Laser speckle contrast imaging (LSCI) is a non-scanning full-field hemodynamic imaging technology, which has the advantages of real-time, non-invasive, and high spatiotemporal resolution. It has become a widely used optical imaging technology for vascular visualization and dynamic blood flow monitoring. The Reflect-LSCI (R-LSCI) system is mostly used for the imaging of superficial blood vessels, exhibiting poor image quality when it comes to deep vascular visualization. Some studies have shown that Transmissive-LSCI (T-LSCI) has advantages in deep tissue imaging. At the same time, adaptive window space direction (awsdK) method has better imaging quality for deep blood vessels in R-LSCI. In this study, we used several LSCI methods processed in the spatial domain to compare the speckle images acquired by the R-LSCI system and the T-LSCI system. The results of comparative experiments show that in the T-LSCI, the awsdK also has the ability to improve the visualization of deep blood vessels without changing the relative velocity information. At the same time, the reflection speckle images and transmission speckle images were compared. The results showed that the T-LSCI was better than the R-LSCI in deep tissue imaging for a certain thickness of tissue
For specific cerebrovascular diseases such as epilepsy and Alzheimer's disease, it is urgent to detect the chronic changes of regional cerebral blood flow (rCBF) for a long time. interferometric diffusing wave spectroscopy (iDWS) based on optical heterodyne detection is expected to improve the detection sensitivity and reduce the interference of scalp blood flow. However, iDWS technology is difficult to achieve two-dimensional blood flow imaging. Therefore, this research proposes a interferometric diffusing speckle contrast imaging (iDSCI) system for monitoring rCBF, which combines an improved diffusion speckle contrast analysis method with iDWS technology. The dynamic phantom experiment demonstrated a good linear relationship between the reconstructed relative blood flow index and the actual flow velocity, and multi-parameter analysis validated the effectiveness of the iDSCI system in monitoring rCBF velocity, improving detection accuracy from 0.82 to 0.97. Therefore, the iDSCI system has the potential to provides a new theoretical basis for the early diagnosis and treatment of cerebrovascular diseases in the future.
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