Murali K

Laser speckle imaging modalities are receiving considerable attention for applications in both superficial and deep tissue imaging. This growing interest arises from their straightforward and cost-effective instrumentation, as well as the ease of computational implementation. Recent developments in this field require the calibration of devices, which often necessitates the simulation of dynamic laser speckles in tissue. Previous simulation models have predominantly relied on statistical tools such as copulas or Fourier transform methods based on diffraction theory. In our recent work, we introduced the use of Stochastic Differential Equations (SDEs) to model the temporal evolution of speckles, utilizing a predetermined probability density function and temporal autocorrelation. We further extended this simulation to create a calibrating phantom by inputting the stochastic time series data generated by SDEs into a piezo actuator. In the previous studies, the time evolution of each pixel was modeled independently using multiple SDEs. In the current work, we introduce a method for generating spatio-temporal dynamics of speckles characterized by predefined spatial and temporal correlation structures. This approach integrates SDE with Cholesky decomposition and inverse sampling method to achieve the desired spatial correlation profiles. The simulation results are obtained for practically feasible experimental parameters, effectively generating speckles that closely approximate the values observed in experimental settings.

Significance: The imaging of cerebral blood flow in small rodents is crucial for a better understanding of brain functions in healthy and diseased conditions. Existing methods often struggle to provide both superficial and deep tissue blood flow measurements in a non-invasive, flexible, and reliable manner, creating a need for an integrated platform that addresses these limitations.

Aim: We aim to design and develop a multi-modal laser speckle–based imaging platform and associated algorithms to image superficial and deep tissue cerebral blood flow in small rodents.

Approach: A modular design has been adopted to integrate laser speckle contrast imaging and multi-speckle diffuse correlation tomography to a single cerebral blood flow imaging platform for small rodents with an independent module for animal holding and handling. A topographic imaging method, equipped with a filter to remove surface artifacts, was incorporated to image cerebral blood flow changes in response to forepaw and olfactory stimuli activations, with the skull and scalp kept intact.

Results: A significant increase in blood flow was found in the olfactory bulbs of mice post-stimulation by various odors (p<0.01). Similarly, forepaw stimulation resulted in a significant increase in blood flow in the contralateral side of the somatosensory cortex with the application of the filter for skull and scalp intact, skull intact, and skull removed cases (p<0.01).

Conclusions: We have validated our system through functional studies, demonstrating its capability to detect enhanced blood flow changes across the olfactory bulbs and somatosensory cortex in rodents with potential for broad applications in preclinical research.

Highlights:
• Both fluorescence intensity and lifetime increase after fragmentation of the Fab’ NIR probe
• Fluorescence lifetime contrast increases upon internalization of Fab’ NIR probe
• CRISPRi yielded two times lower EGFR expression in triple-negative breast cancer cells
• Fab’ probe showed the feasibility of CRISPRi effect imaging in vivo at an early time point

Summary
Clinical imaging-assisted oncosurgical navigation requires cancer-specific miniaturized optical imaging probes. We report a near-infrared (NIR) Fab’-based epidermal growth factor receptor (EGFR)-specific probe carrying 3 NIR fluorophores (Fab’-800CW), which retained high-affinity binding to EGFR ectodomain (equilibrium KDE = 1 nM). Fab’-800CW showed a robust 4-times gain of fluorescence intensity (FI) and a 20% lifetime (FLT) increase under the conditions mimicking intracellular degradation. The probe was tested by using triple-negative breast cancer (TNBC) cell lines obtained by applying CRISPR interference (CRISPRi) effect of EGFR-targeting sgRNA and dCas9-KRAB chimera coexpression in MDA-MB-231 cells (WT cells). FI imaging in cell culture proved a 50% EGFR expression attenuation by CRISPRi. FI imaging in animals harboring attenuated or WT TNBC tumors with ex vivo corroboration identified differences between WT and CRISPRi tumors FI at 30 min post injection. Our results suggest the feasibility of EGFR expression imaging using a Fab’-based probe relevant for imaging-guided cancer surgery.

We introduce a novel method to design and implement a tunable dynamical tissue phantom for laser speckle-based in-vivo blood flow imaging. This approach relies on stochastic differential equations (SDE) to control a piezoelectric actuator which, upon illuminated with a laser source, generates speckles of pre-defined probability density function and auto-correlation. The validation experiments show that the phantom can generate dynamic speckles that closely replicate both surfaces as well as deep tissue blood flow for a reasonably wide range and accuracy.

Laser speckle-based blood flow imaging is a well-accepted and widely used method for pre-clinical and clinical applications. Although it was introduced as a method to measure only superficial blood flow (< 1mm depth), several recently introduced variants resulted in measuring deep tissue blood flow (a few cm) as well. A means of simulating laser speckles is often necessary for the analysis and development of these imaging modalities, as evident from many such attempts towards developing simulation tools in the past. Such methods often employ Fourier transforms or statistical tools to simulate speckles with desired statistical properties. We present the first method to use a stochastic differential equation to generate laser speckles with a pre-determined probability density function and a temporal auto-correlation. The method allows the choice of apriori gamma distribution along with simple exponential or more complex temporal auto-correlation statistics for simulated speckles, making it suitable for different blood flow profiles. In contrast to the existing methods that often generate speckles associated with superficial flow, we simulate both superficial and diffuse speckles leading to applications in deep tissue blood flow imaging. In addition, we have also incorporated appropriate models for noise associated with the detectors to simulate realistic speckles. We have validated our model by comparing the simulated speckles with those obtained from in-vivo studies in mice and healthy human subject.

A spatially weighted filter applied to both the measurement and the Jacobian is proposed for high-density diffuse correlation tomography (DCT) to remove unwanted extracerebral interferences and artefacts along with better depth localization in the reconstructed blood flow images. High-density DCT is implemented by appropriate modification of recently introduced Multi-speckle Diffuse Correlation Spectroscopy (M-DCS) system. Additionally, we have used autocorrelation measurements at multiple delay-times in an iterative manner to improve the reconstruction results. The proposed scheme has been validated by simulations, phantom experiments and in-vivo human experiments.

A computationally simpler algorithm to reconstruct the optical property distribution of turbid media using diffuse optical tomographic principles is presented. The proposed algorithm eliminates the requirement of large Jacobian matrix inversion which otherwise is essential for tomographic imaging. The most significant Jacobians are identified based on proper thresholding of the measurement and the intersection of these Jacobians gives the approximate spatial location of the inhomogeneity. The algorithm is tested and optimized using simulations and further validated using tissue-mimicking phantom-based experiments and in-vivo small-animal experiments.

We present a multi-speckle diffuse correlation spectroscopy (DCS) system for measuring cerebral blood flow in the healthy adult human brain. In contrast to the need for a high frame rate camera to measure the multi-speckle intensity auto-correlation, we employ a low frame rate camera to measure the auto-correlation using the recently introduced multi-step volterra integral method (MVIM). The results are validated by comparison against the blood flow measured using standard DCS system.

We establish the equivalence between laser speckle contrast-based and diffuse correlation spectroscopy methods in in vivo imaging of blood flow using the Volterra integral equation theory. We further substantiate the need of regularized fitting while employing the multiexposure speckle contrast imaging to recover autocorrelation function.

A multi-step Volterra integral equation-based algorithm was developed to measure the electric field auto-correlation function from multi-exposure speckle contrast data. This enabled us to derive an estimate of the full diffuse correlation spectroscopy data-type from a low-cost, camera-based system. This method is equally applicable for both single and multiple scattering field auto-correlation models. The feasibility of the system and method was verified using simulation studies, tissue mimicking phantoms and subsequently in in vivo experiments.