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Purpose: About one-third of individuals with focal epilepsy continue to have seizures despite optimal medical management. These patients are potentially curable with neurosurgery if the epileptogenic zone (EZ) can be identified and resected. Stereo-electroencephalography (SEEG) to record epileptic activity with intracranial depth electrodes may be required to identify the EZ. Each SEEG electrode trajectory, the path between the entry on the skull and the cerebral target, must be planned carefully to avoid trauma to blood vessels and conflicts between electrodes. In current clinical practice trajectories are determined manually, typically taking 2-3 h per patient (15 min per electrode). Manual planning (MP) aims to achieve an implantation plan with good coverage of the putative EZ, an optimal spatial resolution, and 3D distribution of electrodes. Computer-assisted planning tools can reduce planning time by quantifying trajectory suitability., Methods: We present an automated multiple trajectory planning (MTP) algorithm to compute implantation plans. MTP uses dynamic programming to determine a set of plans. From this set a depth-first search algorithm finds a suitable plan. We compared our MTP algorithm to (a) MP and (b) an automated single trajectory planning (STP) algorithm on 18 patient plans containing 165 electrodes., Results: MTP changed all 165 trajectories compared to MP. Changes resulted in lower risk (122), increased grey matter sampling (99), shorter length (92), and surgically preferred entry angles (113). MTP changed 42 % (69/165) trajectories compared to STP. Every plan had between 1 to 8 (median 3.5) trajectories changed to resolve electrode conflicts, resulting in surgically preferred plans., Conclusion: MTP is computationally efficient, determining implantation plans containing 7-12 electrodes within 1 min, compared to 2-3 h for MP., Competing Interests: The authors declare that they have no conflict of interest.

Purpose: Brain vessels are among the most critical landmarks that need to be assessed for mitigating surgical risks in stereo-electroencephalography (SEEG) implantation. Intracranial haemorrhage is the most common complication associated with implantation, carrying significantly associated morbidity. SEEG planning is done pre-operatively to identify avascular trajectories for the electrodes. In current practice, neurosurgeons have no assistance in the planning of electrode trajectories. There is great interest in developing computer-assisted planning systems that can optimise the safety profile of electrode trajectories, maximising the distance to critical structures. This paper presents a method that integrates the concepts of scale, neighbourhood structure and feature stability with the aim of improving robustness and accuracy of vessel extraction within a SEEG planning system., Methods: The developed method accounts for scale and vicinity of a voxel by formulating the problem within a multi-scale tensor voting framework. Feature stability is achieved through a similarity measure that evaluates the multi-modal consistency in vesselness responses. The proposed measurement allows the combination of multiple images modalities into a single image that is used within the planning system to visualise critical vessels., Results: Twelve paired data sets from two image modalities available within the planning system were used for evaluation. The mean Dice similarity coefficient was 0.89 ± 0.04, representing a statistically significantly improvement when compared to a semi-automated single human rater, single-modality segmentation protocol used in clinical practice (0.80 ± 0.03)., Conclusions: Multi-modal vessel extraction is superior to semi-automated single-modality segmentation, indicating the possibility of safer SEEG planning, with reduced patient morbidity.

Objective: To determine whether resection of areas with evidence of intense, synchronized neural firing during seizures is an accurate indicator of postoperative outcome., Methods: Channels meeting phase-locked high gamma (PLHG) criteria were identified retrospectively from intracranial EEG recordings (102 seizures, 46 implantations, 45 patients). Extent of removal of both the seizure onset zone (SOZ) and PLHG was correlated with seizure outcome, classified as good (Engel class I or II, n = 32) or poor (Engel class III or IV, n = 13)., Results: Patients with good outcomes had significantly greater proportions of both SOZ and the first 4 (early) PLHG sites resected. Improved outcome classification was noted with early PLHG, as measured by the area under the receiver operating characteristic curves (PLHG 0.79, SOZ 0.68) and by odds ratios for resections including at least 75% of sites identified by each measure (PLHG 9.7 [95% CI: 2.3-41.5], SOZ 5.3 [95% CI: 1.2-23.3]). Among patients with resection of at least 75% of the SOZ, 78% (n = 30) had good outcomes, increasing to 91% when the resection also included at least 75% of early PLHG sites (n = 22)., Conclusions: This study demonstrates the localizing value of early PLHG, which is comparable to that provided by the SOZ. Incorporation of PLHG into the clinical evaluation may improve surgical efficacy and help to focus resections on the most critical areas., (© 2015 American Academy of Neurology.)

