LCC-Demons: a robust and accurate dieomorphic registration algorithm

Non-linear registration is a key instrument for computational anatomy to study the morphology of organs and tissues. However, in order to be an eective instrument for the clinical practice, registration algorithms must be computationally ecient, accurate and most importantly robust to the multiple biases aecting medical images. In this work we propose a fast and robust registration framework based on the log-Demons dieomorphic registration algorithm. The transformation is parameterized by stationary velocity elds (SVFs), and the similarity metric implements a symmetric local correlation coecient (LCC). Moreover, we show how the SVF setting provides a stable and consistent numerical scheme for the computation of the Jacobian determinant and the ux of the deformation across the boundaries of a given region. Thus, it provides a robust evaluation of spatial changes. We tested the LCC-Demons in the inter-subject registration setting, by comparing with state-of-the-art registration algorithms on public available datasets, and in the intra-subject longitudinal registration problem, for the statistically powered measurements of the longitudinal atrophy in Alzheimer’s disease. Experimental results show that LCC-Demons is a generic, exible, ecient