SymBA: Diffeomorphic Registration Based on Gradient Orientation Alignment and Boundary Proximity of Sparsely Selected Voxels

We propose a novel non-linear registration strategy which seeks an optimal deformation that maps corresponding boundaries of similar orientation. Our approach relies on a local similarity metric based on gradient orientation alignment and distance to the nearest inferred boundary and is evaluated on a reduced set of locations corresponding to inferred boundaries. The deformation model is characterized as the integration of a time-constant velocity field and optimization is performed in coarse to fine multi-level strategy with a gradient ascent technique. Our approach is computational efficient since it relies on a sparse selection of voxels corresponding to detected boundaries, yielding robust and accurate results with reduced processing times. We demonstrate quantitative results in the context of the non-linear registration of inter-patient magnetic resonance brain volumes obtained from a public dataset (CUMC12). Our proposed approach achieves a similar level of accuracy as other state-of-the-art methods but with processing times as short as 1.5 minutes. We also demonstrate preliminary qualitative results in the time-sensitive registration contexts of registering MR brain volumes to intra-operative ultrasound for improved guidance in neurosurgery.