Reduced risk trajectory planning in image-guided keyhole neurosurgery

Automatic Path-Planning Techniques for Minimally Invasive Stereotactic Neurosurgical Procedures-A Systematic Review

This review systematically examines the recent research from the past decade on diverse path-planning algorithms tailored for stereotactic neurosurgery applications. Our comprehensive investigation involved a thorough search of scholarly papers from Google Scholar, PubMed, IEEE Xplore, and Scopus, utilizing stringent inclusion and exclusion criteria. The screening and selection process was meticulously conducted by a multidisciplinary team comprising three medical students, robotic experts with specialized knowledge in path-planning techniques and medical robotics, and a board-certified neurosurgeon. Each selected paper was reviewed in detail, and the findings were synthesized and reported in this review. The paper is organized around three different types of intervention tools: straight needles, steerable needles, and concentric tube robots. We provide an in-depth analysis of various path-planning algorithms applicable to both single and multi-target scenarios. Multi-target planning techniques are only discussed for straight tools as there is no published work on multi-target planning for steerable needles and concentric tube robots. Additionally, we discuss the imaging modalities employed, the critical anatomical structures considered during path planning, and the current status of research regarding its translation to clinical human studies. To the best of our knowledge and as a conclusion from this systematic review, this is the first review paper published in the last decade that reports various path-planning techniques for different types of tools for minimally invasive neurosurgical applications. Furthermore, this review outlines future trends and identifies existing technology gaps within the field. By highlighting these aspects, we aim to provide a comprehensive overview that can guide future research and development in path planning for stereotactic neurosurgery, ultimately contributing to the advancement of safer and more effective neurosurgical procedures.

Optimal Concentric Tube Robot Design for Safe Intracerebral Hemorrhage Removal

Purpose

The purpose of this paper is to investigate the geometrical design and path planning of Concentric tube robots (CTR) for intracerebral hemorrhage (ICH) evacuation, with a focus on minimizing the risk of damaging white matter tracts and cerebral arteries.

Methods

To achieve our objective, we propose a parametrization method describing a general class of CTR geometric designs. We present mathematical models that describe the CTR design constraints and provide the calculation of a path risk value. We then use a genetic algorithm to determine the optimal tube geometry for targeting within the brain.

Results

Our results show that a multi-tube CTR design can significantly reduce the risk of damaging critical brain structures compared to the conventional straight tube design. However, there is no significant relationship between the path risk value and the number and shape of the additional inner curved tubes.

Conclusion

Considering the challenges of CTR hardware design, fabrication, and control, we conclude that the most practical geometry for a CTR path in ICH treatment is a straight outer tube followed by a planar curved inner tube. These findings have important implications for the development of safe and effective CTRs for ICH evacuation by enabling dexterous manipulation to minimize damage to critical brain structures.

Optimising trajectory planning for stereotactic brain tumour biopsy using artificial intelligence: a systematic review of the literature

PURPOSE

Despite advances in technology, stereotactic brain tumour biopsy remains challenging due to the risk of injury to critical structures. Indeed, choosing the correct trajectory remains essential to patient safety. Artificial intelligence can be used to perform automated trajectory planning. We present a systematic review of automated trajectory planning algorithms for stereotactic brain tumour biopsies.

METHODS

A PRISMA adherent systematic review was conducted. Databases were searched using keyword combinations of 'artificial intelligence', 'trajectory planning' and 'brain tumours'. Studies reporting applications of artificial intelligence (AI) to trajectory planning for brain tumour biopsy were included.

RESULTS

All eight studies were in the earliest stage of the IDEAL-D development framework. Trajectory plans were compared through a variety of surrogate markers of safety, of which the minimum distance to blood vessels was the most common. Five studies compared manual to automated planning strategies and favoured automation in all cases. However, this comes with a significant risk of bias.

CONCLUSIONS

This systematic review reveals the need for IDEAL-D Stage 1 research into automated trajectory planning for brain tumour biopsy. Future studies should establish the congruence between expected risk of algorithms and the ground truth through comparisons to real world outcomes.

