IBIS: an OR ready open-source platform for image-guided neurosurgery

A novel portable augmented reality surgical navigation system for maxillofacial surgery: technique and accuracy study

Surgical navigation, despite its potential benefits, faces challenges in widespread adoption in clinical practice. Possible reasons include the high cost, increased surgery time, attention shifts during surgery, and the mental task of mapping from the monitor to the patient. To address these challenges, a portable, all-in-one surgical navigation system using augmented reality (AR) was developed, and its feasibility and accuracy were investigated. The system achieves AR visualization by capturing a live video stream of the actual surgical field using a visible light camera and merging it with preoperative virtual images. A skull model with reference spheres was used to evaluate the accuracy. After registration, virtual models were overlaid on the real skull model. The discrepancies between the centres of the real spheres and the virtual model were measured to assess the AR visualization accuracy. This AR surgical navigation system demonstrated precise AR visualization, with an overall overlap error of 0.53 ± 0.21 mm. By seamlessly integrating the preoperative virtual plan with the intraoperative field of view in a single view, this novel AR navigation system could provide a feasible solution for the use of AR visualization to guide the surgeon in performing the operation as planned.

NeuroIGN: Explainable Multimodal Image-Guided System for Precise Brain Tumor Surgery

Precise neurosurgical guidance is critical for successful brain surgeries and plays a vital role in all phases of image-guided neurosurgery (IGN). Neuronavigation software enables real-time tracking of surgical tools, ensuring their presentation with high precision in relation to a virtual patient model. Therefore, this work focuses on the development of a novel multimodal IGN system, leveraging deep learning and explainable AI to enhance brain tumor surgery outcomes. The study establishes the clinical and technical requirements of the system for brain tumor surgeries. NeuroIGN adopts a modular architecture, including brain tumor segmentation, patient registration, and explainable output prediction, and integrates open-source packages into an interactive neuronavigational display. The NeuroIGN system components underwent validation and evaluation in both laboratory and simulated operating room (OR) settings. Experimental results demonstrated its accuracy in tumor segmentation and the success of ExplainAI in increasing the trust of medical professionals in deep learning. The proposed system was successfully assembled and set up within 11 min in a pre-clinical OR setting with a tracking accuracy of 0.5 (± 0.1) mm. NeuroIGN was also evaluated as highly useful, with a high frame rate (19 FPS) and real-time ultrasound imaging capabilities. In conclusion, this paper describes not only the development of an open-source multimodal IGN system but also demonstrates the innovative application of deep learning and explainable AI algorithms in enhancing neuronavigation for brain tumor surgeries. By seamlessly integrating pre- and intra-operative patient image data with cutting-edge interventional devices, our experiments underscore the potential for deep learning models to improve the surgical treatment of brain tumors and long-term post-operative outcomes.

A Novel Registration Method for a Mixed Reality Navigation System Based on a Laser Crosshair Simulator: A Technical Note

Mixed Reality Navigation (MRN) is pivotal in augmented reality-assisted intelligent neurosurgical interventions. However, existing MRN registration methods face challenges in concurrently achieving low user dependency, high accuracy, and clinical applicability. This study proposes and evaluates a novel registration method based on a laser crosshair simulator, evaluating its feasibility and accuracy. A novel registration method employing a laser crosshair simulator was introduced, designed to replicate the scanner frame's position on the patient. The system autonomously calculates the transformation, mapping coordinates from the tracking space to the reference image space. A mathematical model and workflow for registration were designed, and a Universal Windows Platform (UWP) application was developed on HoloLens-2. Finally, a head phantom was used to measure the system's target registration error (TRE). The proposed method was successfully implemented, obviating the need for user interactions with virtual objects during the registration process. Regarding accuracy, the average deviation was 3.7 ± 1.7 mm. This method shows encouraging results in efficiency and intuitiveness and marks a valuable advancement in low-cost, easy-to-use MRN systems. The potential for enhancing accuracy and adaptability in intervention procedures positions this approach as promising for improving surgical outcomes.

