Automation Advances in Stereoelectroencephalography Planning

Vakharia, Vejay N.; Duncan, John S.

doi:10.1016/j.nec.2020.03.005

Cited by 8 publications

(4 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In generating these priors, we purposefully excluded trajectories that targeted unique patient-specific abnormalities, such as focal cortical dysplasia, as these would not be generalizable when considering trajectory planning in other patients. In such cases, computer-assisted planning can still be utilized using the segmentation of the lesion as the target and allow the algorithm to choose the most appropriate entry point (12). As a further analysis, we implemented a K-NN classifier as part of an adaptive learning algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…The EpiNav™ algorithm generates SEEG trajectories based on optimization of user-defined parameters, which include intracerebral length, drilling angle to the skull, gray matter sampling ratio, minimum distance from vasculature, risk score, and avoidance of critical structures ( 12 ). For an in-depth discussion on planning parameter selection see ( 13 ). The user-defined parameters applied during this study are shown in Table 1 .…”

Section: Methodsmentioning

confidence: 99%

“…Depending on the spatial distribution of the SEEG implantation and the patient's individual anatomy, injections of the ipsilateral internal carotid artery and a vertebral artery were performed. The EpiNav TM algorithm generates SEEG trajectories based on optimization of user-defined parameters, which include intracerebral length, drilling angle to the skull, gray matter sampling ratio, minimum distance from vasculature, risk score, and avoidance of critical structures (12). For an in-depth discussion on planning parameter selection see (13).…”

Section: Ethical Approvalmentioning

confidence: 99%

“…Stereotactic neurosurgery requires precise pre-operative trajectory planning and accurate implementation to ensure safety and efficacy. Stereoelectroencephalography (SEEG) is a diagnostic procedure in which multiple electrodes, typically [10][11][12][13][14][15][16], are implanted within the brain in patients with drugrefractory focal epilepsy to approximate the epileptogenic zone so that subsequent resective or ablative interventions can render the patient seizure-free. The most significant risk of this procedure is intracerebral hemorrhage, which results in significant morbidity in 2-3% of cases (1).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Refining Planning for Stereoelectroencephalography: A Prospective Validation of Spatial Priors for Computer-Assisted Planning With Application of Dynamic Learning

et al. 2020

Self Cite

View full text Add to dashboard Cite

Objective: Stereoelectroencephalography (SEEG) is a procedure in which many electrodes are stereotactically implanted within different regions of the brain to estimate the epileptogenic zone in patients with drug-refractory focal epilepsy. Computer-assisted planning (CAP) improves risk scores, gray matter sampling, orthogonal drilling angles to the skull and intracerebral length in a fraction of the time required for manual planning. Due to differences in planning practices, such algorithms may not be generalizable between institutions. We provide a prospective validation of clinically feasible trajectories using "spatial priors" derived from previous implantations and implement a machine learning classifier to adapt to evolving planning practices. Methods: Thirty-two patients underwent consecutive SEEG implantations utilizing computer-assisted planning over 2 years. Implanted electrodes from the first 12 patients (108 electrodes) were used as a training set from which entry and target point spatial priors were generated. CAP was then prospectively performed using the spatial priors in a further test set of 20 patients (210 electrodes). A K-nearest neighbor (K-NN) machine learning classifier was implemented as an adaptive learning method to modify the spatial priors dynamically. Results: All of the 318 prospective computer-assisted planned electrodes were implanted without complication. Spatial priors developed from the training set generated clinically feasible trajectories in 79% of the test set. The remaining 21% required entry or target points outside of the spatial priors. The K-NN classifier was able to dynamically model real-time changes in the spatial priors in order to adapt to the evolving planning requirements. Conclusions: We provide spatial priors for common SEEG trajectories that prospectively integrate clinically feasible trajectory planning practices from previous Vakharia et al. Spatial Priors for Computer-Assisted Planning SEEG implantations. This allows institutional SEEG experience to be incorporated and used to guide future implantations. The deployment of a K-NN classifier may improve the generalisability of the algorithm by dynamically modifying the spatial priors in real-time as further implantations are performed.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Ethical Approvalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Refining Planning for Stereoelectroencephalography: A Prospective Validation of Spatial Priors for Computer-Assisted Planning With Application of Dynamic Learning

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

Optimization of patient-specific stereo-EEG recording sensitivity

Dessert,

Thio,

Grill

2023

Brain Communications

View full text Add to dashboard Cite

Stereo-EEG is a minimally invasive technique used to localize the origin of epileptic activity (the epileptogenic zone) in patients with drug-resistant epilepsy. However, current stereo-EEG trajectory planning methods are agnostic to the spatial recording sensitivity of implanted electrodes. In this study, we used image-based patient-specific computational models to design optimized stereo-EEG electrode configurations. Patient-specific optimized electrode configurations exhibited substantially higher recording sensitivity than clinically implanted configurations, and this may lead to a more accurate delineation of the epileptogenic zone. The optimized configurations also achieved equally good or better recording sensitivity with fewer electrodes compared with clinically implanted configurations, and this may reduce the risk for complications, including intracranial haemorrhage. This approach improves localization of the epileptogenic zone by transforming the clinical use of stereo-EEG from a discrete ad hoc sampling to an intelligent mapping of the regions of interest.

show abstract

Projection-Based Collision Detection Algorithm for Stereoelectroencephalography Electrode Risk Assessment and Re-Planning

et al. 2021

View full text Add to dashboard Cite

Surgical planning is crucial to Stereoelectroencephalography (SEEG), a minimally invasive procedure that requires clinicians to operate with no direct view of the brain. Decisions making involves different clinical specialties and requires analysis of multiple multimodal datasets. We present a DepthMap tool designed to localize, measure, and visualize surgical risk, and an AlternativeFinder tool, designed to search for alternative trajectories maintaining adherence to the initial trajectory with three different replanning strategies: similar entry, similar target, or parallel trajectory. The two tools transform the 3D problem into the 2D domain using projective geometry and distance mapping. Both use the graphics processing unit (GPU) to create a 2D depth image used by DepthMap for measurement and visualization, and by AlternativeFinder to find alternative trajectories. Tools were tested with 12 SEEG cases using digital subtraction angiography. DepthMap was used to measure vessel distance. AlternativeFinder was then used to search for alternatives. Computation time and displacements of the entry and target points for each trajectory and adherence strategy were recorded. The DepthMap tool found vessels in 118 initial trajectories (out of 145). Ninety alternative trajectories were found to meet the required avascular constraints (average 820K alternatives evaluated per initial trajectory). The average computation time was 449 ms per initial trajectory (77 ms when alternatives were found). The tools presented helped clinicians examine and re-plan SEEG trajectories to avoid vascular risks using three adherence strategies. Quantitative measurement of the adherence shows the potential of this tool for clinical use.

show abstract

Automation Advances in Stereoelectroencephalography Planning

Cited by 8 publications

References 41 publications

Refining Planning for Stereoelectroencephalography: A Prospective Validation of Spatial Priors for Computer-Assisted Planning With Application of Dynamic Learning

Refining Planning for Stereoelectroencephalography: A Prospective Validation of Spatial Priors for Computer-Assisted Planning With Application of Dynamic Learning

Optimization of patient-specific stereo-EEG recording sensitivity

Projection-Based Collision Detection Algorithm for Stereoelectroencephalography Electrode Risk Assessment and Re-Planning

Contact Info

Product

Resources

About