Validation of a novel path planner for stereotactic neurosurgical interventions-A retrospective clinical study

BACKGROUND

The gold standard workflow for targeting structures in the brain involves manual path planning. This preoperative manual path planning is very time-intensive and laborious, especially when some outcome measures such as maximum ablation and penetration depth has to be optimised.

METHODS

Our novel path planner generates an optimal path which maximises the hippocampus penetration and distance from critical structures using a precomputed cost map and a reward map.

RESULTS

The average penetration ratio for 12 cases is 88.13 ± 23.23% for a resolution of 1° and a safety margin of 1 mm. Average run time for the path planner based on 1° resolution was 1.99 ± 0.68 min.

CONCLUSIONS

Results show that the algorithm can generate safe and clinically relevant paths with a quantitative representation of the penetration depth and is faster than the average reported time for manual path planning.

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.

Virtual Reality in Neurosurgery: Beyond Neurosurgical Planning

Background: While several publications have focused on the intuitive role of augmented reality (AR) and virtual reality (VR) in neurosurgical planning, the aim of this review was to explore other avenues, where these technologies have significant utility and applicability. Methods: This review was conducted by searching PubMed, PubMed Central, Google Scholar, the Scopus database, the Web of Science Core Collection database, and the SciELO citation index, from 1989–2021. An example of a search strategy used in PubMed Central is: “Virtual reality” [All Fields] AND (“neurosurgical procedures” [MeSH Terms] OR (“neurosurgical” [All Fields] AND “procedures” [All Fields]) OR “neurosurgical procedures” [All Fields] OR “neurosurgery” [All Fields] OR “neurosurgery” [MeSH Terms]). Using this search strategy, we identified 487 (PubMed), 1097 (PubMed Central), and 275 citations (Web of Science Core Collection database). Results: Articles were found and reviewed showing numerous applications of VR/AR in neurosurgery. These applications included their utility as a supplement and augment for neuronavigation in the fields of diagnosis for complex vascular interventions, spine deformity correction, resident training, procedural practice, pain management, and rehabilitation of neurosurgical patients. These technologies have also shown promise in other area of neurosurgery, such as consent taking, training of ancillary personnel, and improving patient comfort during procedures, as well as a tool for training neurosurgeons in other advancements in the field, such as robotic neurosurgery. Conclusions: We present the first review of the immense possibilities of VR in neurosurgery, beyond merely planning for surgical procedures. The importance of VR and AR, especially in “social distancing” in neurosurgery training, for economically disadvantaged sections, for prevention of medicolegal claims and in pain management and rehabilitation, is promising and warrants further research.

Multimodal Risk-Based Path Planning for Neurosurgical Interventions

Image-guided neurosurgical interventions are challenging due to the complex anatomy of the brain and the inherent risk of damaging vital structures. This paper presents a neurosurgical planning tool for safe and effective neurosurgical interventions, minimizing the risk through optimized access planning. The strengths of the proposed system are the integration of multiple risk structures combined into a holistic model for fast and intuitive user interaction, and a modular architecture. The tool is intended to support neurosurgeons to quickly determine the most appropriate surgical entry point and trajectory through the brain with minimized risk. The user interface guides a user through the decision-making process and may save planning time of neurosurgical interventions. The navigation tool has been interfaced to the Robot Operating System, which allows the integration into automated workflows and the planning of linear and nonlinear trajectories. Determined risk structures and trajectories can be visualized intuitively as a projection map on the skin or cortical surface. Two risk calculation modes (strict and joint) are offered to the neurosurgeons, depending on the intracranial procedure's type and complexity. A qualitative evaluation with clinical experts shows the practical relevance, while a quantitative performance and functionality analysis proves the robustness and effectiveness of the system.

Surgical planning assistance in keyhole and percutaneous surgery: A systematic review

Surgical planning of percutaneous interventions has a crucial role to guarantee the success of minimally invasive surgeries. In the last decades, many methods have been proposed to reduce clinician work load related to the planning phase and to augment the information used in the definition of the optimal trajectory. In this survey, we include 113 articles related to computer assisted planning (CAP) methods and validations obtained from a systematic search on three databases. First, a general formulation of the problem is presented, independently from the surgical field involved, and the key steps involved in the development of a CAP solution are detailed. Secondly, we categorized the articles based on the main surgical applications, which have been object of study and we categorize them based on the type of assistance provided to the end-user.

