Hybrid Multimodal Registration of Carotid Magnetic Resonance and Ultrasound Images Using Free-Form Deformation

In this work, a hybrid approach for the multimodal registration of carotid magnetic resonance and ultrasound images is described. Multimodal registration is vital for integrating or fusing complementary information from different sources into a composite form. It will provide synergistic information about the objects under examination and thus, help in the assessment of carotid artery disease. The proposed hybrid approach combines the strengths of featurebased and intensity-based registration approaches to attain more precise registration in challenging problems. The featurebased iterative closest point algorithm has been used to provide the initial alignment of images which was traditionally done by manual operators. Subsequently, the intensity-based approach uses rigid-body model, to describe the global motion and non-rigid free-form deformation model based on B-splines, to describe the local motion of the carotid arteries. The normalized mutual information metric assessed the similarity in both rigid and non-rigid transformation models. Quantitative and qualitative evaluations of the proposed hybrid technique have also been presented. The results showed that the proposed hybrid registration method achieved a target registration error (TRE) of 0.1094 mm which is significantly less by 79.9% as compared to TRE of 0.5444 mm achieved using pure geometric method and by 73.4% in comparison to TRE of 0.4121 mm achieved using the composition of rigid geometric and iconic approaches.