Toward Estimating MRI-Ultrasound Registration Error in Image-Guided Neurosurgery

Image-guided neurosurgery allows surgeons to view their tools in relation to preoperatively acquired patient images and models. To continue using neuronavigation systems throughout operations, image registration between preoperative images [typically magnetic resonance imaging (MRI)] and intraoperative images (e.g., ultrasound) is common to account for brain shift (deformations of the brain during surgery). We implemented a method to estimate MRI-ultrasound registration errors, with the goal of enabling surgeons to quantitatively assess the performance of linear or nonlinear registrations. To the best of our knowledge, this is the first dense error estimating algorithm applied to multimodal image registrations. The algorithm is based on a previously proposed sliding-window convolutional neural network that operates on a voxelwise basis. To create training data where the true registration error is known, simulated ultrasound images were created from preoperative MRI images and artificially deformed. The model was evaluated on artificially deformed simulated ultrasound data and real ultrasound data with manually annotated landmark points. The model achieved a mean absolute error (MAE) of 0.977 ± 0.988 mm and a correlation of 0.8 ± 0.062 on the simulated ultrasound data, and an MAE of 2.24 ± 1.89 mm and a correlation of 0.246 on the real ultrasound data. We discuss concrete areas to improve the results on real ultrasound data. Our progress lays the foundation for future developments and ultimately implementation of clinical neuronavigation systems.

Advances in artificial intelligence, robotics, augmented and virtual reality in neurosurgery

Neurosurgical practitioners undergo extensive and prolonged training to acquire diverse technical proficiencies, while neurosurgical procedures necessitate a substantial amount of pre-, post-, and intraoperative clinical data acquisition, making decisions, attention, and convalescence. The past decade witnessed an appreciable escalation in the significance of artificial intelligence (AI) in neurosurgery. AI holds significant potential in neurosurgery as it supplements the abilities of neurosurgeons to offer optimal interventional and non-interventional care to patients by improving prognostic and diagnostic outcomes in clinical therapy and assisting neurosurgeons in making decisions while surgical interventions to enhance patient outcomes. Other technologies including augmented reality, robotics, and virtual reality can assist and promote neurosurgical methods as well. Moreover, they play a significant role in generating, processing, as well as storing experimental and clinical data. Also, the usage of these technologies in neurosurgery is able to curtail the number of costs linked with surgical care and extend high-quality health care to a wider populace. This narrative review aims to integrate the results of articles that elucidate the role of the aforementioned technologies in neurosurgery.

Regional variability in intracerebral properties of NREM to REM sleep transitions in humans

Transitions between wake and sleep states show a progressive pattern underpinned by local sleep regulation. In contrast, little evidence is available on non-rapid eye movement (NREM) to rapid eye movement (REM) sleep boundaries, considered as mainly reflecting subcortical regulation. Using polysomnography (PSG) combined with stereoelectroencephalography (SEEG) in humans undergoing epilepsy presurgical evaluation, we explored the dynamics of NREM-to-REM transitions. PSG was used to visually score transitions and identify REM sleep features. SEEG-based local transitions were determined automatically with a machine learning algorithm using features validated for automatic intra-cranial sleep scoring (10.5281/zenodo.7410501). We analyzed 2988 channel-transitions from 29 patients. The average transition time from all intracerebral channels to the first visually marked REM sleep epoch was 8 s ± 1 min 58 s, with a great heterogeneity between brain areas. Transitions were observed first in the lateral occipital cortex, preceding scalp transition by 1 min 57 s ± 2 min 14 s (d = -0.83), and close to the first sawtooth wave marker. Regions with late transitions were the inferior frontal and orbital gyri (1 min 1 s ± 2 min 1 s, d = 0.43, and 1 min 1 s ± 2 min 5 s, d = 0.43, after scalp transition). Intracranial transitions were earlier than scalp transitions as the night advanced (last sleep cycle, d = -0.81). We show a reproducible gradual pattern of REM sleep initiation, suggesting the involvement of cortical mechanisms of regulation. This provides clues for understanding oneiric experiences occurring at the NREM/REM boundary.