Evaluation of three automatic brain vessel segmentation methods for stereotactical trajectory planning

BACKGROUND AND OBJECTIVE

Stereotactical procedures require exact trajectory planning to avoid blood vessels in the trajectory path. Innovation in imaging and image recognition techniques have facilitated the automatic detection of blood vessels during the planning process and may improve patient safety in the future. To assess the feasibility of a vessel detection and warning system using currently available imaging and vessel segmentation techniques.

METHODS

Image data were acquired from post-contrast, isovolumetric T1-weighted sequences (T1CE) and time.-of-flight MR angiography at 3T or 7T from a total of nine subjects. Vessel segmentation by a combination of a vessel-enhancement filter with subsequent level-set segmentation was evaluated using three different methods (Vesselness, FastMarching and LevelSet) in 45 stereotactic trajectories. Segmentation results were compared to a gold-standard of manual segmentation performed jointly by two human experts.

RESULTS

The LevelSet method performed best with a mean interclass correlation coefficient (ICC) of 0.76 [0.73, 0.81] compared to the FastMarching method with ICC 0.70 [0.67, 0.73] respectively. The Vesselness algorithm achieved clearly inferior overall performance with a mean ICC of 0.56 [0.53, 0.59]. The differences in mean ICC between all segmentation methods were statistically significant (p < 0.001 with post-hoc p < 0.026). The LevelSet method performed likewise good in MPRAGE and 3T-TOF images and excellent in 7T-TOF image data. The negative predictive value (NPV) was very high (>97%) for all methods and modalities. Positive predictive values (PPV) were found in the overall range of 65-90% likewise depending on algorithm and modality. This pattern reflects the disposition of all segmentation methods - in case of misclassification - to produce preferentially false-positive than false-negative results. In a clinical setting, two to three potential collision warnings would be given per trajectory on average with a PPV of around 50%.

CONCLUSIONS

It is feasible to integrate a clinically meaningful vessel detection and collision warning system into stereotactical planning software. Both, T1CE and MRA sequences are suitable as image data for such an application.

Risk assessment methodology for trajectory planning in keyhole neurosurgery using genetic algorithms

BACKGROUND

Preoperative assessment to find the safest trajectory in keyhole neurosurgery can reduce post operative complications.

METHODS

We introduced a novel preoperative risk assessment semiautomated methodology based on the sum of N maximum risk values using a generic genetic algorithm for the safest trajectory search.

RESULTS

A set of candidates trajectories were found for two surgical procedures. The trajectories search is done using a risk map considering the proximity of voxels within risk structures in multiple points and a genetic algorithm to avoid an exhaustive search. The trajectories were validated by a group of neurosurgeons.

CONCLUSIONS

The trajectories obtained with the proposal method were shorter in 5% and have greater distance from the voxels within the blood vessels in 4.7%. The use of genetic algorithm (GA) speeds up the search for the safest trajectory, decreasing in 99.9% the time required for an exhaustive search.

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.

Automated Steerable Path Planning for Deep Brain Stimulation Safeguarding Fiber Tracts and Deep Gray Matter Nuclei

Deep Brain Stimulation (DBS) is a neurosurgical procedure consisting in the stereotactic implantation of stimulation electrodes to specific brain targets, such as deep gray matter nuclei. Current solutions to place the electrodes rely on rectilinear stereotactic trajectories (RTs) manually defined by surgeons, based on pre-operative images. An automatic path planner that accurately targets subthalamic nuclei (STN) and safeguards critical surrounding structures is still lacking. Also, robotically-driven curvilinear trajectories (CTs) computed on the basis of state-of-the-art neuroimaging would decrease DBS invasiveness, circumventing patient-specific obstacles. This work presents a new algorithm able to estimate a pool of DBS curvilinear trajectories for reaching a given deep target in the brain, in the context of the EU's Horizon EDEN2020 project. The prospect of automatically computing trajectory plans relying on sophisticated newly engineered steerable devices represents a breakthrough in the field of microsurgical robotics. By tailoring the paths according to single-patient anatomical constraints, as defined by advanced preoperative neuroimaging including diffusion MR tractography, this planner ensures a higher level of safety than the standard rectilinear approach. Ten healthy controls underwent Magnetic Resonance Imaging (MRI) on 3T scanner, including 3DT1-weighted sequences, 3Dhigh-resolution time-of-flight MR angiography (TOF-MRA) and high angular resolution diffusion MR sequences. A probabilistic q-ball residual-bootstrap MR tractography algorithm was used to reconstruct motor fibers, while the other deep gray matter nuclei surrounding STN and vessels were segmented on T1 and TOF-MRA images, respectively. These structures were labeled as obstacles. The reliability of the automated planner was evaluated; CTs were compared to RTs in terms of efficacy and safety. Targeting the anterior STN, CTs performed significantly better in maximizing the minimal distance from critical structures, by finding a tuned balance between all obstacles. Moreover, CTs resulted superior in reaching the center of mass (COM) of STN, as well as in optimizing the entry angle in STN and in the skull surface.

