Automatic segmentation of stereoelectroencephalography (SEEG) electrodes post-implantation considering bending

Stereoelectroencephalography for drug resistant epilepsy: precision and complications in stepwise improvement of frameless implantation

PURPOSE

Stereoelectroencephalography (SEEG) is the standard for invasive investigations in epilepsy surgery. Our aim was to investigate if similar precision and low complication rate can be achieved with optimized frameless navigation as with frame-based or dedicated stereotactic SEEG robot.

METHODS

We compared five different implantation techniques assessing entry, target errors and complications in 53 SEEGs from 50 patients: Group 1 - surface registration and Vertek probe, Group 2 - rigid registration with conventional CT and Vertek probe, Group 3 - rigid registration and Vertek probe, Group 4 - rigid registration and Autoguide, Group 5 - rigid, sterile registration and Autoguide. Analysis was done using random effects linear modelling to calculate improvement in percent using Group 1 as a reference, p < 0.001 was considered significant.

RESULTS

Mean patient age at implantation was 23 years (range 4-46 years) and mean number of implanted electrodes per patient were 11 (range 3-15). Accuracy data was available for 36 SEEG implantations (419 electrodes). The median entry/target errors were (mm): Group 1:4.6/4.3; Group 2:1.8/2.3; Group 3:0.9/1.5; Group 4:1.1/1.2; Group 5:0/0.7. Improvement of accuracy for entry error was 38% for Group 2 (p = 0.004), 47% for Group 3 (p < 0.001), 50% for Group 4 (p < 0.001), and 72% for Group 5 (p < 0.001). Improvement of accuracy for target error was 17% for Group 2 (p = 0.17), 22% for Group 3 (p < 0.001), 35% for Group 4 (p < 0.001), and 51% for Group 5 (p < 0.001). Complications (hemorrhage, edema, headache) occurred in 7/53 SEEGs, none of these led to permanent deficit. 40/53 investigations resulted in an epilepsy surgery procedure.

CONCLUSION

High precision and low complication rate in SEEG implantation can be achieved with frameless navigation using rigid, sterile registration.

Signal acquisition of brain-computer interfaces: A medical-engineering crossover perspective review

Brain-computer interface (BCI) technology represents a burgeoning interdisciplinary domain that facilitates direct communication between individuals and external devices. The efficacy of BCI systems is largely contingent upon the progress in signal acquisition methodologies. This paper endeavors to provide an exhaustive synopsis of signal acquisition technologies within the realm of BCI by scrutinizing research publications from the last ten years. Our review synthesizes insights from both clinical and engineering viewpoints, delineating a comprehensive two-dimensional framework for understanding signal acquisition in BCIs. We delineate nine discrete categories of technologies, furnishing exemplars for each and delineating the salient challenges pertinent to these modalities. This review furnishes researchers and practitioners with a broad-spectrum comprehension of the signal acquisition landscape in BCI, and deliberates on the paramount issues presently confronting the field. Prospective enhancements in BCI signal acquisition should focus on harmonizing a multitude of disciplinary perspectives. Achieving equilibrium between signal fidelity, invasiveness, biocompatibility, and other pivotal considerations is imperative. By doing so, we can propel BCI technology forward, bolstering its effectiveness, safety, and dependability, thereby contributing to an auspicious future for human-technology integration.

Automated Identification of Stereoelectroencephalography Contacts and Measurement of Factors Influencing Accuracy of Frame Stereotaxy

OBJECTIVE

Stereoelectroencephalography (SEEG) is an established invasive diagnostic technique for use in patients with drug-resistant focal epilepsy evaluated before resective epilepsy surgery. The factors that influence the accuracy of electrode implantation are not fully understood. Adequate accuracy prevents the risk of major surgery complications. Precise knowledge of the anatomical positions of individual electrode contacts is crucial for the interpretation of SEEG recordings and subsequent surgery.

METHODS

We developed an image processing pipeline to localize implanted electrodes and detect individual contact positions using computed tomography (CT), as a substitute for time-consuming manual labeling. The algorithm automates measurement of parameters of the electrodes implanted in the skull (bone thickness, implantation angle and depth) for use in modeling of predictive factors that influence implantation accuracy.

