  We present INSPIRE, a top-performing general-purpose method for deformable
image registration. INSPIRE extends our existing symmetric registration
framework based on distances combining intensity and spatial information to an
elastic B-splines based transformation model. We also present several
theoretical and algorithmic improvements which provide high computational
efficiency and thereby applicability of the framework in a wide range of real
scenarios. We show that the proposed method delivers both highly accurate as
well as stable and robust registration results. We evaluate the method on a
synthetic dataset created from retinal images, consisting of thin networks of
vessels, where INSPIRE exhibits excellent performance, substantially
outperforming the reference methods. We also evaluate the method on four
benchmark datasets of 3D images of brains, for a total of 2088 pairwise
registrations; a comparison with 15 other state-of-the-art methods reveals that
INSPIRE provides the best overall performance. Code is available at
github.com/MIDA-group/inspire.
