Dual-exposure temporal laser speckle imaging for simultaneously accessing microvascular blood perfusion and angiography.

Laser speckle contrast imaging (LSCI) has gained significant attention in the biomedical field for its ability to map the spatio-temporal dynamics of blood perfusion in vivo. However, LSCI faces difficulties in accurately resolving blood perfusion in microvessels. Although the transmissive detecting geometry can improve the spatial resolution of tissue imaging, ballistic photons directly transmitting forward through tissue without scattering will cause misestimating in the flow speed by LSCI because of the lack of a quantitative theoretical model of transmissvie LSCI. Here, we develop a model of temporal LSCI which accounts for the effect of nonscattered light on estimating decorrelation time. Based on this model, we further propose a dual-exposure temporal laser speckle imaging method (dEtLSCI) to correct the overestimation of background speed when performing traditional transmissive LSCI, and reconstruct microvascular angiography using the scattered component extracted from total transmitted light. Experimental results demonstrated that our new method opens an opportunity for LSCI to simultaneously resolve the blood vessels morphology and blood flow speed at microvascular level in various contexts, ranging from the drug-induced vascular response to angiogenesis and the blood perfusion monitoring during tumor growth.

Quantitative laser speckle auto-inverse covariance imaging for robust estimation of blood flow.

We present a quantitative model to provide robust estimation of the decorrelation time using laser speckle auto-inverse covariance. It has the advantages of independence from the statistical sample size, speckle size, static scattering, and detector noise. We have shown cerebral blood flow imaging through an intact mouse skull using this model. Phantom experiments and two animal models, middle cerebral artery occlusion, and cortical spreading depression were used to evaluate its performance.

Extendable, large-field multi-modal optical imaging system for measuring tissue hemodynamics.

Simultaneous imaging of multiple hemodynamic parameters helps to evaluate the physiological and pathological status of biological tissue. To achieve multimodal hemodynamics imaging with a large field of view, an infinite conjugate relay lens system compatible with the standard C-Mount camera lens is designed to adapt one camera lens with multiple CCD/CMOS cameras for simultaneously multi-wavelength imaging. Using this relay lens system, dual wavelength reflectance imaging and laser speckle contrast imaging were combined to simultaneously detect the changes in blood flow, oxygenation, and hemoglobin concentrations. To improve the accuracy of hemoglobin concentration measurement with an LED illumination source, an integral algorithm is proposed that accounts for the dependence of differential pathlength factors (DPF) on hemoglobin concentrations and the integral effect of both the emission spectrum of the light source and the spectrum response of the detector. The imaging system is validated by both phantom and in vivo experiments, including the arterial occlusion, and the detection of blood volume pulse (BVP) and blood flow pulse (BFP) signal in human subjects. The system helps in the exploration of macroscopic tissue hemodynamics.

Dilated Residual Learning With Skip Connections for Real-Time Denoising of Laser Speckle Imaging of Blood Flow in a Log-Transformed Domain.

Laser speckle contrast imaging (LSCI) is a wide-field and noncontact imaging technology for mapping blood flow. Although the denoising method based on block-matching and three-dimensional transform-domain collaborative filtering (BM3D) was proposed to improve its signal-to-noise ratio (SNR) significantly, the processing time makes it difficult to realize real-time denoising. Furthermore, it is still difficult to obtain an acceptable level of SNR with a few raw speckle images given the presence of significant noise and artifacts. A feed-forward denoising convolutional neural network (DnCNN) achieves state-of-the-art performance in denoising nature images and is efficiently accelerated by GPU. However, it performs poorly in learning with original speckle contrast images of LSCI owing to the inhomogeneous noise distribution. Therefore, we propose training DnCNN for LSCI in a log-transformed domain to improve training accuracy and it achieves an improvement of 5.13 dB in the peak signal-to-noise ratio (PSNR). To decrease the inference time and improve denoising performance, we further propose a dilated deep residual learning network with skip connections (DRSNet). The image-quality evaluations of DRSNet with five raw speckle images outperform that of spatially average denoising with 20 raw speckle images. DRSNet takes 35 ms (i.e., 28 frames per second) for denoising a blood flow image with 486×648 pixels on an NVIDIA 1070 GPU, which is approximately 2.5 times faster than DnCNN. In the test sets, DRSNet also improves 0.15 dB in the PSNR than that of DnCNN. The proposed network shows good potential in real-time monitoring of blood flow for biomedical applications.

Laser speckle auto-inverse covariance imaging for mean-invariant estimation of blood flow.

Laser speckle contrast imaging maps the changes in blood flow by estimating the decorrelation time of a scattered light field. However, speckle contrast is a biased statistics estimator that results in a theoretic bias between its expected value and the true value. Moreover, the average of speckle contrast depends on the statistical sampling size, which further hinders the estimation of decorrelation time from speckle contrast. Here, we present a new, to the best of our knowledge, unbiased statistics analysis based on auto-inverse covariance to improve the estimation of decorrelation time using laser speckle. Theoretical and experimental results demonstrate that the speckle auto-inverse covariance analysis is mean-invariant, so that the average of the estimation is not dependent on the sampling size. Furthermore, it can produce less statistical fluctuation, especially for slow flow, and consume less computation time than that of speckle contrast analysis.

Fluctuations of temporal contrast in laser speckle imaging of blood flow.

Laser speckle contrast imaging can be used to estimate changes in blood flow velocity in either the spatial or temporal domain. Temporal speckle contrast analysis provides higher spatial resolution than spatial speckle contrast does. However, owing to limitations of the statistical sample size in practical applications, the speckle contrast obtained consistently fluctuates around an accurate value. It is important to reveal the quantitative relationship between the statistical sample size and fluctuations of temporal speckle contrast. In this Letter, we present a new model for temporal speckle contrast that accounts for the effects of statistical size owing to the finite frames of the speckle images used for temporal analysis. Furthermore, an expression for estimating the fluctuations of temporal speckle contrast is derived. Both phantom and animal experiment results support our theoretical model.