Evaluating multi-exposure speckle imaging estimates of absolute autocorrelation times

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.

Comparison of numerical-integration-based methods for blood flow estimation in diffuse correlation spectroscopy

BACKGROUND AND OBJECTIVE

Diffuse correlation spectroscopy (DCS) is an optical blood flow monitoring technology that has been utilized in various biomedical applications. In signal processing of DCS, nonlinear fitting of the experimental data and the theoretical model can be a hindrance in real-time blood flow monitoring. As one of the approaches to resolve the issue, INISg1, the inverse of numerical integration of squared g1 (a normalized electric field autocorrelation function), that could surpass the state-of-the-art technique at the time in terms of signal processing speed, has been introduced. While it is possible to implement INISg1 using various numerical integration methods, no relevant studies have been performed. Meanwhile, INISg1 was only tested within limited experimental conditions, which cannot guarantee the robustness of INISg1 in various experimental conditions. Thus, this study aims to introduce variants of INISg1 and perform a thorough comparison of the original INISg1 and its variants.

METHODS

In this study, based on the right Riemann sum (RR) and trapezoid rule (TR) of numerical integration, INISg1_RR and INISg1_TR are suggested. They are thoroughly compared with the original INISg1 using model-based simulations that offer us control of most of the experimental conditions, including integration time, β, and photon count rate.

RESULTS

Except for some extreme cases, INISg1 performed more robustly than INISg1_RR and INISg1_TR. However, in extreme conditions, variants of INISg1 performed better than INISg1. With the same condition, the signal processing speed of INISg1 was 1.63 and 1.98 times faster than INISg1_RR and INISg1_TR, respectively.

CONCLUSION

This study shows that INISg1 is robust in most cases and the study can be a guide for researchers using INISg1 and its variants in different types of DCS applications.

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

Laser speckle contrast imaging (LSCI) is a widefield imaging technique that enables high spatiotemporal resolution measurement of blood flow. Laser coherence, optical aberrations, and static scattering effects restrict LSCI to relative and qualitative measurements. Multi-exposure speckle imaging (MESI) is a quantitative extension of LSCI that accounts for these factors but has been limited to post-acquisition analysis due to long data processing times. Here we propose and test a real-time quasi-analytic solution to fitting MESI data, using both simulated and real-world data from a mouse model of photothrombotic stroke. This rapid estimation of multi-exposure imaging (REMI) enables processing of full-frame MESI images at up to 8 Hz with negligible errors relative to time-intensive least-squares methods. REMI opens the door to real-time, quantitative measures of perfusion change using simple optical systems.

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

Laser speckle contrast imaging (LSCI) is a widefield imaging technique that enables high spatiotemporal resolution measurement of blood flow. Laser coherence, optical aberrations, and static scattering effects restrict LSCI to relative and qualitative measurements. Multi-exposure speckle imaging (MESI) is a quantitative extension of LSCI that accounts for these factors but has been limited to post-acquisition analysis due to long data processing times. Here we propose and test a real-time quasi-analytic solution to fitting MESI data, using both simulated and real-world data from a mouse model of photothrombotic stroke. This rapid estimation of multi-exposure imaging (REMI) enables processing of full-frame MESI images at up to 8 Hz with negligible errors relative to time-intensive least-squares methods. REMI opens the door to real-time, quantitative measures of perfusion change using simple optical systems.

Intraoperative multi-exposure speckle imaging of cerebral blood flow

Multiple studies have demonstrated that laser speckle contrast imaging (LSCI) has high potential to be a valuable cerebral blood flow monitoring technique during neurosurgery. However, the quantitative accuracy and sensitivity of LSCI is limited, and highly dependent on the exposure time. An extension to LSCI called multi-exposure speckle imaging (MESI) overcomes these limitations, and was evaluated intraoperatively in patients undergoing brain tumor resection. This clinical study ( n = 8) recorded multiple exposure times from the same cortical tissue area spanning 0.5-20 ms, and evaluated images individually as single-exposure LSCI and jointly using the MESI model. This study demonstrated that the MESI estimates provided the broadest flow sensitivity for sampling the flow magnitude in the human brain, closely followed by the shorter exposure times. Conservation of flow analysis on vascular bifurcations was used to validate physiological accuracy, with highly conserved flow estimates (<10%) from both MESI and 1 ms LSCI ( n = 14 branches). The MESI model had high goodness-of-fit with proper image calibration and acquisition, and was used to monitor blood flow changes after tissue cautery. Results from this study demonstrate that intraoperative MESI can be performed with high quantitative accuracy and sensitivity for cerebral blood flow monitoring.

Reflectance-mode interferometric near-infrared spectroscopy quantifies brain absorption, scattering, and blood flow index in vivo

Interferometric near-infrared spectroscopy (iNIRS) is a new technique that measures time-of-flight- (TOF-) resolved autocorrelations in turbid media, enabling simultaneous estimation of optical and dynamical properties. Here, we demonstrate reflectance-mode iNIRS for noninvasive monitoring of a mouse brain in vivo. A method for more precise quantification with less static interference from superficial layers, based on separating static and dynamic components of the optical field autocorrelation, is presented. Absolute values of absorption, reduced scattering, and blood flow index (BFI) are measured, and changes in BFI and absorption are monitored during a hypercapnic challenge. Absorption changes from TOF-resolved iNIRS agree with absorption changes from continuous wave NIRS analysis, based on TOF-integrated light intensity changes, an effective path length, and the modified Beer-Lambert Law. Thus, iNIRS is a promising approach for quantitative and noninvasive monitoring of perfusion and optical properties in vivo.

Improving the estimation of flow speed for laser speckle imaging with single exposure time

Laser speckle contrast imaging is a full-field imaging technique for measuring blood flow by mapping the speckle contrast with high spatial and temporal resolution. However, the statically scattered light from stationary tissues seriously degrades the accuracy of flow speed estimation. In this Letter, we present a simple calibration approach to calculate the proportions of dynamically scattered light and correct the effect of static scattering with single exposure time. Both the phantom and animal experimental results suggest that this calibration approach has the ability to improve the estimation of the relative blood flow in the presence of static scattering.