Simplifying intensity variance analysis for optical coherence tomography-based angiography

Abstract

Optical coherence tomography-based angiography (OCTA) is a high-resolution imaging technology for mapping microvascular networks in vivo. Intensity variance OCTA methods have been developed for blood flow analysis. However, the complex statistical calculations of intensity variances result in high computational costs. In this study, we developed statistical algorithms for simplifying estimates of the intensity variances, including the range, the mean error, and the maximum error algorithms. A rat cerebral cortex was imaged by the simplified algorithms and the conventional algorithm. The number of repeated samplings was compared for the intensity variance analysis. Then the image quality and the calculation time were assessed. The results show that the simplified algorithms can shorten the calculation time and generate microvascular networks with similar image quality compared to the conventional intensity variance OCTA algorithm.