MMORF - FSL's MultiMOdal Registration Framework

Abstract

We present MMORF--FSLs MultiMOdal Registration Framework--a newly released nonlinear image registration tool designed primarily for application to MRI images of the brain. MMORF is capable of simultaneously optimising both displacement and rotational transformations within a single registration framework by leveraging rich information from multiple scalar and tensor modalities. The regularisation employed in MMORF promotes local rigidity in the deformation, and we have previously demonstrated how this effectively controls both shape and size distortion, and leads to more biologically plausible warps. The performance of MMORF is benchmarked against three established nonlinear registration methods--FNIRT, ANTs and DR-TAMAS--across four domains: FreeSurfer label overlap, DTI similarity, task-fMRI cluster mass, and distortion. Results show that MMORF performs as well as or better than all other methods across every domain--both in terms of accuracy and levels of distortion. MMORF is available as part of FSL, and its inputs and outputs are fully compatible with existing workflows. We believe that MMORF will be a valuable tool for the neuroimaging community, regardless of the domain of any downstream analysis, providing state-of-the-art registration performance that integrates into the rich and widely adopted suite of analysis tools in FSL.