Due to their computational efficiency and other salient properties, B-splines form the basis not only in comprising the de facto standard for curve and surface representation but also for various nonrigid registration techniques frequently employed in medical image analysis. These registration techniques fall under the rubric of Free-Form Deformation (FFD) approaches in which the object to be registered is embedded within a B-spline object. The deformation of the B-spline object represents the transformation of the registration. Representative, and often cited within the relevant community, of this class of techniques is the formulation of Rueckert et. al  who employed cubic splines with normalized mutual information to study breast deformation. Similar techniques from various groups provided incremental novelty in the form of disparate explicit regularization terms as well as the employment of various image metrics and tailored optimization methods. For several algorithms, the underlying gradient-based optimization retained its essential characteristics since Rueckert's incarnation. We assert that such a straightforward gradient-learning is suboptimal in certain cases and to remedy this sub-optimality, we propose a fitting-based strategy for registration in the spirit of Thirion 's Demons  and directly manipulated free-form deformations , which takes advantage of our previously developed generalized B-spline fitting algorithm .