Intraoperative brain shift compensation is important for improving the accuracy of neuronavigational systems and
ultimately, the accuracy of brain tumor resection as well as patient quality of life. Biomechanical models are practical
methods for brain shift compensation in the operating room (OR). These methods assimilate incomplete deformation
data on the brain acquired from intraoperative imaging techniques (e.g., ultrasound and stereovision), and simulate
whole-brain deformation under loading and boundary conditions in the OR. Preoperative images of the patient's head
(e.g., preoperative magnetic resonance images (pMR)) are then deformed accordingly based on the computed
displacement field to generate updated visualizations for subsequent surgical guidance. Apparently, the clinical
feasibility of the technique depends on the efficiency as well as the accuracy of the computational scheme. In this paper,
we identify the major steps involved in biomechanical simulation of whole-brain deformation and demonstrate the
efficiency and accuracy of each step. We show that a combined computational cost of 5 minutes with an accuracy of 1-2
millimeter can be achieved which suggests that the technique is feasible for routine application in the OR.
Brain shift poses a significant challenge to accurate image-guided neurosurgery. To this end, finite element (FE) brain
models have been developed to estimate brain motion during these procedures. The significance of the brain-skull
boundary conditions (BCs) for accurate predictions in these models has been explored in dynamic impact and inertial
rotation injury computational simulations where the results have shown that the brain mechanical response is sensitive to
the type of BCs applied. We extend the study of brain-skull BCs to quasi-static brain motion simulations which prevail
in neurosurgery. Specifically, a frictionless brain-skull BC using a contact penalty method master-slave paradigm is
incorporated into our existing deformation forward model (forced displacement method). The initial brain-skull gap
(CSF thickness) is assumed to be 2mm for demonstration purposes. The brain surface nodes are assigned as either fixed
(at bottom along the gravity direction), free (at brainstem), with prescribed displacement (at craniotomy) or as slave
nodes potentially in contact with the skull (all the remaining). Each slave node is assigned a penalty parameter (β=5)
such that when the node penetrates the rigid body skull inner-surface (master surface), a contact force is introduced
proportionally to the penetration. Effectively, brain surface nodes are allowed to move towards or away from the
cranium wall, but are ultimately restricted from penetrating the skull. We show that this scheme improves the model's
ability to represent the brain-skull interface.
Biomechanical models of brain deformation are useful tools for estimating the shift that occurs during neurosurgical
interventions. Incorporation of intra-operative data into the biomechanical model improves the accuracy of the
registration between the patient and the image volume. The representer method to solve the adjoint equations (AEM)
for data assimilation has been developed. In order to improve the computational efficiency and to process more intraoperative
data, we modified the adjoint equation method by changing the way in which intraoperative data is applied.
The current formulation is developed around a point-based data-model misfit. Surface based data-model misfit could
be a more robust and computationally efficient technique. Our approach is to express the surface misfit as the volume
between the measured surface and model predicted surface. An iterative method is used to solve the adjoint equations.
The surface misfit criterion is tested in a cortical distension clinical case and compared to the results generated with the
prior point-based methodology solved either iteratively or with the representer algorithm. The results show that solving
the adjoint equations with an iterative method improves computational efficiency dramatically over the representer
approach and that reformulating the minimization criterion in terms of a surface description is even more efficient.
Applying intra-operative data in the form of a surface misfit is computationally very efficient and appears promising
with respect to its accuracy in estimating brain deformation.
Shift of brain tissues during surgical procedures affects the precision of image-guided neurosurgery (IGNS). To improve the accuracy of the alignment between the patient and images, finite element model-based non-rigid registration methods have been investigated. The best prior estimate (BPE), the forced displacement method (FDM), the weighted basis solutions (WBS), and the adjoint equations method (AEM) are versions of this approach that have appeared in the literature. In this paper, we present a quantitative comparison study on a set of three patient cases. Three-dimensional displacement data from the surface and subsurface was extracted using the intra-operative ultrasound (iUS) and intraoperative stereovision (iSV). These data are then used as the "ground truth" in a quantitative study to evaluate the accuracy of estimates produced by the finite element models. Different types of clinical cases are presented, including distension and combination of sagging and distension. In each case, a comparison of the performance is made with the four methods. The AEM method which recovered 26-62% of surface brain motion and 20-43% of the subsurface deformation, produced the best fit between the measured data and the model estimates.