Demons algorithms for fluid and curvature registration

Authors:
Publisher website
Google Scholar
Abstract
As the number of clinical applications requiring nonrigid image registration continues to grow, it is important to design registration algorithms that not only build on the best available theory, but also are computationally efficient. Thirion's Demons algorithm [1] estimates nonrigid deformations by successively estimating force vectors that drive the deformation toward alignment, and then smoothing the force vectors by convolution with a Gaussian kernel. It essentially approximates a deformation under diffusion regularization [2], and it is a popular choice of algorithm for nonrigid registration because of its linear computational complexity and ease of implementation. In this article, we show how the Demons algorithm can be generalized to handle other common regularizers, yielding O(n) algorithms that employ Gaussian convolution for elastic, fluid, and curvature registration. We compare the speed of the proposed algorithms with algorithms based on Fourier methods [3] for registering serial chest CT studies.