The HoloLens in medicine: A systematic review and taxonomy

The HoloLens (Microsoft Corp., Redmond, WA), a head-worn, optically see-through augmented reality (AR) display, is the main player in the recent boost in medical AR research. In this systematic review, we provide a comprehensive overview of the usage of the first-generation HoloLens within the medical domain, from its release in March 2016, until the year of 2021. We identified 217 relevant publications through a systematic search of the PubMed, Scopus, IEEE Xplore and SpringerLink databases. We propose a new taxonomy including use case, technical methodology for registration and tracking, data sources, visualization as well as validation and evaluation, and analyze the retrieved publications accordingly. We find that the bulk of research focuses on supporting physicians during interventions, where the HoloLens is promising for procedures usually performed without image guidance. However, the consensus is that accuracy and reliability are still too low to replace conventional guidance systems. Medical students are the second most common target group, where AR-enhanced medical simulators emerge as a promising technology. While concerns about human-computer interactions, usability and perception are frequently mentioned, hardly any concepts to overcome these issues have been proposed. Instead, registration and tracking lie at the core of most reviewed publications, nevertheless only few of them propose innovative concepts in this direction. Finally, we find that the validation of HoloLens applications suffers from a lack of standardized and rigorous evaluation protocols. We hope that this review can advance medical AR research by identifying gaps in the current literature, to pave the way for novel, innovative directions and translation into the medical routine.

Ultrasound-based navigated pedicle screw insertion without intraoperative radiation: feasibility study on porcine cadavers

BACKGROUND

Navigation systems for spinal fusion surgery rely on intraoperative computed tomography (CT) or fluoroscopy imaging. Both expose patient, surgeons and operating room staff to significant amounts of radiation. Alternative methods involving intraoperative ultrasound (iUS) imaging have recently shown promise for image-to-patient registration. Yet, the feasibility and safety of iUS navigation in spinal fusion have not been demonstrated.

PURPOSE

To evaluate the accuracy of pedicle screw insertion in lumbar and thoracolumbar spinal fusion using a fully automated iUS navigation system.

STUDY DESIGN

Prospective porcine cadaver study.

METHODS

Five porcine cadavers were used to instrument the lumbar and thoracolumbar spine using posterior open surgery. During the procedure, iUS images were acquired and used to establish automatic registration between the anatomy and preoperative CT images. Navigation was performed with the preoperative CT using tracked instruments. The accuracy of the system was measured as the distance of manually collected points to the preoperative CT vertebral surface and compared against fiducial-based registration. A postoperative CT was acquired, and screw placements were manually verified. We report breach rates, as well as axial and sagittal screw deviations.

RESULTS

A total of 56 screws were inserted (5.50 mm diameter n=50, and 6.50 mm diameter n=6). Fifty-two screws were inserted safely without breach. Four screws (7.14%) presented a medial breach with an average deviation of 1.35±0.37 mm (all <2 mm). Two breaches were caused by 6.50 mm diameter screws, and two by 5.50 mm screws. For vertebrae instrumented with 5.50 mm screws, the average axial diameter of the pedicle was 9.29 mm leaving a 1.89 mm margin in the left and right pedicle. For vertebrae instrumented with 6.50 mm screws, the average axial diameter of the pedicle was 8.99 mm leaving a 1.24 mm error margin in the left and right pedicle. The average distance to the vertebral surface was 0.96 mm using iUS registration and 0.97 mm using fiducial-based registration.

CONCLUSIONS

We successfully implanted all pedicle screws in the thoracolumbar spine using the ultrasound-based navigation system. All breaches recorded were minor (<2 mm) and the breach rate (7.14%) was comparable to existing literature. More investigation is needed to evaluate consistency, reproducibility, and performance in surgical context.

CLINICAL SIGNIFICANCE

Intraoperative US-based navigation is feasible and practical for pedicle screw insertion in a porcine model. It might be used as a low-cost and radiation-free alternative to intraoperative CT and fluoroscopy in the future.

Systematic Review: Applications of Intraoperative Ultrasound in Spinal Surgery

BACKGROUND

Due to increased practicality and decreased costs and radiation, interest has risen for intraoperative ultrasound (iUS) in spinal surgery applications; however, few studies have provided a robust overview of its use in spinal surgery. We synthesize findings of existing literature on usage of iUS in navigation, pedicle screw placement, and identification of anatomy during spinal interventions.

METHODS

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were utilized in this systematic review. Studies were identified through PubMed, Scopus, and Google Scholar databases using the search string. Abstracts mentioning iUS in spine applications were included. Upon full-text review, exclusion criteria were implemented, including outdated studies or those with weak topic relevance or statistical power. Upon elimination of duplicates, multi-reviewer screening for eligibility, and citation search, 44 manuscripts were analyzed.

