Enhanced co-registration methods to improve intracranial electrode contact localization

Anatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods

BACKGROUND

Intracranial electrodes are typically localized from post-implantation CT artifacts. Automatic algorithms localizing low signal-to-noise ratio artifacts and high-density electrode arrays are missing. Additionally, implantation of grids/strips introduces brain deformations, resulting in registration errors when fusing post-implantation CT and pre-implantation MR images. Brain-shift compensation methods project electrode coordinates to cortex, but either fail to produce smooth solutions or do not account for brain deformations.

NEW METHODS

We first introduce GridFit, a model-based fitting approach that simultaneously localizes all electrodes' CT artifacts in grids, strips, or depth arrays. Second, we present CEPA, a brain-shift compensation algorithm combining orthogonal-based projections, spring-mesh models, and spatial regularization constraints.

RESULTS

We tested GridFit on ∼6000 simulated scenarios. The localization of CT artifacts showed robust performance under difficult scenarios, such as noise, overlaps, and high-density implants (<1 mm errors). Validation with data from 20 challenging patients showed 99% accurate localization of the electrodes (3160/3192). We tested CEPA brain-shift compensation with data from 15 patients. Projections accounted for simple mechanical deformation principles with < 0.4 mm errors. The inter-electrode distances smoothly changed across neighbor electrodes, while changes in inter-electrode distances linearly increased with projection distance.

COMPARISON WITH EXISTING METHODS

GridFit succeeded in difficult scenarios that challenged available methods and outperformed visual localization by preserving the inter-electrode distance. CEPA registration errors were smaller than those obtained for well-established alternatives. Additionally, modeling resting-state high-frequency activity in five patients further supported CEPA.

CONCLUSION

GridFit and CEPA are versatile tools for registering intracranial electrode coordinates, providing highly accurate results even in the most challenging implantation scenarios. The methods are implemented in the iElectrodes open-source toolbox.

Cerebral blood flow quantification with multi-delay arterial spin labeling in ischemic stroke and the association with early neurological outcome

Restoring blood flow to brain tissue at risk of infarction is essential for tissue survival and clinical outcome. We used cerebral blood flow (CBF) quantified with multiple post-labeling delay (PLD) pseudocontinuous arterial spin labeling (ASL) MRI after ischemic stroke and assessed the association between CBF and early neurological outcome. We acquired ASL with 7 PLDs at 3.0 T in large vessel occlusion stroke patients at 24 h. We quantified CBF relative to the contralateral hemisphere (rCBF) and defined hyperperfusion as a ≥30% increase and hypoperfusion as a ≥40% decrease in rCBF. We included 44 patients (median age: 70 years, median NIHSS: 13, 40 treated with endovascular thrombectomy) of whom 37 were recanalized. Hyperperfusion in ischemic core occurred in recanalized but not in non-recanalized patients (65.8% vs 0%, p = 0.006). Hypoperfusion occurred only in the latter group (0% vs 85.7%, p < 0.001). In recanalized patients, hyperperfusion was also seen in salvaged penumbra (38.9%). Higher rCBF in ischemic core (aβ, -2.75 [95% CI: -4.11 to -1.40]) and salvaged penumbra (aβ, -5.62 [95% CI: -9.57 to -1.68]) was associated with lower NIHSS scores at 24 h. In conclusion, hyperperfusion frequently occurs in infarcted and salvaged brain tissue following successful recanalization and early neurological outcome is positively associated with the level of reperfusion.

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.

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.

Safety of MRI in the localization of implanted intracranial electrodes for refractory epilepsy

BACKGROUND AND PURPOSE

This is an observational study to evaluate the safety of magnetic resonance imaging (MRI) to localize subdural grids and depth electrodes in patients with refractory epilepsy using a 1.5 Tesla MR scanner.

