Computer simulation of tumour resection‐induced brain deformation by a meshless approach

Abstract

Tumour resection requires precise planning and navigation to maximise tumour removal while simultaneously protecting nearby healthy tissues. Neurosurgeons need to know the location of the remaining tumour after partial tumour removal before continuing with the resection. Our approach to the problem uses biomechanical modelling and computer simulation to compute the brain deformations after the tumour is resected. In this study, we use meshless Total Lagrangian explicit dynamics as the solver. The problem geometry is extracted from the patient-specific magnetic resonance imaging (MRI) data and includes the parenchyma, tumour, cerebrospinal fluid and skull. The appropriate non-linear material formulation is used. Loading is performed by imposing intra-operative conditions of gravity and reaction forces between the tumour and surrounding healthy parenchyma tissues. A finite frictionless sliding contact is enforced between the skull (rigid) and parenchyma. The meshless simulation results are compared to intra-operative MRI sections. We also calculate Hausdorff distances between the computed deformed surfaces (ventricles and tumour cavities) and surfaces observed intra-operatively. Over 80% of points on the ventricle surface and 95% of points on the tumour cavity surface were successfully registered (results within the limits of two times the original in-plane resolution of the intra-operative image). Computed results demonstrate the potential for our method in estimating the tissue deformation and tumour boundary during the resection.

Automatic framework for patient-specific modelling of tumour resection-induced brain shift

“…Table 1 summarises the information of brain tumour patients analysed in this study. We have analysed Patient 1 in our previous research [3,4].…”

“…Furthermore, intra-operative MRI suites are available only in a small number of hospitals due to their high cost. On the other hand, the integration of some surgical information (such as the geometry of resected tumour in the tumour resection [3,4] and displacement of the exposed brain surface in the craniotomy [5][6][7][8][9]) into a comprehensive patient-specific biomechanical brain model could either enable surgeons to pre-operatively evaluate the risks and plan for the most appropriate surgical strategy [10][11][12] or locate the pathological targets during the surgery without taking intra-operative MRIs [5][6][7][8][9]. Unlike image-based registration [13,14], biomechanics-based registration method does not always require an intra-operative image.…”

“…In these simulations, loading was defined by Dirichlet boundary conditions and the constitutive was Neo-Hookean [5,7,[23][24][25]. In Yu et al [4], we adapted MTLED algorithm to work with traction boundary conditions and volumetric loads as well as the highly nonlinear Ogden brain model. In this paper, we built an automatic framework based on our existing modelling strategy and solution algorithm [4].…”

“…In Yu et al [4], we adapted MTLED algorithm to work with traction boundary conditions and volumetric loads as well as the highly nonlinear Ogden brain model. In this paper, we built an automatic framework based on our existing modelling strategy and solution algorithm [4]. Our previous research [4] used our meshless algorithm (MTLED) to compute the brain shift during tumour resection and warped the high-quality pre-operative MRI to the intra-operative configuration of the brain.…”

“…In this paper, we built an automatic framework based on our existing modelling strategy and solution algorithm [4]. Our previous research [4] used our meshless algorithm (MTLED) to compute the brain shift during tumour resection and warped the high-quality pre-operative MRI to the intra-operative configuration of the brain.…”