Stability, Structure and Scale: Improvements in Multi-modal Vessel Extraction for SEEG Trajectory Planning

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 signi cant 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 pro le 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 datasets from two image modalities available within the planning system were used for evaluation. The mean Dice similarity coe cient was 0.89 ± 0.04, representing a statistically signi cantly 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

SEEG trajectory planning: combining stability, structure and scale in vessel extraction

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)

3D path planning for flexible needle steering in neurosurgery

Background: We propose a 3D path planning method to steer flexible needles along curved paths in the context of Deep Brain Stimulation (DBS) procedures. Methods: Our approach is based on a rapidly‐exploring random tree strategy and it takes into account constraints coming from anatomical obstacles and physical constraints dictated by flexible needle kinematics. The strategy is evaluated in simulation on a realistic 3D CAD model of the brain. Results: The subthalamic nucleus (STN) and the fornix can be reached along several curved paths from various entry points. As compared to the usual straight line path, these curved paths avoid tissue damage to important neural structures while allowing for a much greater selection of entry points. Conclusions: This path planning method offers alternative curved paths to reach DBS targets with flexible needles. The method potentially leads to safer paths and additional entry points capable of reaching the desired stimulation targets

A computer assisted planning system for the placement of sEEG electrodes in the treatment of epilepsy

Approximately 20-30% of patients with focal epilepsy are medically refractory and may be candidates for curative surgery. Stereo EEG is the placement of multiple depth electrodes into the brain to record seizure activity and precisely identify the area to be resected. The two important criteria for electrode implantation are accurate navigation to the target area, and avoidance of critical structures such as blood vessels. In current practice neurosurgeons have no assistance in the planning of the electrode trajectories. To provide assistance a real-time solution was developed that first identifies the potential entry points by analysing the entry-angle, then computes the associated risks for trajectories starting from these locations. The entry angle, the total length of the trajectory and distances to critical structures are presented in an interactive way that is integrated with standard electrode placement planning tools and advanced visualisation. We show that this improves the planning of intracranial implantation, with safer trajectories in less time. © 2014 Springer International Publishing Switzerland

Computer-Assisted Versus Manual Planning for Stereotactic Brain Biopsy: A Retrospective Comparative Pilot Study

BACKGROUND: Stereotactic brain biopsy is among the most common neurosurgical procedures. Planning an optimally safe surgical trajectory requires careful attention to a number of features including the following: (1) traversing the skull perpendicularly; (2) minimizing trajectory length; and (3) avoiding critical neurovascular structures. OBJECTIVE: To evaluate a platform, SurgiNav, for automated trajectory planning in stereotactic brain biopsy. METHODS: A prospectively maintained database was searched between February and August 2017 to identify all adult patients who underwent stereotactic brain biopsy and for whom postoperative imaging was available. In each case, the standard preoperative, T1-weighted, gadolinium-enhanced magnetic resonance imaging was used to generate a model of the cortex. A surgical trajectory was then generated using computer-assisted planning (CAP) , and metrics of the trajectory were compared to the trajectory of the previously implemented manual plan (MP). RESULTS: Fifteen consecutive patients were identified. Feasible trajectories were generated using CAP in all patients, and the mean angle determined using CAP was more perpendicular to the skull than using MP (10.0◦ vs 14.6◦ from orthogonal; P = .07), the mean trajectory length was shorter (38.5 vs 43.5 mm; P = .01), and the risk score was lower (0.27 vs 0.52; P = .03). CONCLUSION: CAP for stereotactic brain biopsy appears feasible and may be safer in selected cases

Automatic multi-trajectory planning solution for steerable catheters

The present work describes a novel approach to trajectory planning for minimally invasive surgery consisting of an algorithm able to provide the surgeon with multiple curvilinear paths to connect an entry area defined on the brain cortex to a specific target point in the brain. A criterion based on the minimum distance from the safety-critical brain struc- tures (blood vessels, thalamus and ventricles) is used to rank the obtained trajectories. The solution is integrated onto the EDEN2020∗ programmable bevel-tip needle, a multi-segment probe whose steering ability derives from the offset generated on its tip, and provides a level of tolerance with respect to tracking errors arising from catheter model inaccuracies. The case of study of the work consists of a typical Deep Brain Stimulation scenario where tests have been performed in order to compare the result obtained from standard rectilinear trajectory planning against this novel curvilinear solution using the clearance from obstacles as an index of performance of the estimated solutions

Automated multiple trajectory planning algorithm for the placement of stereo-electroencephalography (SEEG) electrodes in epilepsy treatment.

