Laser speckle imaging (LSI) has been widely used for in vivo detecting cerebral blood flow (CBF) under various physiological and pathological conditions. So far, nearly all literature on in vivo LSI does not consider the influence of disturbances due to respiration and/or heart beating of animals. In this paper, we analyze how such physiologic motions affect the spatial resolution of the conventional laser speckle contrast analysis (LASCA). We propose a registered laser speckle contrast analysis (rLASCA) method which first registers raw speckle images with a 3 x 3 convolution kernel, normalized correlation metric and cubic B-spline interpolator, and then constructs the contrast image for CBF. rLASCA not only significantly improves the distinguishability of small vessels, but also efficiently suppresses the noises induced by respiration and/or heart beating. In an application of imaging the angiogenesis of rat's brain tumor, rLASCA outperformed the conventional LASCA in providing a much higher resolution for new small vessels. As a processing method for LSI, rLASCA can be directly applied to other LSI experiments where the disturbances from different sources (like respiration, heart beating) exist.