We propose an efficient non-parametric diffeomorphic image registration algorithm based on Thirion’s demons algorithm. In the first part of this paper, we show that Thirion’s demons algorithm can be seen as an optimization procedure on the entire space of displacement fields. We provide strong theoretical roots to the different variants of Thirion’s demons algorithm. This analysis predicts a theoretical advantage for the symmetric forces variant of the demons algorithm. We show on controlled experiments that this advantage is confirmed in practice and yields a faster convergence. In the second part of this paper, we adapt the optimization procedure underlying the demons algorithm to a space of diffeomorphic transformations. In contrast to many diffeomorphic registration algorithms, our solution is computationally efficient since in practice it only replaces an addition of displacement fields by a few compositions. Our experiments show that in addition to being diffeomorphic, our algorithm provides results that are similar to the ones from the demons algorithm but with transformations that are much smoother and closer to the gold standard, available in controlled experiments, in terms of Jacobians.

Abstract. The algorithm described in this paper takes (a) two temporallyseparated CT scans, I1 and I2, and (b) a series of locations in I1, and it produces, for each location, an affine transformation mapping the locations and their immediate neighborhood from I1 to I2. It does this without deformable registration by using a combination of feature extraction, indexing, refinement and decision processes. Together these essentially “recognize ” the neighborhoods. We show on lung CT scans that this works at near interactive speeds, and is at least as accurate as the Diffeomorphic Demons algorithm [1]. The algorithm may be used both for diagnosis and treatment monitoring. 1

Evaluating geometrical accuracy of image registration methods in SPECT guided radiation therapy

journal homepage: www.elsevier.com/locate/media Location registration and recognition (LRR) for serial analysis of nodules