Atlas construction from high angular resolution diffusion imaging data represented by Gaussian Mixture fields

Groupwise image registration is an essential part of atlas construction which is a very import and challenging task in medical image analysis. In this paper, we present a novel atlas construction technique using a groupwise registration of high angular resolution diffusion (MR) imaging datasets each of which is represented by a Gaussian Mixture field. To solve the registration problem, an L₂ distance is used to measure the similarity between two Gaussian Mixtures, which leads to an energy function whosegradient can be computed in closed form. A projection method is developed to construct a “sharp” (not blurred) atlas from the result of this groupwise registration. Synthetic and real data experiments are presented to demonstrate the efficacy of the proposed method.