Chaigneau Emmanuelle, Charpak Serge
Institut de la Vision, INSERM U968, Paris, France.
Institut de la Vision, CNRS UMR 7210, Paris, France.
Front Physiol. 2022 Apr 7;13:848002. doi: 10.3389/fphys.2022.848002. eCollection 2022.
Laser scanning microscopy is widely used to measure blood hemodynamics with line-scans in physiological and pathological vessels. With scans of broken lines, i.e., lines made of several segments with different orientations, it also allows simultaneous monitoring of vessel diameter dynamics or the activity of specific cells. Analysis of red blood cell (RBC) velocity from line-scans requires specific image-processing algorithms, as angle measurements, Line-Scanning Particle Image Velocimetry (LSPIV) or Fourier transformation of line-scan images. The conditions under which these image-processing algorithms give accurate measurements have not been fully characterized although the accuracy of measurements vary according to specific experimental parameters: the vessel type, the RBC velocity, the scanning parameters, and the image signal to noise ratio. Here, we developed mathematical models for the three previously mentioned line-scan image-processing algorithms. Our models predict the experimental conditions in which RBC velocity measurements are accurate. We illustrate the case of different vessel types and give the parameter space available for each of them. Last, we developed a software generating artificial line-scan images and used it to validate our models.
激光扫描显微镜被广泛用于通过对生理和病理血管进行线扫描来测量血液动力学。通过对虚线（即由具有不同方向的多个线段组成的线）进行扫描，它还允许同时监测血管直径动态或特定细胞的活性。从线扫描分析红细胞（RBC）速度需要特定的图像处理算法，如角度测量、线扫描粒子图像测速法（LSPIV）或线扫描图像的傅里叶变换。尽管测量的准确性会根据特定的实验参数（血管类型、RBC速度、扫描参数和图像信噪比）而有所不同，但这些图像处理算法给出准确测量的条件尚未得到充分表征。在此，我们为上述三种线扫描图像处理算法开发了数学模型。我们的模型预测了RBC速度测量准确的实验条件。我们说明了不同血管类型的情况，并给出了每种类型可用的参数空间。最后，我们开发了一个生成人工线扫描图像的软件，并使用它来验证我们的模型。
