Explicit B-spline regularization in diffeomorphic image registration - Tustison, Nicholas J.; Avants, Brian (Frontiers in Neuroinformatics, 2013)
Diffeomorphic mappings are central to image registration due largely to their topological properties and success in providing biologically plausible solutions to deformation and morphological estimation problems. Popular diffeomorphic image registration algorithms include those characterized by time-varying and constant velocity fields, and symmetrical considerations. Prior information in the form of regularization is used to enforce transform plausibility taking the form of physics-based constraints or through some approximation thereof, e.g. Gaussian smoothing of the vector fields (a la Thirion's Demons \citep{thirion1998}). In the context of the original Demons' framework, the so-called {\it directly manipulated free-form deformation} \citep{tustison2009} can be viewed as a smoothing alternative in which explicit regularization is achieved through fast B-spline approximation. This characterization can be used to provide B-spline ``flavored'' diffeomorphic image registration solutions with several advantages. Implementation is open source and available through the Insight Toolkit and our Advanced Normalization Tools (ANTs) repository. A thorough comparative evaluation with the well-known SyN algorithm \citep{avants2008}, implemented within the same framework, and its B-spline analog is performed using open labeled brain data and open source evaluation tools.