Assessment of the vasculature is critical for overall success in cranial vascular neurological surgery procedures. Although several methods of monitoring cortical perfusion intraoperatively are available, not all are appropriate or convenient in a surgical environment. Recently, 2 optical methods of care have emerged that are able to obtain high spatial resolution images with easily implemented instrumentation: indocyanine green (ICG) angiography and laser speckle contrast imaging (LSCI).To evaluate the usefulness of ICG and LSCI in measuring vessel perfusion.An experimental setup was developed that simultaneously collects measurements of ICG fluorescence and LSCI in a rodent model. A 785-nm laser diode was used for both excitation of the ICG dye and the LSCI illumination. A photothrombotic clot model was used to occlude specific vessels within the field of view to enable comparison of the 2 methods for monitoring vessel perfusion.The induced blood flow change demonstrated that ICG is an excellent method for visualizing the volume and type of vessel at a single point in time; however, it is not always an accurate representation of blood flow. In contrast, LSCI provides a continuous and accurate measurement of blood flow changes without the need of an external contrast agent.These 2 methods should be used together to obtain a complete understanding of tissue perfusion.

Post-surgical hypoparathyroidism and hypocalcemia are known to occur after nearly 50% of all thyroid surgeries as a result of accidental disruption of blood supply to healthy parathyroid glands, which are responsible for regulating calcium. However, there are currently no clinical methods for accurately identifying compromised glands and the surgeon relies on visual assessment alone to determine if any gland(s) should be excised and auto-transplanted. Here, we present Laser Speckle Contrast Imaging (LSCI) for real-time assessment of parathyroid viability. Taking an experienced surgeon's visual assessment as the gold standard, LSCI can be used to distinguish between well vascularized (n = 32) and compromised (n = 27) parathyroid glands during thyroid surgery with an accuracy of 91.5%. Ability to detect vascular compromise with LSCI was validated in parathyroidectomies. Results showed that this technique is able to detect parathyroid gland devascularization before it is visually apparent to the surgeon. Measurements can be performed in real-time and without the need to turn off operating room lights. LSCI shows promise as a real-time, contrast-free, objective method for helping reduce hypoparathyroidism after thyroid surgery.

When a biological tissue is illuminated with coherent light, an interference pattern will be formed at the detector, the so-called speckle pattern. Laser speckle contrast imaging (LSCI) is a technique based on the dynamic change in this backscattered light as a result of interaction with red blood cells. It can be used to visualize perfusion in various tissues and, even though this technique has been extensively described in the literature, the actual clinical implementation lags behind. We provide an overview of LSCI as a tool to image tissue perfusion. We present a brief introduction to the theory, review clinical studies from various medical fields, and discuss current limitations impeding clinical acceptance.

Temporal-statistical analysis of laser-speckle image (TS-LSI) preserves the original spatial resolution, in contrast to conventional spatial-statistical analysis (SS-LSI). Concerns have been raised regarding the temporal independency of TS-LSI signals and its insensitivity toward stationary-speckle contamination. Our results from flow phantoms and in vivo rat retinas demonstrated that the TS-LSI signals are temporally statistically independent and TS-LSI minimizes stationary-speckle contamination. The latter is because the stationary speckle is "non-random" and thus non-ergodic where the temporal average of stationary speckle needs not equal its spatial ensemble average. TS-LSI detects blood flow in smaller blood vessels and is less susceptible to stationary-speckle artifacts.

Laser speckle imaging (LSI) has been widely used for in vivo detecting cerebral blood flow (CBF) under various physiological and pathological conditions. So far, nearly all literature on in vivo LSI does not consider the influence of disturbances due to respiration and/or heart beating of animals. In this paper, we analyze how such physiologic motions affect the spatial resolution of the conventional laser speckle contrast analysis (LASCA). We propose a registered laser speckle contrast analysis (rLASCA) method which first registers raw speckle images with a 3 x 3 convolution kernel, normalized correlation metric and cubic B-spline interpolator, and then constructs the contrast image for CBF. rLASCA not only significantly improves the distinguishability of small vessels, but also efficiently suppresses the noises induced by respiration and/or heart beating. In an application of imaging the angiogenesis of rat's brain tumor, rLASCA outperformed the conventional LASCA in providing a much higher resolution for new small vessels. As a processing method for LSI, rLASCA can be directly applied to other LSI experiments where the disturbances from different sources (like respiration, heart beating) exist.

Laser speckle contrast imaging (LSCI) is a full-field, noncontact imaging technology for mapping blood flow with high spatio-temporal resolution, in which the speckle contrast can be estimated either in spatial domain or temporal domain. Temporal LSCI (tLSCI) provides higher spatial resolution than spatial domain does. However, when the number of sampling frames is limited, it is difficult to obtain accurate blood flow velocity owing to the significant statistical noise. The widely used spatially averaged tLSCI (savg-tLSCI) usually requires a large number of sampling frames to obtain acceptable denoising performance. Here, based on the nonlocal filtering strategy of block-matching and three-dimensional transform-domain collaborative filtering (BM3D), Manhattan distance-based adaptive BM3D (MD-ABM3D) is proposed to effectively manage the complicated inhomogeneous noise in tLSCI image and improve the signal-to-noise ratio. Manhattan distance improves the accuracy of the block matching in strong noise, and the adaptive algorithm adapts to the inhomogeneous noise and estimates suitable parameters for improved denoising. MD-ABM3D improves 4.91 dB in peak signal-to-noise ratio relative to savg-tLSCI. It achieves stability for denoising tLSCI image with different temporal windows. The image-quality evaluation of MD-ABM3D for tLSCI (t = 20 frames) equals that of savg-tLSCI (t = 60 frames). It achieves high signal-to-noise ratio with a reduced number of sampling frames. A reduced number of sampling frames are more practical for biomedical applications. It also offers higher temporal resolution and less disturbance from the motion of the moving object.

Laser speckle contrast analysis (LASCA) is limited to being a qualitative method for the measurement of blood flow and tissue perfusion as it is sensitive to the measurement configuration. The signal intensity is one of the parameters that can affect the contrast values due to the quantization of the signals by the camera and analog-to-digital converter (ADC). In this paper we deduce the theoretical relationship between signal intensity and contrast values based on the probability density function (PDF) of the speckle pattern and simplify it to a rational function. A simple method to correct this contrast error is suggested. The experimental results demonstrate that this relationship can effectively compensate the bias in contrast values induced by the quantized signal intensity and correct for bias induced by signal intensity variations across the field of view.