RESULTS

Fifty-four patients evaluated by SEEG were analyzed. A total of 662 SEEG electrodes with 8,745 contacts were stereotactically inserted. The automated detector localized all contacts with better accuracy than manual labeling (p < 0.001). The retrospective implantation accuracy of the target point was 2.4 ± 1.1 mm. A multifactorial analysis determined that almost 58% of the total error was attributable to measurable factors. The remaining 42% was attributable to random error.

CONCLUSION

SEEG contacts can be reliably marked by our proposed method. The trajectory of electrodes can be parametrically analyzed to predict and validate implantation accuracy using a multifactorial model.

SIGNIFICANCE

This novel, automated image processing technique is a potentially clinically important, assistive tool for increasing the yield, efficiency, and safety of SEEG.

Advances in human intracranial electroencephalography research, guidelines and good practices

Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.

Modeling intracranial electrodes. A simulation platform for the evaluation of localization algorithms

Introduction

Intracranial electrodes are implanted in patients with drug-resistant epilepsy as part of their pre-surgical evaluation. This allows the investigation of normal and pathological brain functions with excellent spatial and temporal resolution. The spatial resolution relies on methods that precisely localize the implanted electrodes in the cerebral cortex, which is critical for drawing valid inferences about the anatomical localization of brain function. Multiple methods have been developed to localize the electrodes, mainly relying on pre-implantation MRI and post-implantation computer tomography (CT) images. However, they are hard to validate because there is no ground truth data to test them and there is no standard approach to systematically quantify their performance. In other words, their validation lacks standardization. Our work aimed to model intracranial electrode arrays and simulate realistic implantation scenarios, thereby providing localization algorithms with new ways to evaluate and optimize their performance.

Results

We implemented novel methods to model the coordinates of implanted grids, strips, and depth electrodes, as well as the CT artifacts produced by these. We successfully modeled realistic implantation scenarios, including different sizes, inter-electrode distances, and brain areas. In total, ∼3,300 grids and strips were fitted over the brain surface, and ∼850 depth electrode arrays penetrating the cortical tissue were modeled. Realistic CT artifacts were simulated at the electrode locations under 12 different noise levels. Altogether, ∼50,000 thresholded CT artifact arrays were simulated in these scenarios, and validated with real data from 17 patients regarding the coordinates' spatial deformation, and the CT artifacts' shape, intensity distribution, and noise level. Finally, we provide an example of how the simulation platform is used to characterize the performance of two cluster-based localization methods.

Conclusion

We successfully developed the first platform to model implanted intracranial grids, strips, and depth electrodes and realistically simulate thresholded CT artifacts and their noise. These methods provide a basis for developing more complex models, while simulations allow systematic evaluation of the performance of electrode localization techniques. The methods described in this article, and the results obtained from the simulations, are freely available via open repositories. A graphical user interface implementation is also accessible via the open-source iElectrodes toolbox.

Previous, current, and future stereotactic EEG techniques for localising epileptic foci

INTRODUCTION

Drug-resistant focal epilepsy presents a significant morbidity burden globally, and epilepsy surgery has been shown to be an effective treatment modality. Therefore, accurate identification of the epileptogenic zone for surgery is crucial, and in those with unclear noninvasive data, stereoencephalography is required.

AREAS COVERED

This review covers the history and current practices in the field of intracranial EEG, particularly analyzing how stereotactic image-guidance, robot-assisted navigation, and improved imaging techniques have increased the accuracy, scope, and use of SEEG globally.

EXPERT OPINION

We provide a perspective on the future directions in the field, reviewing improvements in predicting electrode bending, image acquisition, machine learning and artificial intelligence, advances in surgical planning and visualization software and hardware. We also see the development of EEG analysis tools based on machine learning algorithms that are likely to work synergistically with neurophysiology experts and improve the efficiency of EEG and SEEG analysis and 3D visualization. Improving computer-assisted planning to minimize manual input from the surgeon, and seamless integration into an ergonomic and adaptive operating theater, incorporating hybrid microscopes, virtual and augmented reality is likely to be a significant area of improvement in the near future.

LeGUI: A Fast and Accurate Graphical User Interface for Automated Detection and Anatomical Localization of Intracranial Electrodes

Accurate anatomical localization of intracranial electrodes is important for identifying the seizure foci in patients with epilepsy and for interpreting effects from cognitive studies employing intracranial electroencephalography. Localization is typically performed by coregistering postimplant computed tomography (CT) with preoperative magnetic resonance imaging (MRI). Electrodes are then detected in the CT, and the corresponding brain region is identified using the MRI. Many existing software packages for electrode localization chain together separate preexisting programs or rely on command line instructions to perform the various localization steps, making them difficult to install and operate for a typical user. Further, many packages provide solutions for some, but not all, of the steps needed for confident localization. We have developed software, Locate electrodes Graphical User Interface (LeGUI), that consists of a single interface to perform all steps needed to localize both surface and depth/penetrating intracranial electrodes, including coregistration of the CT to MRI, normalization of the MRI to the Montreal Neurological Institute template, automated electrode detection for multiple types of electrodes, electrode spacing correction and projection to the brain surface, electrode labeling, and anatomical targeting. The software is written in MATLAB, core image processing is performed using the Statistical Parametric Mapping toolbox, and standalone executable binaries are available for Windows, Mac, and Linux platforms. LeGUI was tested and validated on 51 datasets from two universities. The total user and computational time required to process a single dataset was approximately 1 h. Automatic electrode detection correctly identified 4362 of 4695 surface and depth electrodes with only 71 false positives. Anatomical targeting was verified by comparing electrode locations from LeGUI to locations that were assigned by an experienced neuroanatomist. LeGUI showed a 94% match with the 482 neuroanatomist-assigned locations. LeGUI combines all the features needed for fast and accurate anatomical localization of intracranial electrodes into a single interface, making it a valuable tool for intracranial electrophysiology research.

Computational modeling of seizure spread on a cortical surface

In the field of computational epilepsy, neural field models helped to understand some large-scale features of seizure dynamics. These insights however remain on general levels, without translation to the clinical settings via personalization of the model with the patient-specific structure. In particular, a link was suggested between epileptic seizures spreading across the cortical surface and the so-called theta-alpha activity (TAA) pattern seen on intracranial electrographic signals, yet this link was not demonstrated on a patient-specific level. Here we present a single patient computational study linking the seizure spreading across the patient-specific cortical surface with a specific instance of the TAA pattern recorded in the patient. Using the realistic geometry of the cortical surface we perform the simulations of seizure dynamics in The Virtual Brain platform, and we show that the simulated electrographic signals qualitatively agree with the recorded signals. Furthermore, the comparison with the simulations performed on surrogate surfaces reveals that the best quantitative fit is obtained for the real surface. The work illustrates how the patient-specific cortical geometry can be utilized in The Virtual Brain for personalized model building, and the importance of such approach.

Validation of semi-automated anatomically labeled SEEG contacts in a brain atlas for mapping connectivity in focal epilepsy

OBJECTIVE

Stereotactic electroencephalography (SEEG) has been widely used to explore the epileptic network and localize the epileptic zone in patients with medically intractable epilepsy. Accurate anatomical labeling of SEEG electrode contacts is critically important for correctly interpreting epileptic activity. We present a method for automatically assigning anatomical labels to SEEG electrode contacts using a 3D-segmented cortex and coregistered postoperative CT images.

METHOD

Stereotactic electroencephalography electrode contacts were spatially localized relative to the brain volume using a standard clinical procedure. Each contact was then assigned an anatomical label by clinical epilepsy fellows. Separately, each contact was automatically labeled by coregistering the subject's MRI to the USCBrain atlas using the BrainSuite software and assigning labels from the atlas based on contact locations. The results of both labeling methods were then compared, and a subsequent vetting of the anatomical labels was performed by expert review.

RESULTS

Anatomical labeling agreement between the two methods for over 17 000 SEEG contacts was 82%. This agreement was consistent in patients with and without previous surgery (P = .852). Expert review of contacts in disagreement between the two methods resulted in agreement with the atlas based over manual labels in 48% of cases, agreement with manual over atlas-based labels in 36% of cases, and disagreement with both methods in 16% of cases. Labels deemed incorrect by the expert review were then categorized as either in a region directly adjacent to the correct label or as a gross error, revealing a lower likelihood of gross error from the automated method.

SIGNIFICANCE

The method for semi-automated atlas-based anatomical labeling we describe here demonstrates potential to assist clinical workflow by reducing both analysis time and the likelihood of gross anatomical error. Additionally, it provides a convenient means of intersubject analysis by standardizing the anatomical labels applied to SEEG contact locations across subjects.

Gray Matter Sampling Differences Between Subdural Electrodes and Stereoelectroencephalography Electrodes

Objective: Stereoelectroencephalography (SEEG) has seen a recent increase in popularity in North America; however, concerns regarding the spatial sampling capabilities of SEEG remain. We aimed to quantify and compare the spatial sampling of subdural electrode (SDE) and SEEG implants.

Methods: Patients with drug-resistant epilepsy who underwent invasive monitoring were included in this retrospective case-control study. Ten SEEG cases were compared with ten matched SDE cases based on clinical presentation and pre-implantation hypothesis. To quantify gray matter sampling, MR and CT images were coregistered and a 2.5mm radius sphere was superimposed over the center of each electrode contact. The estimated recording volume of gray matter was defined as the cortical voxels within these spherical models. Paired t-tests were performed to compare volumes and locations of SDE and SEEG recording. A Ripley's K-function analysis was performed to quantify differences in spatial distributions.

Results: The average recording volume of gray matter by each individual contact was similar between the two modalities. SEEG implants sampled an average of 20% more total gray matter, consisted of an average of 17% more electrode contacts, and had 77% more of their contacts covering gray matter within sulci. Insular coverage was only achieved with SEEG. SEEG implants generally consist of discrete areas of dense local coverage scattered across the brain; while SDE implants cover relatively contiguous areas with lower density recording.

Significance: Average recording volumes per electrode contact are similar for SEEG and SDE, but SEEG may allow for greater overall volumes of recording as more electrodes can be routinely implanted. The primary difference lies in the location and distribution of gray matter than can be sampled. The selection between SEEG and SDE implantation depends on sampling needs of the invasive implant.

Fast Automated Stereo-EEG Electrode Contact Identification and Labeling Ensemble

INTRODUCTION

Stereotactic electroencephalography (SEEG) has emerged as the preferred modality for intracranial monitoring in drug-resistant epilepsy (DRE) patients being evaluated for neurosurgery. After implantation of SEEG electrodes, it is important to determine the neuroanatomic locations of electrode contacts (ECs), to localize ictal onset and propagation, and integrate functional information to facilitate surgical decisions. Although there are tools for coregistration of preoperative MRI and postoperative CT scans, identification, sorting, and labeling of SEEG ECs is often performed manually, which is resource intensive. We report development and validation of a software named Fast Automated SEEG Electrode Contact Identification and Labeling Ensemble (FASCILE).

METHODS

FASCILE is written in Python 3.8.3 and employs a novel automated method for identifying ECs, assigning them to respected SEEG electrodes, and labeling. We compared FASCILE with our clinical process of identifying, sorting, and labeling ECs, by computing localization error in anteroposterior, superoinferior, and lateral dimensions. We also measured mean Euclidean distances between ECs identified by FASCILE and the clinical method. We compared time taken for EC identification, sorting, and labeling for the software developer using FASCILE, a first-time clinical user using FASCILE, and the conventional clinical process.

RESULTS

Validation in 35 consecutive DRE patients showed a mean overall localization error of 0.73 ± 0.15 mm. FASCILE required 10.7 ± 5.5 min/patient for identifying, sorting, and labeling ECs by a first-time clinical user, compared to 3.3 ± 0.7 h/patient required for the conventional clinical process.

CONCLUSION

Given the accuracy, speed, and ease of use, we expect FASCILE to be used frequently for SEEG-driven epilepsy surgery. It is freely available for noncommercial use. FASCILE is specifically designed to expedite localization of ECs, assigning them to respective SEEG electrodes (sorting), and labeling them and not for coregistration of CT and MRI data as there are commercial software available for this purpose.

Patient-specific prediction of SEEG electrode bending for stereotactic neurosurgical planning

Purpose 

Electrode bending observed after stereotactic interventions is typically not accounted for in either computer-assisted planning algorithms, where straight trajectories are assumed, or in quality assessment, where only metrics related to entry and target points are reported. Our aim is to provide a fully automated and validated pipeline for the prediction of stereo-electroencephalography (SEEG) electrode bending.

Methods 

We transform electrodes of 86 cases into a common space and compare features-based and image-based neural networks on their ability to regress local displacement (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathbf{lu} $$\end{document}lu) or electrode bending (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{\mathbf{eb }}$$\end{document}eb^). Electrodes were stratified into six groups based on brain structures at the entry and target point. Models, both with and without Monte Carlo (MC) dropout, were trained and validated using tenfold cross-validation.

Results 

mage-based models outperformed features-based models for all groups, and models that predicted \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathbf{lu} $$\end{document}lu performed better than for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{\mathbf{eb }}$$\end{document}eb^. Image-based model prediction with MC dropout resulted in lower mean squared error (MSE) with improvements up to 12.9% (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathbf{lu} $$\end{document}lu) and 39.9% (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{\mathbf{eb }}$$\end{document}eb^), compared to no dropout. Using an image of brain tissue types (cortex, white and deep grey matter) resulted in similar, and sometimes better performance, compared to using a T1-weighted MRI when predicting \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathbf{lu} $$\end{document}lu. When inferring trajectories of image-based models (brain tissue types), 86.9% of trajectories had an MSE\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le 1$$\end{document}≤1 mm.

Conclusion 

An image-based approach regressing local displacement with an image of brain tissue types resulted in more accurate electrode bending predictions compared to other approaches, inputs, and outputs. Future work will investigate the integration of electrode bending into planning and quality assessment algorithms.

Supplementary Information

The online version supplementary material available at 10.1007/s11548-021-02347-8.

Increasing the accuracy of 3D EEG implantations

OBJECTIVE

The accuracy of stereoelectroencephalography (SEEG) electrode implantation is an important factor in maximizing its safety. The authors established a quality assurance (QA) process to aid advances in implantation accuracy.

METHODS

The accuracy of three consecutive modifications of a frameless implantation technique was quantified in three cohorts comprising 22, 8, and 23 consecutive patients. The modifications of the technique aimed to increase accuracy of the bolt placement.

RESULTS

The lateral shift of the axis of the implanted bolt at the level of the planned entry point was reduced from a mean of 3.0 ± 1.6 mm to 1.4 ± 0.8 mm. The lateral shift of the axis of the implanted bolt at the level of the planned target point was reduced from a mean of 3.8 ± 2.5 mm to 1.6 ± 0.9 mm.

CONCLUSIONS

This QA framework helped to isolate and quantify the factors introducing inaccuracy in SEEG implantation, and to monitor ongoing accuracy and the effect of technique modifications.

Automated computation and analysis of accuracy metrics in stereoencephalography

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Automatic computation of SEEG accuracy metrics agree with those done manually.

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The choice of image to generate a scalp model has an effect on entry point metrics.

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Metrics have the lowest mean and variability when using an electrode bolt axis.

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Lateral shift deviation should include a measure of insertion depth error.

Background

Implantation accuracy of electrodes during neurosurgical interventions is necessary to ensure safety and efficacy. Typically, metrics are computed by visual inspection which is tedious, prone to inter-/intra-observer variation, and difficult to replicate across sites.

New Method

We propose an automated approach for computing implantation metrics and investigate potential sources of error. We focus on accuracy metrics commonly reported in the literature to validate our approach against metrics computed manually including entry point (EP) and target point (TP) localisation errors and angle differences between planned and implanted trajectories in 15 patients with a total of 158 stereoelectroencephalography (SEEG) electrodes. We evaluate the effect of line-of-best-fit approaches, EP definition and lateral versus Euclidean distance on metrics to provide recommendations for reporting implantation accuracy metrics.

Results

We found no bias between manual and automated approaches for calculating accuracy metrics with limits of agreement of ±1 mm and ±1°. Automated metrics are robust to sources of errors including registration and electrode bending. We observe the highest error in EP deviations of μ = 0.25 mm when the post-implantation CT is used to define the point of entry.

Comparison with Existing Method(s)

We found no reports of automated approaches for quality assessment of SEEG electrode implantation. Neither the choice of metrics nor the possible errors that could occur have been investigated previously.

Conclusions

Our automated approach is useful to avoid human errors, unintentional bias and variation that may be introduced when manually computing metrics. Our work is relevant and timely to facilitate comparisons of studies reporting implantation accuracy.

