University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom.
University of Twente, MIRA Institute for Biomedical Technology and Technical Medicine, Enschede, The Netherlands.
J Biomed Opt. 2018 Feb;23(2):1-8. doi: 10.1117/1.JBO.23.2.026009.
Photoacoustic flowmetry (PAF) based on time-domain cross correlation of photoacoustic signals is a promising technique for deep tissue measurement of blood flow velocity. Signal processing has previously been developed for single element transducers. Here, the processing methods for acoustic resolution PAF using a clinical ultrasound transducer array are developed and validated using a 64-element transducer array with a -6 dB detection band of 11 to 17 MHz. Measurements were performed on a flow phantom consisting of a tube (580 μm inner diameter) perfused with human blood flowing at physiological speeds ranging from 3 to 25 mm / s. The processing pipeline comprised: image reconstruction, filtering, displacement detection, and masking. High-pass filtering and background subtraction were found to be key preprocessing steps to enable accurate flow velocity estimates, which were calculated using a cross-correlation based method. In addition, the regions of interest in the calculated velocity maps were defined using a masking approach based on the amplitude of the cross-correlation functions. These developments enabled blood flow measurements using a transducer array, bringing PAF one step closer to clinical applicability.
基于光声信号的时域互相关的光声流速测量（PAF）是一种很有前途的深层组织血流速度测量技术。信号处理以前是针对单元素换能器开发的。在这里，开发了使用临床超声换能器阵列的声学分辨率 PAF 的处理方法，并使用具有 -6 dB 检测带宽为 11 至 17 MHz 的 64 元件换能器阵列进行了验证。在由以生理速度（3 至 25 mm/s）流动的人血灌注的内径为 580 μm 的管组成的流动体模上进行了测量。处理管道包括：图像重建、滤波、位移检测和掩蔽。发现高通滤波和背景减除是实现准确流速估计的关键预处理步骤，这是使用基于互相关的方法计算得出的。此外，使用基于互相关函数幅度的掩蔽方法来定义计算速度图中的感兴趣区域。这些发展使我们能够使用换能器阵列进行血流测量，使 PAF 更接近临床应用。
