Multi-modal robust inverse-consistent linear registration

Registration performance can significantly deteriorate when image regions do not comply with model assumptions. Robust estimation improves registration accuracy by reducing or ignoring the contribution of voxels with large intensity differences, but existing approaches are limited to monomodal registration. In this work, we propose a robust and inverse-consistent technique for cross-modal, affine image registration. The algorithm is derived from a contextual framework of image registration. The key idea is to use a modality invariant representation of images based on local entropy estimation, and to incorporate a heteroskedastic noise model. This noise model allows us to draw the analogy to iteratively reweighted least squares estimation and to leverage existing weighting functions to account for differences in local information content in multimodal registration. Furthermore, we use the nonparametric windows density estimator to reliably calculate entropy of small image patches. Finally, we derive the Gauss–Newton update and show that it is equivalent to the efficient second-order minimization for the fully symmetric registration approach. We illustrate excellent performance of the proposed methods on datasets containing outliers for alignment of brain tumor, full head, and histology images.National Cancer Institute (U.S.) (Grant K25-CA181632-01A1)National Center for Research Resources (U.S.) (Grant P41-RR13218)National Center for Research Resources (U.S.) (Grant P41-RR14075)National Center for Research Resources (U.S.) (Grant U24-RR021382)National Institute of Biomedical Imaging and Bioengineering (U.S.) (Grant R01EB006758)National Alliance for Medical Image Computing (U.S.) (Grant U54-EB005149)National Institute on Aging (Grant AG022381)National Institute on Aging (Grant 5R01AG008122-22)National Center for Complementary and Alternative Medicine (U.S.) (Grant RC1 AT005728-01)National Institute of Neurological Diseases and Stroke (U.S.) (Grant R01 NS052585-01)National Institute of Neurological Diseases and Stroke (U.S.) (Grant 1R21NS072652-01)National Institute of Neurological Diseases and Stroke (U.S.) (Grant 1R01NS070963)National Institutes of Health (U.S.) (Grant 5U01-MH093765