Assessing spatial resolution versus sensitivity from laser speckle contrast imaging: application to frequency analysis

Abstract

For blood perfusion monitoring, laser speckle contrast (LSC) imaging is a recent non-contact technique that has the characteristic of delivering noise-like speckled images. To exploit LSC images for quantitative physiological measurements, we developed an approach that implements controlled spatial averaging to reduce the detrimental impact of the noise and improve measurement sensitivity. By this approach, spatial resolution and measurement sensitivity can be traded-off in a flexible way depending on the quantitative prospect of the study. As an application, detectability of the cardiac activity from LSC images of forearm using power spectrum analysis is studied through the construction of spatial activity maps offering a window on the blood flow perfusion and its regional distribution. Comparisons with results obtained with signals of laser Doppler flowmetry probes are performed.

Multiscale Compression Entropy of Microvascular Blood FlowSignals: Comparison of Results from Laser Speckle Contrastand Laser Doppler Flowmetry Data in Healthy Subjects

“…This means that, for scale factors larger than three, LDF signals complexity becomes closer to the ones of LSCI time series with the largest ROI size. These results are in accordance with previous works where it has been reported that: (i) there are different dynamical patterns for LSCI and LDF data [26]; (ii) when the ROI size increases in LSCI data and when LSCI pixel values are averaged in ROI and followed with time, the patterns of the resulting time series approach the patterns of LDF signals [26,31,32,38]. Several studies have been carried on compression entropy [34,37,[39][40][41][42][43][44][45][46].…”

Segmentation of Renal Perfusion Signals From Laser Speckle Imaging Into Clusters With Phase Synchronized Dynamics

“…A Gaussian spatial filter (width of 8 pixels corresponding to -10 dB cutoff) was applied to each frame to increase the signal-noise ratio of the time-series at each pixel [16], and the filtered images were then spatially downsampled by a factor of 4 to reduce redundant information at adjacent pixels caused by the spatial filter. The size of the spatial filter was determined from a previous study where it was found to adequately identify autoregulation signals [12].…”

Accessing to arteriovenous blood flow dynamics response using combined laser speckle contrast imaging and skin optical clearing

“…If the change in vascular diameter or flow velocity caused by drug is sc C Δ , and the standard deviation of the changes is C sd , the signal-to-noise ratio (SNR) can be defined as follows [23,27]: Figure 2 demonstrates the typical white-light images, blood flow velocity maps and profiles of the flow velocity values along the horizontal white line. It can be found that neither the cutaneous microvasculature nor the blood flow information is visible through the turbid skin.…”

“…It has been becoming a fascinating tool to assess the cutaneous blood flow dynamics [18][19][20][21][22]. However, the turbid skin-induced static speckles conceals the dynamic information of the blood flow and reduces the imaging resolution and contrast, that makes it difficult to sensitively monitor the cutaneous blood flow dynamical response [23]. Fortunately, the tissue optical clearing technique [24][25][26] has shown a great potential to improve the performance of many optical imaging modalities [27][28][29][30][31][32][33][34][35][36].…”