METHODS

We implemented an optimized MRI protocol providing adequate image quality for the assessment of subdural grids and depth electrodes, while minimizing the specific absorption rate (SAR). We reviewed all MRI studies performed in patients with subdural grids and depth electrodes between January 2010 and October 2018. Image quality was graded as acceptable or nonacceptable for the assessment of intracranial device positioning. We reviewed the medical record and any imaging obtained after intracranial implant removal for adverse event or complication occurring during and after the procedure.

RESULTS

Ninety-nine patients with refractory epilepsy underwent MRI scans using a magnetization-prepared rapid acquisition of gradient echo sequence and a transmit-receive head coil with depth electrodes and subdural grids in place. Two patients underwent two separate depth electrode implantations for a total of 101 procedures and MRI scans. No clinical adverse events were reported during or immediately after imaging. Image quality was graded as acceptable for 97 MRI scans. Review of follow-up CT and MRI studies after implant removal, available for 70 patients, did not demonstrate unexpected complications in 69 patients.

CONCLUSION

In our experience, a low SAR MRI protocol can be used to safely localize intracranial subdural grids and depth electrode in patients with refractory epilepsy.

Efficient automated localization of ECoG electrodes in CT images via shape analysis

Purpose

People with drug-refractory epilepsy are potential candidates for surgery. In many cases, epileptogenic zone localization requires intracranial investigations, e.g., via ElectroCorticoGraphy (ECoG), which uses subdural electrodes to map eloquent areas of large cortical regions. Precise electrodes localization on cortical surface is mandatory to delineate the seizure onset zone. Simple thresholding operations performed on patients’ computed tomography (CT) volumes recognize electrodes but also other metal objects (e.g., wires, stitches), which need to be manually removed. A new automated method based on shape analysis is proposed, which provides substantially improved performances in ECoG electrodes recognition.

Methods

The proposed method was retrospectively tested on 24 CT volumes of subjects with drug-refractory focal epilepsy, presenting a large number (> 1700) of round platinum electrodes. After CT volume thresholding, six geometric features of voxel clusters (volume, symmetry axes lengths, circularity and cylinder similarity) were used to recognize the actual electrodes among all metal objects via a Gaussian support vector machine (G-SVM). The proposed method was further tested on seven CT volumes from a public repository. Simultaneous recognition of depth and ECoG electrodes was also investigated on three additional CT volumes, containing penetrating depth electrodes.

Results

The G-SVM provided a 99.74% mean classification accuracy across all 24 single-patient datasets, as well as on the combined dataset. High accuracies were obtained also on the CT volumes from public repository (98.27% across all patients, 99.68% on combined dataset). An overall accuracy of 99.34% was achieved for the recognition of depth and ECoG electrodes.

Conclusions

The proposed method accomplishes automated ECoG electrodes localization with unprecedented accuracy and can be easily implemented into existing software for preoperative analysis process. The preliminary yet surprisingly good results achieved for the simultaneous depth and ECoG electrodes recognition are encouraging.

Ethical approval n°NCT04479410 by “IRCCS Neuromed” (Pozzilli, Italy), 30th July 2020.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11548-021-02325-0.

Fabrication and validation of reference structures for the localization of subdural standard- and micro-electrodes in MRI

OBJECTIVE

Report simple reference structure fabrication and validate the precise localization of subdural micro- and standard electrodes in magnetic resonance imaging (MRI) in phantom experiments.

APPROACH

Electrode contacts with diameters of 0.3 mm and 4 mm are localized in 1.5 T MRI using reference structures made of silicone and iron oxide nanoparticle doping. The precision of the localization procedure was assessed for several standard MRI sequences and implant orientations in phantom experiments and compared to common clinical localization procedures.

MAIN RESULTS

A localization precision of 0.41 ± 0.20 mm could be achieved for both electrode diameters compared to 1.46 ± 0.69 mm that was achieved for 4 mm standard electrode contacts localized using a common clinical standard method. The new reference structures are intrinsically bio-compatible, and they can be detected with currently available feature detection software so that a clinical implementation of this technology should be feasible.

