Data-guide for brain deformation in surgery: comparison of linear and nonlinear models

Abstract

BackgroundPre-operative imaging devices generate high-resolution images but intra-operative imaging devices generate low-resolution images. To use high-resolution pre-operative images during surgery, they must be deformed to reflect intra-operative geometry of brain.MethodsWe employ biomechanical models, guided by low resolution intra-operative images, to determine location of normal and abnormal regions of brain after craniotomy. We also employ finite element methods to discretize and solve the related differential equations. In the process, pre- and intra-operative images are utilized and corresponding points are determined and used to optimize parameters of the models. This paper develops a nonlinear model and compares it with linear models while our previous work developed and compared linear models (mechanical and elastic).ResultsNonlinear model is evaluated and compared with linear models using simulated and real data. Partial validation using intra-operative images indicates that the proposed models reduce the localization error caused by brain deformation after craniotomy.ConclusionsThe proposed nonlinear model generates more accurate results than the linear models. When guided by limited intra-operative surface data, it predicts deformation of entire brain. Its execution time is however considerably more than those of linear models.

Surface Registration with Eigenvalues and Eigenvectors

“…Traditional landmark-based methods usually detect relevant corresponding points or curves in two shapes, i.e., landmarking is essential in many shape registration and mapping applications [4], [5], [6], [7], [8], [9]. There are two drawbacks in this type of methods.…”