Anatomy-driven multiple trajectory planning (ADMTP) of intracranial electrodes for epilepsy surgery

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Abstract

Purpose: Epilepsy is potentially curable with resective surgery if the epileptogenic zone (EZ) can be identified. If non-invasive imaging is unable to elucidate the EZ, intracranial electrodes may be implanted to identify the EZ as well as map cortical function. In current clinical practice, each electrode trajectory is determined by time-consuming manual inspection of preoperative imaging to find a path that avoids blood vessels while traversing appropriate deep and superficial regions of interest (ROIs). We present anatomy-driven multiple trajectory planning (ADMTP) to find safe trajectories from a list of user-defined ROIs within minutes rather than the hours required for manual planning. Methods: Electrode trajectories are automatically computed in three steps: (1) Target Point Selection to identify appropriate target points within each ROI; (2) Trajectory Risk Scoring to quantify the cumulative distance to critical structures (blood vessels) along each trajectory, defined as the skull entry point to target point. (3) Implantation Plan Computation: to determine a feasible combination of low-risk trajectories for all electrodes. Results: ADMTP was evaluated on 20 patients (190 electrodes). ADMTP lowered the quantitative risk score in 83% of electrodes. Qualitative results show ADMTP found suitable trajectories for 70% of electrodes; a similar portion of manual trajectories were considered suitable. Trajectory suitability for ADMTP was 95% if traversing sulci was not included in the safety criteria. ADMTP is computationally efficient, computing between 7 and 12 trajectories in 54.5 (17.3–191.9) s. Conclusions: ADMTP efficiently compute safe and surgically feasible electrode trajectories.

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APA

Sparks, R., Vakharia, V., Rodionov, R., Vos, S. B., Diehl, B., Wehner, T., … Ourselin, S. (2017). Anatomy-driven multiple trajectory planning (ADMTP) of intracranial electrodes for epilepsy surgery. International Journal of Computer Assisted Radiology and Surgery, 12(8), 1245–1255. https://doi.org/10.1007/s11548-017-1628-z

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