, image texture features had a peak value or trough value, and velocity vectors with high accuracy can be obtained. Otherwise, poor quality velocity vectors were obtained. When the texture features were used as a feature set, the accuracy of K-nearest neighbor classifier can reach 86.4% in vitro and 87.5% in vivo, respectively. The texture-based method is shown to be able to quickly identify the optimum microbubble concentration and improve the accuracy for Echo PIV imaging.
许多最近关于超声粒子图像测速（Echo PIV）的研究表明，二维（2-D）流速测量的准确性在很大程度上取决于成像过程中超声对比剂（UCAs）的浓度。本文提出了一种基于纹理的方法，用于实时识别 Echo PIV 测量的最佳微泡浓度。从体外旋转和脉动流（10MHz）以及体内小鼠颈总动脉流（40MHz）的超声对比图像中提取了纹理特征、灰度水平的标准差、灰度共生矩阵的对比度、能量和均匀性，UCAs 的浓度各不相同。结果表明，在体外浓度为 0.8∼2 × 10³个气泡/mL，体内浓度为 1∼5 × 10⁵个气泡/mL 时，图像纹理特征具有峰值或谷值，可以获得具有高精度的速度矢量。否则，会得到质量较差的速度矢量。当将纹理特征用作特征集时，K-最近邻分类器的准确性在体外分别达到 86.4%，体内达到 87.5%。基于纹理的方法能够快速识别最佳微泡浓度，提高 Echo PIV 成像的准确性。