Multimodal connectivity based eloquence score computation and visualisation for computer-aided neurosurgical path planning

Non-invasive assessment of cognitive importance has been a major challenge for planning of neurosurgical procedures. In the past decade, in vivo brain imaging modalities have been considered for estimating the ‘eloquence’ of brain areas. In order to estimate the impact of damage caused by an access path towards a target region inside of the skull, multi-modal metrics are introduced in this paper. Accordingly, this estimated damage is obtained by combining multi-modal metrics. In other words, this damage is an aggregate of intervened grey matter volume and axonal fibre numbers, weighted by their importance within the assigned anatomical and functional networks. To validate these metrics, an exhaustive search algorithm is implemented for characterising the solution space and visually representing connectional cost associated with a path initiated from underlying points. In this presentation, brain networks are built from resting state functional magnetic resonance imaging (fMRI) and deterministic tractography. their results demonstrate that the proposed approach is capable of refining traditional heuristics, such as choosing the minimal distance from the lesion, by supplementing connectional importance of the resected tissue. This provides complementary information to help the surgeon in avoiding important functional hubs and their anatomical linkages; which are derived from neuroimaging modalities and incorporated to the related anatomical landmarks.

Anatomy-driven multiple trajectory planning (ADMTP) of intracranial electrodes for epilepsy surgery

Purpose

Epilepsy is potentially curable with resective surgery if the epileptogenic zone (EZ) can be identified. If non-invasive imaging is unable to elucidate the EZ, intracranial electrodes may be implanted to identify the EZ as well as map cortical function. In current clinical practice, each electrode trajectory is determined by time-consuming manual inspection of preoperative imaging to find a path that avoids blood vessels while traversing appropriate deep and superficial regions of interest (ROIs). We present anatomy-driven multiple trajectory planning (ADMTP) to find safe trajectories from a list of user-defined ROIs within minutes rather than the hours required for manual planning.

Methods

Electrode trajectories are automatically computed in three steps: (1) Target Point Selection to identify appropriate target points within each ROI; (2) Trajectory Risk Scoring to quantify the cumulative distance to critical structures (blood vessels) along each trajectory, defined as the skull entry point to target point. (3) Implantation Plan Computation: to determine a feasible combination of low-risk trajectories for all electrodes.

Results

ADMTP was evaluated on 20 patients (190 electrodes). ADMTP lowered the quantitative risk score in 83% of electrodes. Qualitative results show ADMTP found suitable trajectories for 70% of electrodes; a similar portion of manual trajectories were considered suitable. Trajectory suitability for ADMTP was 95% if traversing sulci was not included in the safety criteria. ADMTP is computationally efficient, computing between 7 and 12 trajectories in 54.5 (17.3–191.9) s.

Conclusions

ADMTP efficiently compute safe and surgically feasible electrode trajectories.

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.

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 significantly 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 profile 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 data sets from two image modalities available within the planning system were used for evaluation. The mean Dice similarity coefficient was \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.89\pm 0.04$$\end{document}0.89±0.04, representing a statistically significantly improvement when compared to a semi-automated single human rater, single-modality segmentation protocol used in clinical practice (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.80 \pm 0.03$$\end{document}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.