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

Retrospective evaluation and SEEG trajectory analysis for interactive multi-trajectory planner assistant

Purpose: Focal epilepsy is a neurological disease that can be surgically treated by removing area of the brain generating the seizures. The stereotactic electroencephalography (SEEG) procedure allows patient brain activity to be recorded in order to localize the onset of seizures through the placement of intracranial electrodes. The planning phase can be cumbersome and very time consuming, and no quantitative information is provided to neurosurgeons regarding the safety and efficacy of their trajectories. In this work, we present a novel architecture specifically designed to ease the SEEG trajectory planning using the 3D Slicer platform as a basis. Methods: Trajectories are automatically optimized following criteria like vessel distance and insertion angle. Multi-trajectory optimization and conflict resolution are optimized through a selective brute force approach based on a conflict graph construction. Additionally, electrode-specific optimization constraints can be defined, and an advanced verification module allows neurosurgeons to evaluate the feasibility of the trajectory. Results: A retrospective evaluation was performed using manually planned trajectories on 20 patients: the planning algorithm optimized and improved trajectories in 98% of cases. We were able to resolve and optimize the remaining 2% by applying electrode-specific constraints based on manual planning values. In addition, we found that the global parameters used discards 68% of the manual planned trajectories, even when they represent a safe clinical choice. Conclusions: Our approach improved manual planned trajectories in 98% of cases in terms of quantitative indexes, even when applying more conservative criteria with respect to actual clinical practice. The improved multi-trajectory strategy overcomes the previous work limitations and allows electrode optimization within a tolerable time span

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

Optimizing Trajectories for Cranial Laser Interstitial Thermal Therapy Using Computer-Assisted Planning: A Machine Learning Approach

Laser interstitial thermal therapy (LITT) is an alternative to open surgery for drug-resistant focal mesial temporal lobe epilepsy (MTLE). Studies suggest maximal ablation of the mesial hippocampal head and amygdalohippocampal complex (AHC) improves seizure freedom rates while better neuropsychological outcomes are associated with sparing of the parahippocampal gyrus (PHG). Optimal trajectories avoid sulci and CSF cavities and maximize distance from vasculature. Computer-assisted planning (CAP) improves these metrics, but the combination of entry and target zones has yet to be determined to maximize ablation of the AHC while sparing the PHG. We apply a machine learning approach to predict entry and target parameters and utilize these for CAP. Ten patients with hippocampal sclerosis were identified from a prospectively managed database. CAP LITT trajectories were generated using entry regions that include the inferior occipital, middle occipital, inferior temporal, and middle temporal gyri. Target points were varied by sequential AHC erosions and transformations of the centroid of the amygdala. A total of 7600 trajectories were generated, and ablation volumes of the AHC and PHG were calculated. Two machine learning approaches (random forest and linear regression) were investigated to predict composite ablation scores and determine entry and target point combinations that maximize ablation of the AHC while sparing the PHG. Random forest and linear regression predictions had a high correlation with the calculated values in the test set (ρ = 0.7) for both methods. Maximal composite ablation scores were associated with entry points around the junction of the inferior occipital, middle occipital, and middle temporal gyri. The optimal target point was the anteromesial amygdala. These parameters were then used with CAP to generate clinically feasible trajectories that optimize safety metrics. Machine learning techniques accurately predict composite ablation score. Prospective studies are required to determine if this improves seizure-free outcome while reducing neuropsychological morbidity following LITT for MTLE