Robust Deformable Registration of Pre and Post-Resection Ultrasound Volumes for Visualization of Residual Tumor in Neurosurgery

Abstract

The brain tissue deforms significantly during neurosurgery, which has led to the use of intra-operative ultrasound in many sites to provide updated ultrasound images of tumor and critical parts of the brain. Several factors degrade the quality of post-resection ultrasound images such as hemorrhage, air bubbles in tumor cavity and the application of blood-clotting agent around the edges of the resection. As a result, registration of post- and pre-resection ultrasound is of significant clinical importance. In this paper, we propose a nonrigid symmetric registration (NSR) framework for accurate alignment of pre- and post-resection volumetric ultrasound images in near real-time. We first formulate registration as the minimization of a regularized cost function, and analytically derive its derivative to efficiently optimize the cost function. We use Efficient Second-order Minimization (ESM) method for fast and robust optimization. Furthermore, we use inverse-consistent deformation method to generate realistic deformation fields. The results show that NSR significantly improves the quality of alignment between pre- and post-resection ultrasound images.