Training for planning tumour resection: augmented reality and human factors

Planning surgical interventions is a complex task, demanding a high degree of perceptual, cognitive, and sensorimotor skills to reduce intra- and post-operative complications. This process requires spatial reasoning to coordinate between the preoperatively acquired medical images and patient reference frames. In the case of neurosurgical interventions, traditional approaches to planning tend to focus on providing a means for visualizing medical images, but rarely support transformation between different spatial reference frames. Thus, surgeons often rely on their previous experience and intuition as their sole guide is to perform mental transformation. In case of junior residents, this may lead to longer operation times or increased chance of error under additional cognitive demands. In this paper, we introduce a mixed augmented-/virtual-reality system to facilitate training for planning a common neurosurgical procedure, brain tumour resection. The proposed system is designed and evaluated with human factors explicitly in mind, alleviating the difficulty of mental transformation. Our results indicate that, compared to conventional planning environments, the proposed system greatly improves the nonclinicians' performance, independent of the sensorimotor tasks performed ( ). Furthermore, the use of the proposed system by clinicians resulted in a significant reduction in time to perform clinically relevant tasks ( ). These results demonstrate the role of mixed-reality systems in assisting residents to develop necessary spatial reasoning skills needed for planning brain tumour resection, improving patient outcomes.

The role of automatic computer-aided surgical trajectory planning in improving the expected safety of stereotactic neurosurgery

PURPOSE

Minimal invasion computer-assisted neurosurgical procedures with various tool insertions into the brain may carry hemorrhagic risks and neurological deficits. The goal of this study is to investigate the role of computer-based surgical trajectory planning tools in improving the potential safety of image-based stereotactic neurosurgery.

METHODS

Multi-sequence MRI studies of eight patients who underwent image-guided neurosurgery were retrospectively processed to extract anatomical structures-head surface, ventricles, blood vessels, white matter fibers tractography, and fMRI data of motor, sensory, speech, and visual areas. An experienced neurosurgeon selected one target for each patient. Five neurosurgeons planned a surgical trajectory for each patient using three planning methods: (1) conventional; (2) visualization, in which scans are augmented with overlays of anatomical structures and functional areas; and (3) automatic, in which three surgical trajectories with the lowest expected risk score are automatically computed. For each surgeon, target, and method, we recorded the entry point and its surgical trajectory and computed its expected risk score and its minimum distance from the key structures.

RESULTS

A total of 120 surgical trajectories were collected (5 surgeons, 8 targets, 3 methods). The surgical trajectories expected risk scores improved by 76% ([Formula: see text], two-sample student's t test); the average distance of a trajectory from nearby blood vessels increased by 1.6 mm ([Formula: see text]) from 0.6 to 2.2 mm (243%). The initial surgical trajectories were changed in 85% of the cases based on the expected risk score and the trajectory distance from blood vessels.

CONCLUSIONS

Computer-based patient-specific preoperative planning of surgical trajectories that minimize the expected risk of vascular and neurological damage due to incorrect tool placement is a promising technique that yields consistent improvements.

Neuronavigation using susceptibility-weighted venography: application to deep brain stimulation and comparison with gadolinium contrast

Careful trajectory planning on preoperative vascular imaging is an essential step in deep brain stimulation (DBS) to minimize risks of hemorrhagic complications and postoperative neurological deficits. This paper compares 2 MRI methods for visualizing cerebral vasculature and planning DBS probe trajectories: a single data set T1-weighted scan with double-dose gadolinium contrast (T1w-Gd) and a multi–data set protocol consisting of a T1-weighted structural, susceptibility-weighted venography, and time-of-flight angiography (T1w-SWI-TOF). Two neurosurgeons who specialize in neuromodulation surgery planned bilateral STN DBS in 18 patients with Parkinson's disease (36 hemispheres) using each protocol separately. Planned trajectories were then evaluated across all vascular data sets (T1w-Gd, SWI, and TOF) to detect possible intersection with blood vessels along the entire path via an objective vesselness measure. The authors' results show that trajectories planned on T1w-SWI-TOF successfully avoided the cerebral vasculature imaged by conventional T1w-Gd and did not suffer from missing vascular information or imprecise data set registration. Furthermore, with appropriate planning and visualization software, trajectory corridors planned on T1w-SWI-TOF intersected significantly less fine vasculature that was not detected on the T1w-Gd (p < 0.01 within 2 mm and p < 0.001 within 4 mm of the track centerline). The proposed T1w-SWI-TOF protocol comes with minimal effects on the imaging and surgical workflow, improves vessel avoidance, and provides a safe cost-effective alternative to injection of gadolinium contrast.

