Abstracts

Rationale: There are various techniques for the placement of depth-electrodes as part of the investigation with SEEG, ranging from frame-based stereotactic techniques, frameless based on neuronavigation and robotic-assisted. Although the procedures are in general safe with a low reported incidence of adverse events, the neurosurgical training for this lacks the assistance of reliable models, that would mimic the real-life challenges, such as the accuracy of the electrode insertion and final placement. The recent availability of low-cost desktop 3D printers may be an answer to this demand. The authors propose a paradigm based on patient-specific 3D-printed skull models that allows for the planning and training of SEEG electrode placement, in this case for frame-based stereotactic procedures. Methods: Two male adult patients with drug-refractory epilepsy previously selected for SEEG investigation were enrolled. Written informed consent was obtained according to the guidelines of the institutional ethics committee.  The preoperative images of the non-contrast enhanced head CT were inspected, anonymized and exported in DICOM format with the use of the software Horos v3.3.5 (http://Horosproject.org), then imported in the InVesalius 3.1.1 medical imaging software for 3D reconstruction (CTI, Brazil).  The 3D surface data was exported into the STL format to be loaded in the printing software Ultimaker Cura v.4.2.1 (Ultimaker B.V.). After final review and preview, the patient-specific skull model data was sent to a desktop 3D printer, Ender-3 pro (Creality 3D, China).  The printed skull model was filled with gelatin 2,4% (Dr.Oetker Brasil Ltda., Brazil) and stored for 12 hours at 10-15°C. A Leksell stereotactic frame was positioned in the skull models (Leksell Coord. Frame G, Elekta, USA) simulating the conditions of the surgery and a CT of the pair model-frame was obtained.  The preoperative gadolinium-enhanced T1-weighted MRI images were fused with the CT images and the stereotactic planning of electrode placement were performed in the surgical workstation (Leksell SurgiPlan, Elekta, Sweden).  The stereotactic coordinates were generated and the skull model with the stereotactic frame was fixed to the surgical table with an ultra base unit and swivel adaptor (Mayfield, Integra LifeSciences Corp., USA). Two types of electrodes were tested (Ad-Tech Medical Instrum. Corp., USA and Dixi Medical, Switzerland) with the use of an L-shaped adaptor with a guide tube for orthogonal trajectories and a stereotactic arc for oblique trajectories. After the electrode placement, the frame was removed and a new CT was obtained. The images were then loaded along with the preoperative MRI images in Horos and GARDEL (GUI for Automatic Registration and Depth Electrode Localization, Aix Marseille Univ, INS, France) software for accuracy check. Results: Two skull models were successfully printed and the gelatin filling allowed a fair simulation of the brain tissue resistance found during deep electrode placement. The softwares used for planning and reconstruction of the post-procedure CT properly detected the model’s images and allowed for the fusion with the preoperative MRI data.  Finally, the eight electrodes inserted were correctly detected and accuracy check (8/8) was possible in a similar fashion to the current institutional clinical routines. Conclusions: The use of low-cost 3D-printed skull models is a feasible and valuable asset for the neurosurgical teaching and training of the technique for SEEG electrode placement.  Funding: FAEPA-HCFMRP-USP, CAPES, CNPq, FAPESP