Registration of CT and Ultrasound Images of the Spine with Neural Network and Orientation Code Mutual Information

Abstract

Pairwise registration of 2D ultrasound (US) and 3D computed tomography (CT) images can improve the efficiency and safety of image-guided anesthesia in spine surgery. However, accurate 2D US and 3D CT registration for multiple vertebras without an appropriate initial registration position is still a challenge, due to the difference of image modalities and missing bone structures in US image. This paper proposes a novel 2D US and 3D CT registration method, in which convolutional neural network (CNN) classification of US images is reported for the first time to achieve rough image registration. And a new orientation code mutual information metric is further applied to finish local registration refinement. By combining automatic rough registration with fine registration refinement, our algorithm achieves 2D US and 3D CT registration for multiple vertebras (L2-L4) without the requirement of an appropriate initial alignment. The accuracy of our algorithm is validated on 50 in vivo clinical US images dataset of multiple vertebras. And a mean target registration error of 2.3 mm is acquired, which is lower than the clinically acceptable accuracy 3.5 mm.