High SNR imaging of cerebral blood flow based on anisotropic diffusion

As a two-dimensional, high-resolution, minimal-invasive monitoring method, laser speckle contrast imaging (LSCI) is widely used to measure the relative speed of cerebral blood flow (CBF) under different physiological and pathological states, i.e. before and after acupuncture. During the last decade, many improved methods (spatial, temporal, and several hybrid methods) for better estimating the contrast values have been proposed based on the updated theoretical explanation of speckle phenomenon. Temporal LSCI method is used to obtain high spatial resolution contrast image with limited temporal resolution. However, the temporal contrast image still contains much noise. There were very few studies of post-processing methods to further improve the SNR of contrast image. Based on the random property of speckle, characteristics of noise in tissue area and vessel areas are different and thus should be treated differently in smoothing and/or filtering when improving the SNR. In this study, we proposed to improve the SNR of contrast image based on anisotropic diffusion which de-noises the contrast image with local gradient information. Simulation and animal studies were presented to evaluate the SNR improvement of contrast image.