Registration of Pre- and Postresection Ultrasound Volumes With Noncorresponding Regions in Neurosurgery.

Abstract

Brain tissue deforms significantly after opening the dura and during tumor resection, invalidating preoperative imaging data. Ultrasound is a popular imaging modality for providing the neurosurgeon with real-time updated images of brain tissue. Interpretation of postresection ultrasound images is difficult due to large brain shift and tissue resection. Furthermore, several factors degrade the quality of postresection ultrasound images such as the strong reflection of waves at the interface of saline water and brain tissue in resection cavities, air bubbles, and the application of blood-clotting agents around the edges of resection. Image registration allows the comparison of postresection ultrasound images with higher quality preresection images, assists in interpretation of postresection images and may help identify residual tumor, and, as such, is of significant clinical importance. In this paper, we propose a nonrigid symmetric registration (NSR) framework for accurate alignment of pre- and postresection volumetric ultrasound images in near real time. We first formulate registration as minimization of a regularized cost function and analytically derive its derivative to efficiently optimize the cost function. An outlier detection algorithm is proposed and utilized in this framework to identify noncorresponding regions (outliers) and therefore improve the robustness and accuracy of registration. We use an efficient second-order minimization method for fast and robust optimization. Furthermore, we exploit a symmetric and inverse-consistent method to generate realistic deformation fields. The results show that NSR significantly improves the quality of the alignment between pre- and postresection ultrasound images.