RESULTS

Navigation using iUS is safe, effective, and economical. iUS registration accuracy and success is within clinically acceptable limits for image-guided navigation (Table 2). Pedicle screw instrumentation with iUS is precise with a favorable safety profile (Table 2). Anatomical landmarks are reliably identified with iUS, and surgeons are overwhelmingly successful in neural or vascular tissue identification with iUS modalities including standard B mode, doppler, and contrast-enhanced ultrasound (CE-US) (Table 3). iUS use in traumatic reduction of fractures properly identifies anatomical structures, intervertebral disc space, and vasculature (Table 3).

CONCLUSION

iUS eliminates radiation, decreases costs, and provides sufficient accuracy and reliability in identification of anatomical and neurovascular structures in various spinal surgery settings.

Trends in the Use of Augmented Reality, Virtual Reality, and Mixed Reality in Surgical Research: a Global Bibliometric and Visualized Analysis

There have been many major developments in the use of augmented reality (AR), virtual reality (VR), and mixed reality (MR) technologies in the context of global surgical research, yet few reports on the trends in this field have been published to date. This study was therefore designed to explore these worldwide trends in this clinically important field. Relevant studies published from 1 January 2009 through 13 October 2020 were retrieved from the Science Citation Index-Expanded (SCI-E) tool of the Web of Science database. Bibliometric techniques were then used to analyze the resultant data, with visual bibliographic coupling, co-authorship, co-citation, co-occurrence, and publication trend analyses subsequently being conducted with GraphPad Prism 8 and with the visualization of similarities (VOS) software tool. There is no patient and public involved. In total, 6221 relevant studies were incorporated into this analysis. At a high level, clear global annual increases in the number of publications in this field were observed. The USA made the greatest contributions to this field over the studied period, with the highest H-index value, the most citations, and the greatest total link strength for analyzed publications. The country with the highest number of average citations per publication was Scotland. The Surgical Endoscopy And Other Interventional Techniques journal contributed the greatest number of publications in this field. The University of London was the institution that produced the greatest volume of research in this field. Overall, studies could be broadly classified into five clusters: Neurological Research, Surgical Techniques, Technological Products, Rehabilitative Medicine, and Clinical Therapy. The trends detected in the present analysis suggest that the number of global publications pertaining to the use of AR, VR, and MR techniques in surgical research is likely to increase in the coming years. Particular attention should be paid to emerging trends in related fields including MR, extended reality, head-mounted displays, navigation, and holographic images.

Current status of augmented reality in cerebrovascular surgery: a systematic review

Augmented reality (AR) is an adjuvant tool in neuronavigation to improve spatial and anatomic understanding. The present review aims to describe the current status of intraoperative AR for the treatment of cerebrovascular pathology. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The following databases were searched: PubMed, Science Direct, Web of Science, and EMBASE up to December, 2020. The search strategy consisted of "augmented reality," "AR," "cerebrovascular," "navigation," "neurovascular," "neurosurgery," and "endovascular" in both AND and OR combinations. Studies included were original research articles with intraoperative application. The manuscripts were thoroughly examined for study design, outcomes, and results. Sixteen studies were identified describing the use of intraoperative AR in the treatment of cerebrovascular pathology. A total of 172 patients were treated for 190 cerebrovascular lesions using intraoperative AR. The most common treated pathology was intracranial aneurysms. Most studies were cases and there was only a case-control study. A head-up display system in the microscope was the most common AR display. AR was found to be useful for tailoring the craniotomy, dura opening, and proper identification of donor and recipient vessels in vascular bypass. Most AR systems were unable to account for tissue deformation. This systematic review suggests that intraoperative AR is becoming a promising and feasible adjunct in the treatment of cerebrovascular pathology. It has been found to be a useful tool in the preoperative planning and intraoperative guidance. However, its clinical benefits remain to be seen.

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.

From Patient to Musician: A Multi-Sensory Virtual Reality Rehabilitation Tool for Spatial Neglect

Unilateral Spatial Neglect (USN) commonly results from a stroke or acquired brain injury. USN affects multiple modalities and results in failure to respond to stimuli on the contralesional side of space. Although USN is a heterogeneous syndrome, present-day therapy methods often fail to consider multiple modalities. Musical Neglect Therapy (MNT) is a therapy method that succeeds in incorporating multiple modalities by asking patients to make music. This research aimed to exploit the immersive and modifiable aspect of VR to translate MNT to a VR therapy tool. The tool was evaluated in a 2-week pilot study with four clinical users. These results are compared to a control group of four non-clinical users. Results indicated that patients responded to triggers in their entire environment and performance results could be clearly differentiated between clinical and non-clinical users. Moreover, patients increasingly corrected their head direction towards their neglected side. Patients stated that the use of VR increased their enjoyment of the therapy. This study contributes to the current research on rehabilitation for USN by proposing the first system to apply MNT in a VR environment. The tool shows promise as an addition to currently used rehabilitation methods. However, results are limited to a small sample size and performance metrics. Future work will focus on validating these results with a larger sample over a longer period. Moreover, future efforts should explore personalisation and gamification to tailor to the heterogeneity of the condition.

