Quasi-analytic solution for real-time multi-exposure speckle imaging of tissue perfusion

Choice of numerical implementation of spatial contrast calculation impacts microcirculation quantitation in laser speckle contrast imaging

Significance

Laser speckle contrast imaging (LSCI) allows noninvasive imaging of microcirculation. Its scope of clinical applications is growing, yet the literature lacks a comparison of the accuracy of methods used to compute the spatial contrastK s from which the blood flow index is derived.

Aim

We aim to evaluate the impact on flow quantitation of different computational approaches used to deriveK s .

Approach

We compare numerical calculation ofK s in Python and ImageJ applied to noise-free simulated data and to experimental data acquired in vivo in anesthetized mice. The estimation of the decorrelation timeτ c , inversely proportional to the blood flow index, is carried out following two approaches: LSCI asymptotic estimation and fitting the multiple exposure speckle imaging (MESI) model toK s ( T ) .

Results

For simulation data, we found variations of up to 58% for the blood flow index in the LSCI approach. Nonlinear fitting of the MESI model was less affected with discrepancies of only a few percent. Considering experimental data, the LSCI approximation led toK s with relative differences (up to 35%) depending on the calculation methods. The noise and limited exposure time strongly limited the accuracy of the LSCI asymptotic estimation. Adjustment of the MESI model to the data led to consistent values ofτ c in the 0.05 to 1 ms range with significant variations depending on the method used to calculateK s .

Conclusions

Numerical methods used to calculateK s should be precisely acknowledged and validated against direct calculation to ensure accuracy. Uniform filter approach leads to accurateK s values and is 100 times more computationally efficient than the D i r e c t calculation. Other investigated methods lead to various levels of errors in flow index estimation using LSCI. Errors are minimized using larger kernels. MESI derivation ofτ c is not immune but less affected by such methodological biases.

Dynamic Light Scattering in Biomedical Applications: feature issue introduction

The feature Issue on "Dynamic Light Scattering in Biomedical Applications" presents a compilation of research breakthroughs and technological advancements that have shaped the field of biophotonics, particularly in the non-invasive exploration of biological tissues. Highlighting the significance of dynamic light scattering (DLS) alongside techniques like laser Doppler flowmetry (LDF), diffusing wave spectroscopy (DWS), and laser speckle contrast imaging (LSCI), this issue underscores the versatile applications of these methods in capturing the intricate dynamics of microcirculatory blood flow across various tissues. Contributions explore developments in fluorescence tomography, the integration of machine learning for data processing, enhancements in microscopy for cancer detection, and novel approaches in optical biophysics, among others. Innovations featured include a high-resolution speckle contrast tomography system for deep blood flow imaging, a rapid estimation technique for real-time tissue perfusion imaging, and the use of convolutional neural networks for efficient blood flow mapping. Additionally, studies delve into the impact of skin strain on spectral reflectance, the sensitivity of cerebral blood flow measurement techniques, and the potential of photobiomodulation for enhancing brain function. This issue not only showcases the latest theoretical and experimental strides in DLS-based imaging but also anticipates the continued evolution of these modalities for groundbreaking applications in disease detection, diagnosis, and monitoring, marking a pivotal contribution to the field of biomedical optics.

Improvements and validation of spatiotemporal speckle correlation model for rolling shutter speckle imaging

Rolling shutter speckle imaging (RSSI) is a single-shot imaging technique that directly measures the temporal dynamics of the scattering media using a low-cost rolling shutter image sensor and vertically elongated speckles. In this paper, we derive and validate a complete spatiotemporal intensity correlation (STIC) model for RSSI, which describes the row-by-row correlation of the dynamic speckles measured with a rolling shutter in the presence of static scattering. Our new model accounts for the finite exposure time of the detector, which can be longer than the sampling interval in RSSI. We derive a comprehensive model that works for all correlation times of rolling shutter measurements. As a result, we can correctly utilize all data points in RSSI, which improves the measurement accuracy and ranges of speckle decorrelation time and dynamic scattering fraction, as demonstrated by phantom experiments. With simulations and experiments, we provide an understanding of the design parameters of RSSI and the measurement range of the speckle dynamics.