Details

Autor(en) / Beteiligte

• Granados, Alejandro
• Rodionov, Roman
• Vakharia, Vejay
• McEvoy, Andrew W.
• Miserocchi, Anna
• O'Keeffe, Aidan G.
• Duncan, John S.
• Sparks, Rachel
• Ourselin, Sébastien

Titel

Automated computation and analysis of accuracy metrics in stereoencephalography

Ist Teil von

• Journal of neuroscience methods, 2020-07, Vol.340, p.108710-108710, Article 108710

Ort / Verlag

Netherlands: Elsevier B.V

Erscheinungsjahr

2020

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •Automatic computation of SEEG accuracy metrics agree with those done manually.•The choice of image to generate a scalp model has an effect on entry point metrics.•Metrics have the lowest mean and variability when using an electrode bolt axis.•Lateral shift deviation should include a measure of insertion depth error. 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. 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. 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. 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. 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.