Machine learning in multiexposure laser speckle contrast imaging can replace conventional laser Doppler flowmetry

Authors:
Open Access
Publisher website
Google Scholar
Abstract
The microcirculatory blood flow can be assessed using laser-based techniques, which indirectly quantify the Doppler shifts that occur when light is scattered by moving red blood cells. These techniques are commonly based on either a temporal1,2 or spatial3,4 analysis of the dynamic speckle pattern formed when Doppler shifted and nonshifted light is backscattered from a laser illuminated tissue and mixed on a detector. In laser Doppler flowmetry (LDF), an almost direct mapping of Doppler shifts can be attained by studying the frequency distribution of the temporal speckle fluctuations. This technique can produce perfusion estimations that are proportional to the blood flow speed if the Nyquist criterion for the photodetector current is fulfilled. Sampling rates well above 10 kHz are typically required, which has limited the LDF applicability to mainly include single point instruments capable of studying variations in tissue perfusion over time, or slow scanning systems measuring one point after the other. Laser speckle contrast imaging (LSCI) presents one way of overcoming the single point limitation while keeping a high time-resolution. The LSCI technique is based on a spatial analysis of the speckle dynamics, where Doppler effects are indirectly quantified as local blurring caused by speckle motion. The speckle blurring effect, measured as the local image contrast, depends on the exposure time of the camera. While short exposure times can capture fast variations related to high Doppler shifts, longer exposure times can be related to the degree of Doppler shifted light.5 Compared to LDF, there is no direct relationship between the distribution of Doppler frequencies and the speckle contrast for single exposure time LSCI.
Funding Information
  • Vetenskapsrådet (2014-6141)
  • VINNOVA (2016-02211)
  • Sweden’s Innovation Agency (2016-02211)
  • Sweden’s Innovation Agency VINNOVA (2016-02211)
  • Sweden’s Innovation Agency VINNOVA (2017-01435)

This publication has 27 references indexed in Scilit: