The brain was retracted by 5 mm and retained at that position for 30 minutes, during which the retraction pressure attenuates to 36% of its peak value. Results: The retraction simulations have been performed for two scenarios: retraction pressure in the brain and forces required to perform the surgery. Consequently, the derived non-linear field equations have been solved using finite element solver ABAQUS. The visco-hyperelastic framework within the theory of non-linear elasticity has been used to set up the boundary value problem. The brain model has been used to formulate and solve a quasi-static boundary value problem for brain deformation during brain retraction. The model incorporates precisely the anatomy and geometrical features of the canine brain. Methods: In this study, we present a 3D finite element brain model reconstructed from MRI dataset. Such surgical simulation platforms require an anatomically correct computational model that can accurately predict the brain deformation in real-time. Excessive retraction often results in damaging the brain tissue, thus requiring advanced skills and prior training using virtual platforms. Retracting the soft brain tissue is an unavoidable procedure during any surgery to access the lesioned tissue deep within the brain. They not only provide a platform for enhancing surgical skills but also minimize risks to the patient’s safety, operation theatre usage, and financial expenditure. The extracted material parameters could then be used to represent brain tissue for developing and solving boundary value problems concerning neurosurgical procedures.īackground and Objectives: Surgical simulators are widely used to promote faster and safer surgical training. Although a close agreement between the stress-strain curves from experimental data, curve fitting and inverse finite element method was observed, however, a significant deviation was found between the material parameters extracted from both the methods. The objective of the study is to demonstrate the differences in material parameters calibrated using standard least-square curve fitting and inverse finite element method. The ex-vivo uniaxial force-displacement data for the goat brain tissue has been used for calibration. In this work, open-source platforms named GIBBON and FEBio have been utilized to solve an inverse finite element-based optimization problem for calibrating the material parameters for the brain tissue. An accurate estimation of the constitutive response of brain tissue is a crucial requirement for the development of a neurosurgical simulation framework.
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