  We present INSPIRE, a top-performing general-purpose method for deformable
image registration. INSPIRE brings distance measures which combine intensity
and spatial information into an elastic B-splines-based transformation model
and incorporates an inverse inconsistency penalization supporting symmetric
registration performance. We introduce several theoretical and algorithmic
solutions which provide high computational efficiency and thereby applicability
of the proposed framework in a wide range of real scenarios. We show that
INSPIRE delivers highly accurate, as well as stable and robust registration
results. We evaluate the method on a 2D dataset created from retinal images,
characterized by presence of networks of thin structures. Here INSPIRE exhibits
excellent performance, substantially outperforming the widely used reference
methods. {We also evaluate INSPIRE on the Fundus Image Registration Dataset
(FIRE), which consists of 134 pairs of separately acquired retinal images.
INSPIRE exhibits excellent performance on the FIRE dataset, substantially
outperforming several domain-specific methods.} We also evaluate the method on
four benchmark datasets of 3D magnetic resonance images of brains, for a total
of 2088 pairwise registrations. A comparison with 17 other state-of-the-art
methods reveals that INSPIRE provides the best overall performance. Code is
available at http://github.com/MIDA-group/inspire
