cmOCT is relatively insensitive to such noises compared to UHS-OMAG or svOCT because this technique employs normalized cross-correlation (NCC) using subimages to evaluate the degree of similarity (or dissimilarity) between inter B-scan frames.27 Assuming that reflectivity for static tissue is time invariant, the changes in amplitude between adjacent subimages ( and ) for static tissue are proportional to one common scaling factor, and thus, can be represented as ( is a scaling factor). For this reason, the NCC equation in 27 always yields 1, meaning that the static signals in the subimages are considered totally correlated, despite of their amplitude difference. However, the reflectivity of the moving blood is random variable, leading to the changes in amplitudes between adjacent subimages. Thus, each pixel in the image possesses different scaling factors, giving their NCC much lower than 1 (weak correlation). Under this circumstance, the high correlation values of static tissues can be picked up from the final correlation map by setting a proper threshold. Subsequently, the cmOCT image is relatively free from the static artifacts. Unfortunately, the background region or the static tissue area with low backscatter, which should have high correlation, tends to have lower correlation comparable to the vessel due to large influence of the noise, inducing decorrelation artifacts in the cmOCT image. On the other hand, UHS-OMAG has benefits of being insensitive to the noise in these low structural intensity areas compared to its cmOCT counterpart. Enfield et al.26 removed this noise by overlaying a binary mask of the OCT structural image to the correlation map in the cmOCT imaging. However, the selection of the binary threshold value is rather arbitrary, still leaving the strong static signals in the cmOCT image.