Deep Brain Stimulation (DBS) has been increasingly employed to treat motor symptoms of Parkinson’s Disease inhibiting the indirect dopaminergic pathway via the Subthalamic Nuclei (STN) stimulation. DBS planning is challenging due to the risk of hemorrhages, seizures and, importantly, the critical STN position. Therefore, planning algorithms for automatically computing DBS trajectories represent a breakthrough in this field. The capability to calculate a path able to provide an appropriate targeting of the STN also safeguarding the relevant anatomical obstacles is a key requisite for a competitive algorithm. In literature, planning solutions able to estimate only rectilinear trajectories for rigid electrodes have been proposed. In these cases, the impossibility to follow curvilinear trajectories may limit the chances to obtain an optimal targeting of the STN with the proper obstacle avoidance. Contrariwise, flexible electrodes can mitigate the limitations of their rigid counterparts through their ability to steer along curvilinear trajectories. In particular, the present work focuses on an electrode whose design mimics the EDEN2020 programmable bevel-tip needle, where the displacement among four interlocked sections generates an offset on its tip so that the tool can follow curvilinear trajectories. The aim of this work is to present a planning algorithm for DBS able to estimate a pool of curvilinear trajectories for an accurate targeting of the STN, ensuring a higher level of safety with respect to the standard rectilinear approach.


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    Title :

    Steerable needle DBS path planning safeguards deep nuclei and white matter tracts



    Publication date :

    2018-01-01


    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



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