Biomechanical modelling and computer aided simulation of deep brain retraction in neurosurgery

Abstract

Background and Objectives: Surgical simulators are widely used to promote faster and safer surgical training. 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. Retracting the soft brain tissue is an unavoidable procedure during any surgery to access the lesioned tissue deep within the brain. Excessive retraction often results in damaging the brain tissue, thus requiring advanced skills and prior training using virtual platforms. Such surgical simulation platforms require an anatomically correct computational model that can accurately predict the brain deformation in real-time.Methods: In this study, we present a 3D finite element brain model reconstructed from MRI dataset. The model incorporates precisely the anatomy and geometrical features of the canine brain. The brain model has been used to formulate and solve a quasi-static boundary value problem for brain deformation during brain retraction. The visco-hyperelastic framework within the theory of non-linear elasticity has been used to set up the boundary value problem. Consequently, the derived non-linear field equations have been solved using finite element solver ABAQUS.Results: The retraction simulations have been performed for two scenarios: retraction pressure in the brain and forces required to perform the surgery. The brain was retracted by 5 mm and retained at that position for 30 minutes, during which the retraction pressure attenuates to 36% of its peak value. Both the model predictions as well as experimental observations on canine brain indicate that brain retraction up to 30 minutes did not cause any significant risk of induced damage. Also, the peak retraction pressure level indicates that intermittent retraction is a safer procedure as compared to the continuous retraction, for the same extent of retraction.Conclusions: The results of the present study indicate the potential of a visco-hyperelastic framework for simulating deep brain retraction effectively. The simulations were able to capture the dominant characteristics of brain tissue undergoing retraction. The developed platform could serve as a basis for the development of a detailed model in the future that can eventually be used for effective preoperative planning and training purposes.