New speckle analysis algorithm for flow visualization in optical coherence tomography images

Optical Coherence Tomography (OCT) is a noninvasive technique capable of generating in vivo high-resolution images. However, OCT images are degraded by a granular and random noise called speckle. Nevertheless, such a noise may be used to gather information regarding the sample, as is exploited by techniques like Speckle Variance – OCT (SV-OCT). SV-OCT is widely used in the literature, but the variance calculation is computationally expensive. Therefore, we propose a new algorithm to employ speckle in identifying flow based on the evaluation of intensity fluctuation between two consecutively acquired OCT images. Our results were compared to those obtained by traditional method of Speckle Variance to demonstrate the feasibility of the technique. Both algorithms were applied to series of OCT images from a microchannel flow phantom, as well as from a biological tissue with blood flow. The results obtained by our method are in good agreement with those from SV-OCT. We've also analyzed the performance of both algorithms, registering the processing time and memory use. Our method performed 31% faster with the same use of memory. Therefore, we demonstrated a new method to map flow on OCT images.