Vascular Atlas Formation Using a Vessel-to-Image Affine Registration Method

We have developed a method for forming vascular atlases using vascular distance maps and a novel vascular model-to-image registration method. Our atlas formation process begins with MR or CT angiogram data from a set of subjects. We extract blood vessels from those data using our tubular object segmentation method. One subject's vascular network model is then chosen as a template, and its vascular distance map (DM) image is computed. Each of the remaining vascular network models is then registered with the DM template using our vascular model-to-image affine registration method. The DM images from the registered vascular models are then computed. The mean and variance images formed from those registered DM images are the vascular atlas. In this paper we apply the atlas formation process to build atlases of normal brain and liver vasculature. We use Monte Carlo simulations to demonstrate the reliability of the underlying registration method. Additionally, we explain the clinical potential of those atlases and conduct z-score analyses to compare individuals with the atlases to detect abnormal vessels.