3-D Deformable Registration of Medical Images Using a Statistical Atlas

Abstract

Registration between 3-D images of human anatomies enables cross-subject diagnosis. However, innate differences in the appearance and location of anatomical structures between individuals make accurate registration difficult. We characterize such anatomical variations to achieve accurate registration. We represent anatomical variations in the form of statistical models, and embed these statistics into a 3-D digital brain atlas which we use as a reference. These models are built by registering a training set of brain MRI volumes with the atlas. This associates each voxel in the atlas with multi-dimensional distributions of variations in intensity and geometry of the training set. We evaluate statistical properties of these distributions to build a statistical atlas. When we register the statistical atlas with a particular subject, the embedded statistics function as prior knowledge to guide the deformation process. This allows the deformation to tolerate variations between individuals while retaining discrimination between different structures. This method gives an overall voxel mis-classification rate of 2.9% on 40 test cases; this is a 34% error reduction over the performance of our previous algorithm without using anatomical knowledge. Besides achieving accurate registration, statistical models of anatomical variations also enable quantitative study of anatomical differences between populations.