Abstract
A novel method for optimizing fast camera parameters to sense flow dynamics is presented. A wide-field statistic of the temporal auto-correlation intensity function from sample back-scattered laser light can be obtained from the high-end fast cameras that have come on to the market in recent years. Although these statistics can reveal flow dynamics within different sample regions, these cameras can be very costly. Here we investigated the impact of several key camera features such as camera frame rate, sensor exposure time, etc, on the output data (auto-correlation decay time and function fit models). The post-processing algorithm steps are described in detail, followed by the findings from in-vitro and in-vivo experiments investigating ways to re-leaf the camera parameters. The experimental results define fast-camera minimum specification requirements for the correct monitoring of normal blood flow conditions. These findings thus contribute to a better understanding of the impact of each parameter on speckle statistics and can contribute to customizing cheaper hardware to specific needs without compromising on accuracy.
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