Analysis of Model-Updated MR Images to Correct for Brain Deformation Due to Tissue Retraction

Abstract

Surgical events such as retraction, resection, and gravitational sag often cause significant tissue movement that compromises the accuracy of neuronavigation systems that use a preoperative image display. Computational modeling has gained interest as a method for correcting registration errors that result from brain deformation by simulating surgical events and creating updated images. The success of simulating surgical events relies upon the application of surgical forces to a model of brain deformation physics. This paper analyzes the model simulation of retraction using a finite element model of the brain. To test the model, we conducted an ex vivo experiment on a porcine model using a retraction system in a MR scanner. The high-resolution images of retraction obtained from the sets of MR images were used to create the 3D volumetric model and serve as a basis of comparison to the model-updated images and calculations. The model is found to recapture 66 % of average tissue motion and reduce the maximum registration error by over 80%. The model-updated images are displayed along with the actual deformation images and show a strong potential for computational modeling as a means to compensate for brain shift and minimize registration errors