SIGNIFICANCE

Neuropathologies are increasingly diagnosed and treated with subdural electrodes, where the exact localization of the electrode contacts with respect to the patient's cortical anatomy is a prerequisite for the procedure. Post-implantation electrode localization using MRI may be advantageous compared to the common alternative of CT-MRI image co-registration, as it avoids systematic localization errors associated with the co-registration itself, as well as brain shift and implant movement. Additionally, MRI provides superior soft tissue contrast for the identification of brain lesions without exposing the patient to ionizing radiation. Recent studies show that smaller electrodes and high-density electrode grids are ideal for clinical and research purposes, but the localization of these devices in MRI has not been demonstrated.

Shift in electrocorticography electrode locations after surgical implantation in children

Interpreting electrocorticography (ECoG) in the context of neuroimaging requires that multimodal information be integrated accurately. However, the implantation of ECoG electrodes can shift the brain impacting the spatial interpretation of electrode locations in the context of pre-implant imaging. We characterized the amount of shift in ECoG electrode locations immediately after implant in a pediatric population. Electrode-shift was quantified as the difference in the electrode locations immediately after surgery (via post-operation CT) compared to the brain surface before the operation (pre-implant T1 MRI). A total of 1140 ECoG contracts were assessed across 18 patients ranging from 3 to 19 (12.1 ± 4.8) years of age who underwent intracranial monitoring in preparation for epilepsy resection surgery. Patients had an average of 63 channels assessed with an average of 5.64 ± 3.27 mm shift from the pre-implant brain surface within 24 h of implant. This shift significantly increased with estimated intracranial volume, but not age. Shift also varied significantly depending of the lobe the contact was over; where contacts on the temporal and frontal lobe had less shift than the parietal. Furthermore, contacts on strips had significantly less shift than those on grids. The shift in the brain surface due to ECoG implantation could lead to a misinterpretation of contact location particularly in patients with larger intracranial volume and for grid contacts over the parietal lobes.

Interpretation of the Intracranial Stereoelectroencephalography Signal

The intracranial electroencephalogram (iEEG) is essential in decision making for epilepsy surgery. Although localization of epileptogenic brain regions by means of iEEG has been the gold standard for surgical decision-making for more than 70 years, established guidelines for what constitutes genuine iEEG epileptic activity and what is normal brain activity are not available. This review provides a summary of the current state of knowledge and understanding on normal iEEG entities and variants, the effects of sleep on regional and lobar iEEG, iEEG patterns of interictal and ictal epileptic activity and their relation to well-described epileptogenic pathologies and surgical outcome.

Localization of movable electrodes in a multi-electrode microdrive in nonhuman primates

BACKGROUND

Recently, large-scale semi-chronic recording systems have been developed, unique in their capability to record simultaneously from multiple individually moveable electrodes. As these recording systems can cover a large area, knowledge of the exact location of each individual electrode is crucial. Currently, the only method of keeping track of electrode depth and thus location is through detailed notebook keeping on neural activity.

NEW METHOD

We have improved the electrode localization by combining pre- and postoperative anatomical magnetic resonance imaging (MRI) scans with high resolution computed tomography (CT) scans throughout the experiment, and validated our method by comparing the resulting location estimates with traditional notebook-keeping. Finally, the actual location of a selection of electrodes was marked at the end of the experiment by creating small metallic depositions using electrical stimulation, and thereby made visible on MRI.

RESULTS

Combining CT scans with a high resolution, artefact reducing sequence during the experiment with a preoperative MRI scan provides crucial information about the exact electrode location of multielectrode arrays with individually moveable electrodes.

COMPARISON WITH EXISTING METHODS

The information obtained from the hybrid CT-MR image and the notes on spiking activity showed a similar pattern, with the clear advantage of the visualization of the exact position of the electrodes using our method.

CONCLUSIONS

The described technique allows for a precise anatomical identification of the recorded brain areas and thus to draw strong conclusions about the role of each targeted cortical area in the behavior under study.