Controlling the error on target motion through real-time mesh adaptation: applications to Deep Brain Stimulation

Abstract : An error-controlled mesh refinement procedure for needle insertion simulations is presented. As an example, the procedure is applied for simulations of electrode implantation for Deep Brain Stimulation. We take into account the brain shift phenomena occurring when a craniotomy is performed. We observe that the error in the computation of the displacement and stress fields is localised around the needle tip and the needle shaft during needle insertion simulation. By suitably and adaptively refining the mesh in this region, our approach enables to control, and thus to reduce, the error whilst maintaining a coarser mesh in other parts of the domain. Through academic and practical examples we demonstrate that our adaptive approach, as compared to a uniform coarse mesh, increases the accuracy of the displacement and stress fields around the needle shaft, while for a given accuracy, saves computational time with respect to an uniform finer mesh. This facilitates real-time simulations. The proposed methodology has direct implications in increasing the accuracy, and controlling the computational expense of the simulation of percutaneous procedures such as biopsy, brachytherapy, regional anesthesia, or cryotherapy. Moreover, the proposed approach can be helpful in the development of robotic surgeries because the simulation taking place in the control loop of a robot needs to be accurate, and to occur in real time.
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Submitted on : Wednesday, December 27, 2017 - 9:45:44 PM
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Huu Phuoc Bui, Satyendra Tomar, Hadrien Courtecuisse, Michel Audette, Stéphane Cotin, et al.. Controlling the error on target motion through real-time mesh adaptation: applications to Deep Brain Stimulation. International Journal for Numerical Methods in Biomedical Engineering, John Wiley and Sons, 2017, pp.1-26. ⟨10.1002/cnm.2958⟩. ⟨hal-01672962⟩

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