Background: Stereoelectroencephalography (SEEG) is an invasive diagnostic procedure in epilepsy surgery that is usually implemented with frame-based methods., Objective: To describe a new technique of frameless SEEG and report a prospective case series at a single center., Methods: Image integration and planning of electrode trajectories were performed preoperatively on specialized software and exported to a Medtronic S7 StealthStation. Trajectories were implemented by frameless stereotaxy using percutaneous drilling and bolt insertion., Results: Twenty-two patients went this technique, with the insertion of 187 intracerebral electrodes. Of 187 electrodes, 175 accurately reached their neurophysiological target, as measured by postoperative computed tomography reconstruction and multimodal image integration with preoperative magnetic resonance imaging. Four electrodes failed to hit their target due to extradural deflection, and 3 were subsequently resited satisfactorily. Eight electrodes were off target by a mean of 3.6 mm (range, 0.9-6.8 mm) due to a combination of errors in bolt trajectory implementation and bending of the electrode. There was 1 postoperative hemorrhage that was clinically asymptomatic and no postoperative infections. Sixteen patients were offered definitive cortical resections, and 6 patients were excluded from resective surgery., Conclusion: Frameless SEEG is a novel and safe method for implementing SEEG and is easily translated into clinical practice.

StereoEEG implantation is performed in patients with epilepsy to determine the site of the seizure onset zone. Intracranial haemorrhage is the most common complication associated to implantation carrying a risk that ranges from 0.6 to 2.7%, with significant associated morbidity. SEEG planning is done pre-operatively to identify avascular trajectories for the electrodes. In current practice neurosurgeons have no assistance in the planning of the electrode trajectories. There is great interest in developing computer assisted planning systems that can optimize the safety profile of electrode trajectories, maximizing the distance to critical brain structures. In this work, we address the problem of blood vessel extraction for SEEG trajectory planning. The proposed method exploits the availability of multi-modal images within a trajectory planning system to formulate a vessel extraction framework that combines the scale and the neighbouring structure of an object. We validated the proposed method in twelve multi-modal patient image sets. The mean Dice similarity coefficient (DSC) was 0.88 ± 0.03, representing a statistically significantly improvement when compared to the semi-automated single rater, single modality segmentation protocol used in current practice (DSC = 0.78 ± 0.02).

Objective We present a single-center prospective study, validating the use of 3D multimodality imaging (3 DMMI) in patients undergoing intracranial electroencephalography ( IC- EEG). Methods IC- EEG implantation preparation entails first designing of the overall strategy of implantation (strategy) and second the precise details of implantation (planning). For each case, the multidisciplinary team made decisions on strategy and planning before the disclosure of multimodal brain imaging models. Any changes to decisions, following disclosure of the multimodal models, were recorded. Results Disclosure of 3 DMMI led to a change in strategy in 15 (34%) of 44 individuals. The changes included addition and subtraction of electrodes, addition of grids, and going directly to resection. For the detailed surgical planning, 3 DMMI led to a change in 35 (81%) of 43 individuals. Twenty-five (100%) of 25 patients undergoing stereo- EEG ( SEEG) underwent a change in electrode placement, with 158 (75%) of 212 electrode trajectories being altered. Significance The use of 3 DMMI makes substantial changes in clinical decision making. [ABSTRACT FROM AUTHOR]

Surgical resection in non-lesional, extratemporal epilepsy, informed by stereoEEG recordings, is challenging. There are no clear borders of resection, and the surgeon is often operating in deep areas of the brain that are difficult to access. We present a technical note where 3D multimodality image integration in EpiNavTM is used to build a planned resection model, based on a previous intracranial EEG evaluation. Intraoperative MRI is then used to ensure a complete resection of the planned model. As stereoEEG becomes more common in the presurgical evaluation of epilepsy, these tools will become increasingly important to facilitate targeted cortical resections. [ABSTRACT FROM AUTHOR]