  Multimodal image alignment involves finding spatial correspondences between
volumes varying in appearance and structure. Automated alignment methods are
often based on local optimization that can be highly sensitive to their
initialization. We propose a global optimization method for rigid multimodal 3D
image alignment, based on a novel efficient algorithm for computing similarity
of normalized gradient fields (NGF) in the frequency domain. We validate the
method experimentally on a dataset comprised of 20 brain volumes acquired in
four modalities (T1w, Flair, CT, [18F] FDG PET), synthetically displaced with
known transformations. The proposed method exhibits excellent performance on
all six possible modality combinations, and outperforms all four reference
methods by a large margin. The method is fast; a 3.4Mvoxel global rigid
alignment requires approximately 40 seconds of computation, and the proposed
algorithm outperforms a direct algorithm for the same task by more than three
orders of magnitude. Open-source implementation is provided.
