A Momentum-Based Acceleration of the Diffeomorphic Demons Algorithm for Registration of MRI and CT Images of the Brain*

Accurate deformable registration of multiple 3D imaging modalities is vital to many areas of diagnostic and interventional radiology and surgery. Diffeomorphic Demons algorithm have been previously reported for mono-modality registration. We extend such methodology to multi-modal (MRI-CT) images of the brain using point-wise mutual information (pMI) with momentum-based acceleration of the optimization. Preprocessing via automatic histogram stretch improved robustness and accuracy of registration in studies involving CT and T1-weighted MRI of a head phantom and clinical studies of five neurosurgery patients. Performance was compared to B-spline Free-Form Deformation (FFD) and Symmetric Normalization (SyN). pMI-Demons achieved target registration error of 0.21±0.07 mm (median±iqr) in phantom and 1.57±0.52 mm in clinical studies, providing alignment comparable to the voxel size without statistically significant difference from FFD and SyN. The pMI-Demons and SyN methods yielded diffeomorphic transformations, whereas FFD yielded unrealistic deformations. pMI-Demons provided a 66% runtime reduction (10 min vs. 30 min for SyN) that facilitates application in rapid image-guided neurosurgery workflows.