Biomechanical modeling of the human head for physically based, nonrigid image registration

Abstract

The accuracy of image-guided neurosurgery generally suffers from brain deformations due to intraoperative changes. These deformations cause significant changes of the anatomical geometry (organ shape and spatial interorgan relations), thus making intraoperative navigation based on preoperative images error prone. In order to improve the navigation accuracy, we developed a biomechanical model of the human head based on the finite element method, which can be employed for the correction of preoperative images to cope with the deformations occurring during surgical interventions. At the current stage of development, the two-dimensional (2-D) implementation of the model comprises two different materials, though the theory holds for the three-dimensional (3-D) case and is capable of dealing with an arbitrary number of different materials. For the correction of a preoperative image, a set of homologous landmarks must be specified which determine correspondences. These correspondences can be easily integrated into the model and are maintained throughout the computation of the deformation of the preoperative image. The necessary material parameter values have been determined through a comprehensive literature study. Our approach has been tested for the case of synthetic images and yields physically plausible deformation results. Additionally, we carried out registration experiments with a preoperative MR image of the human head and a corresponding postoperative image simulating an intraoperative image. We found that our approach yields good prediction results, even in the case when correspondences are given in a relatively small area of the image only.

Serial registration of intraoperative MR images of the brain

“…linear elasticity models discretized using the Finite Element (FE) Method) have been explicitly proposed to constrain image registration (Kyriacou et al, 1999;Hagemann et al, 1999). In these studies, the deformation from one image to the next is enforced by manually, or semi-automatically defining correspondences between contours in the images to register.…”

“…Also, by establishing correspondences between image structures in a different way than with a similarity measure computed all over the image volume, one can provide the deformation model with physically better constrained deformations. The current disadvantages of these methods is that they have only been tested in 2D, with computations done on a pixel basis by Hagemann et al (1999), which causes the FE computations to be particularly computationally expensive.…”

“…As available meshing software did not produce satisfactory results on biomedical structures, we developed our own tetrahedral mesh generation algorithm. Unlike Hagemann et al (1999), we use elements covering several image voxels so as to limit computational complexity without sacrificing accuracy.…”

An Anisotropic Material Model for Image Guided Neurosurgery

“…Most models of deformation either represent the brain as some kind of elastic solid or consolidated material [1,4,6,12]. The most significant limitation at the present time is the computational overhead associated with calculating a Finite Element solution for each update, which limits the complexity of the model that is practical for use in IGNS.…”

A minimum description length objective function for groupwise non-rigid image registration

“…Alternatively, the deformation may be caused by atrophy, such as in dementia, or growth, as in a tumour. In either case, the most suitable choice of registration algorithm may well be one that closely models the underlying physical process, leading to physically-based registration algorithms (e.g., [6,7]), or physically-based models (e.g., [8]) that can be used to evaluate the results of non-rigid registration algorithms.…”