Multisurgeon, multisite validation of a trajectory planning algorithm for deep brain stimulation procedures

Deep brain stimulation, which is used to treat various neurological disorders, involves implanting a permanent electrode into precise targets deep in the brain. Reaching these targets safely is difficult because surgeons have to plan trajectories that avoid critical structures and reach targets within specific angles. A number of systems have been proposed to assist surgeons in this task. These typically involve formulating constraints as cost terms, weighting them by surgical importance, and searching for optimal trajectories, in which constraints and their weights reflect local practice. Assessing the performance of such systems is challenging because of the lack of ground truth and clear consensus on an optimal approach among surgeons. Due to difficulties in coordinating inter-institution evaluation studies, these have been performed so far at the sites at which the systems are developed. Whether or not a scheme developed at one site can also be used at another is thus unknown. In this paper, we conduct a study that involves four surgeons at three institutions to determine whether or not constraints and their associated weights can be used across institutions. Through a series of experiments, we show that a single set of weights performs well for all surgeons in our group. Out of 60 trajectories, our trajectories were accepted by a majority of neurosurgeons in 95% of the cases and the average acceptance rate was 90%. This study suggests, albeit on a limited number of surgeons, that the same system can be used to provide assistance across multiple sites and surgeons.

Multi-trajectories automatic planner for StereoElectroEncephaloGraphy (SEEG)

PURPOSE

StereoElectroEncephaloGraphy (SEEG) is done to identify the epileptogenic zone of the brain using several multi-lead electrodes whose positions in the brain are pre-operatively defined. Intracranial hemorrhages due to disruption of blood vessels can cause major complications of this procedure ([Formula: see text]1%). In order to increase the intervention safety, we developed and tested planning tools to assist neurosurgeons in choosing the best trajectory configuration.

METHODS

An automated planning method was developed that maximizes the distance of the electrode from the vessels and avoids the sulci as entry points. The angle of the guiding screws is optimized to reduce positioning error. The planner was quantitatively and qualitatively compared with manually computed trajectories on 26 electrodes planned for three patients undergoing SEEG by four neurosurgeons. Quantitative comparison was performed computing for each trajectory using (a) the Euclidean distance from the closest vessel and (b) the incidence angle.

RESULTS

Quantitative evaluation shows that automatic planned trajectories are safer in terms of distance from the closest vessel with respect to manually planned trajectories. Qualitative evaluation performed by four neurosurgeons showed that the automatically computed trajectories would have been preferred to manually computed ones in 30% of the cases and were judged good or acceptable in about 86% of the cases. A significant reduction in time required for planning was observed with the automated system (approximately 1/10).

CONCLUSION

The automatic SEEG electrode planner satisfied the essential clinical requirements, by providing safe trajectories in an efficient timeframe.

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).

Automatic trajectory planner for StereoElectroEncephaloGraphy procedures: a retrospective study

In StereoElectroEncephaloGraphy (SEEG) procedures, intracerebral electrodes are implanted in order to identify the epileptogenic zone in drug-resistant epileptic patients. This paper presents an automatic multitrajectory planner that computes the best trajectory in terms of distance from vessels and guiding screws angle, once the candidate entry and target regions are quickly and roughly defined. The planning process is designed also to spare some brain structures, such as cella media and trigone of the lateral ventricles and brain stem. The planner was retrospectively evaluated on 15 patients who had previously undergone SEEG investigation. Quantitative comparison was performed computing for each patient and for each electrode trajectory 1) the Euclidean distance from the closest vessel; 2) the trajectory incidence angle (guiding screws angle); and 3) the sulcality value. The automatic planner proved to satisfy the clinical requirements, planning safe trajectories in a clinical-compatible timeframe. Qualitative evaluation performed by three neurosurgeons showed that the automatically computed trajectories would have been accepted by them.