Transmissive-detected laser speckle contrast imaging for blood flow monitoring in thick tissue: from Monte Carlo simulation to experimental demonstration

Laser speckle contrast imaging (LSCI) is a powerful tool to monitor blood flow distribution and has been widely used in studies of microcirculation, both for animal and clinical applications. Conventionally, LSCI usually works on reflective-detected mode. However, it could provide promising temporal and spatial resolution for in vivo applications only with the assistance of various tissue windows, otherwise, the overlarge superficial static speckle would extremely limit its contrast and resolution. Here, we systematically investigated the capability of transmissive-detected LSCI (TR-LSCI) for blood flow monitoring in thick tissue. Using Monte Carlo simulation, we theoretically compared the performance of transmissive and reflective detection. It was found that the reflective-detected mode was better when the target layer was at the very surface, but the imaging quality would rapidly decrease with imaging depth, while the transmissive-detected mode could obtain a much stronger signal-to-background ratio (SBR) for thick tissue. We further proved by tissue phantom, animal, and human experiments that in a certain thickness of tissue, TR-LSCI showed remarkably better performance for thick-tissue imaging, and the imaging quality would be further improved if the use of longer wavelengths of near-infrared light. Therefore, both theoretical and experimental results demonstrate that TR-LSCI is capable of obtaining thick-tissue blood flow information and holds great potential in the field of microcirculation research.

Real-Time Cerebral Vessel Segmentation in Laser Speckle Contrast Image Based on Unsupervised Domain Adaptation

Laser speckle contrast imaging (LSCI) is a full-field, high spatiotemporal resolution and low-cost optical technique for measuring blood flow, which has been successfully used for neurovascular imaging. However, due to the low signal–noise ratio and the relatively small sizes, segmenting the cerebral vessels in LSCI has always been a technical challenge. Recently, deep learning has shown its advantages in vascular segmentation. Nonetheless, ground truth by manual labeling is usually required for training the network, which makes it difficult to implement in practice. In this manuscript, we proposed a deep learning-based method for real-time cerebral vessel segmentation of LSCI without ground truth labels, which could be further integrated into intraoperative blood vessel imaging system. Synthetic LSCI images were obtained with a synthesis network from LSCI images and public labeled dataset of Digital Retinal Images for Vessel Extraction, which were then used to train the segmentation network. Using matching strategies to reduce the size discrepancy between retinal images and laser speckle contrast images, we could further significantly improve image synthesis and segmentation performance. In the testing LSCI images of rodent cerebral vessels, the proposed method resulted in a dice similarity coefficient of over 75%.

Line Scan Spatial Speckle Contrast Imaging and Its Application in Blood Flow Imaging

Laser speckle imaging has been an indispensable tool for visualizing blood flow in biomedical applications. We proposed a novel design of the laser speckle imaging system, which combines confocal illumination and detection with various speckle analysis methods. The system can be operated by three imaging modes. One is surface illumination laser speckle contrast imaging (SI-LSCI) and the other two are line scan temporal speckle contrast imaging (LS-TSCI) and line scan spatial speckle contrast imaging (LS-SSCI). The experimental results of flow phantoms have validated the mixture model, which combines the Lorentzian and Gaussian models to describe the simultaneous existence of both Brownian motions and ordered flow. Our experimental results of in vivo chick embryos demonstrate that LS-SSCI maintains high temporal resolution and is less affected by motion artifacts. LS-SSCI can provide better image quality for in vivo imaging blood chick embryos than LS-TSCI. Furthermore, the experiential results present that LS-SSCI can detect and quantify the blood flow change during vascular clipping, and shows great potential in diagnosing vascular diseases, such as angiosclerosis, angiostenosis, or angiemphraxis.