Deformable image registration is commonly included in population and longitudinal medical imaging analysis pipelines. Initializing deformable registration with the results of affine registration, where global misalignments have been reduced, can improve overall registration accuracy. Affine registration, however, uses a limited linear transformation model that does not align nonlinear anatomical variations, such as those between pre- and post-operative images or across different individual anatomies. In this work, we introduce a new intermediate deformable image registration (IDIR) method that corrects large deformations via cosine-windowed cross-correlation, and provide an efficient implementation via the fast Fourier transform. We evaluate our general-purpose approach qualitatively and quantitatively on 2D bone X-ray images, 3D brain magnetic resonance images, and 3D abdominal computed tomography images, demonstrating its ability to align large nonlinear anatomical variations within a few iterations and its suitability for initializing standard deformable registration.
Iman Aganj (Fri,) studied this question.