Surface-Based Registration of MR Scan versus Refined Anatomy-Based Registration of CT Scan: Effect on the Accuracy of SEEG Electrodes Implantation Performed in Prone Position under Frameless Neuronavigation

INTRODUCTION

Stereoelectroencephalography (SEEG) refers to a commonly used diagnostic procedure to localise and define the epileptogenic zone of refractory focal epilepsies, by means of minimally invasive operation techniques without large craniotomies.

OBJECTIVE

This study aimed to investigate the influence of different registration methods on the accuracy of SEEG electrode implantation under neuronavigation for paediatric patients with refractory epilepsy.

METHODS

The clinical data of 18 paediatric patients with refractory epilepsy were retrospectively analysed. The SEEG electrodes were implanted under optical neuronavigation while the patients were in the prone position. Patients were divided into two groups on the basis of the surface-based registration of MR scan method and refined anatomy-based registration of CT scan. Registration time, accuracy, and the differences between electrode placement and preoperative planned position were analysed.

RESULTS

Thirty-six electrodes in 7 patients were placed under surface-based registration of MR scan, and 45 electrodes in 11 patients were placed under refined anatomy-based registration of CT scan. The registration time of surface-based registration of MR scan and refined anatomy-based registration of CT scan was 45 ± 12 min and 10 ± 4 min. In addition, the mean registration error, the error of insertion point, and target error were 3.6 ± 0.7 mm, 2.7 ± 0.7 mm, and 3.1 ± 0.5 mm in the surface-based registration of MR scan group, and 1.1 ± 0.3 mm, 1.5 ± 0.5 mm, and 2.2 ± 0.6 mm in the refined anatomy-based registration of CT scan group. The differences between the two registration methods were statistically significant.

CONCLUSIONS

The refined anatomy-based registration of CT scan method can improve the registration efficiency and electrode placement accuracy, and thereby can be considered as the preferred registration method in the application of SEEG electrode implantation under neuronavigation for treatment of paediatric intractable epilepsy.

Computer-Assisted Planning for Stereoelectroencephalography (SEEG)

Stereoelectroencephalography (SEEG) is a diagnostic procedure in which multiple electrodes are stereotactically implanted within predefined areas of the brain to identify the seizure onset zone, which needs to be removed to achieve remission of focal epilepsy. Computer-assisted planning (CAP) has been shown to improve trajectory safety metrics and generate clinically feasible trajectories in a fraction of the time needed for manual planning. We report a prospective validation study of the use of EpiNav (UCL, London, UK) as a clinical decision support software for SEEG. Thirteen consecutive patients (125 electrodes) undergoing SEEG were prospectively recruited. EpiNav was used to generate 3D models of critical structures (including vasculature) and other important regions of interest. Manual planning utilizing the same 3D models was performed in advance of CAP. CAP was subsequently employed to automatically generate a plan for each patient. The treating neurosurgeon was able to modify CAP generated plans based on their preference. The plan with the lowest risk score metric was stereotactically implanted. In all cases (13/13), the final CAP generated plan returned a lower mean risk score and was stereotactically implanted. No complication or adverse event occurred. CAP trajectories were generated in 30% of the time with significantly lower risk scores compared to manually generated. EpiNav has successfully been integrated as a clinical decision support software (CDSS) into the clinical pathway for SEEG implantations at our institution. To our knowledge, this is the first prospective study of a complex CDSS in stereotactic neurosurgery and provides the highest level of evidence to date.

Comparison of interactive and automatic segmentation of stereoelectroencephalography electrodes on computed tomography post-operative images: preliminary results

Stereoelectroencephalography is a surgical procedure used in the treatment of pharmacoresistant epilepsy. Multiple electrodes are inserted in the patient's brain in order to record the electrical activity and detect the epileptogenic zone at the source of the seizures. An accurate localisation of their contacts on post-operative images is a crucial step to interpret the recorded signals and achieve a successful resection afterwards. In this Letter, the authors propose interactive and automatic methods to help the surgeon with the segmentation of the electrodes and their contacts. Then, they present a preliminary comparison of the methods in terms of accuracy and processing time through experimental measurements performed by two users, and discuss these first results. The final purpose of this work is to assist the neurosurgeons and neurologists in the contacts localisation procedure, make it faster, more precise and less tedious.