A Memetic Search Scheme for Robust Registration of Diffusion-Weighted MR Images

Effective image-based artifact correction is an essential step in the application of higher order models in diffusion MRI. Most approaches rely on some kind of retrospective registration, which becomes increasingly challenging in the realm of high b-values and low signal-to-noise ratio (SNR), rendering standard correction schemes more and more ineffective. We propose a novel optimization scheme based on memetic search that allows for simultaneous exploitation of different signal intensity relationships between the images, leading to more robust registration results. We demonstrate the increased robustness and efficacy of our method on simulated as well as in-vivo datasets. The median TRE for an affine registration of b = 3000s/mm(2) acquisitions could be reduced from > 5 mm for a standard correction scheme to < 1 mm using our approach. In-vivo bootstrapping experiments revealed increased precision in all tensor-derived quantities.