Studierfenster: an Open Science Cloud-Based Medical Imaging Analysis Platform

Imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) are widely used in diagnostics, clinical studies, and treatment planning. Automatic algorithms for image analysis have thus become an invaluable tool in medicine. Examples of this are two- and three-dimensional visualizations, image segmentation, and the registration of all anatomical structure and pathology types. In this context, we introduce Studierfenster (www.studierfenster.at): a free, non-commercial open science client-server framework for (bio-)medical image analysis. Studierfenster offers a wide range of capabilities, including the visualization of medical data (CT, MRI, etc.) in two-dimensional (2D) and three-dimensional (3D) space in common web browsers, such as Google Chrome, Mozilla Firefox, Safari, or Microsoft Edge. Other functionalities are the calculation of medical metrics (dice score and Hausdorff distance), manual slice-by-slice outlining of structures in medical images, manual placing of (anatomical) landmarks in medical imaging data, visualization of medical data in virtual reality (VR), and a facial reconstruction and registration of medical data for augmented reality (AR). More sophisticated features include the automatic cranial implant design with a convolutional neural network (CNN), the inpainting of aortic dissections with a generative adversarial network, and a CNN for automatic aortic landmark detection in CT angiography images. A user study with medical and non-medical experts in medical image analysis was performed, to evaluate the usability and the manual functionalities of Studierfenster. When participants were asked about their overall impression of Studierfenster in an ISO standard (ISO-Norm) questionnaire, a mean of 6.3 out of 7.0 possible points were achieved. The evaluation also provided insights into the results achievable with Studierfenster in practice, by comparing these with two ground truth segmentations performed by a physician of the Medical University of Graz in Austria. In this contribution, we presented an online environment for (bio-)medical image analysis. In doing so, we established a client-server-based architecture, which is able to process medical data, especially 3D volumes. Our online environment is not limited to medical applications for humans. Rather, its underlying concept could be interesting for researchers from other fields, in applying the already existing functionalities or future additional implementations of further image processing applications. An example could be the processing of medical acquisitions like CT or MRI from animals [Clinical Pharmacology & Therapeutics, 84(4):448–456, 68], which get more and more common, as veterinary clinics and centers get more and more equipped with such imaging devices. Furthermore, applications in entirely non-medical research in which images/volumes need to be processed are also thinkable, such as those in optical measuring techniques, astronomy, or archaeology.

Spatio-temporal spike dynamics predict surgical outcome in adult focal epilepsy

OBJECTIVE

We hypothesized that spatio-temporal dynamics of interictal spikes reflect the extent and stability of epileptic sources and determine surgical outcome.

METHODS

We studied 30 consecutive patients (14 good outcome). Spikes were detected in prolonged stereo-electroencephalography recordings. We quantified the spatio-temporal dynamics of spikes using the variance of the spike rate, line length and skewness of the spike distribution, and related these features to outcome. We built a logistic regression model, and compared its performance to traditional markers.

RESULTS

Good outcome patients had more dominant and stable sources than poor outcome patients as expressed by a higher variance of spike rates, a lower variance of line length, and a lower variance of positive skewness (ps < 0.05). The outcome was correctly predicted in 80% of patients. This was better or non-inferior to predictions based on a focal lesion (p = 0.016), focal seizure-onset zone, or complete resection (ps > 0.05). In the five patients where traditional markers failed, spike distribution predicted the outcome correctly. The best results were achieved by 18-h periods or longer.

CONCLUSIONS

Analysis of spike dynamics shows that surgery outcome depends on strong, single and stable sources.

SIGNIFICANCE

Our quantitative method has the potential to be a reliable predictor of surgical outcome.

Augmented Reality in Spinal Surgery: Highlights From Augmented Reality Lectures at the Emerging Technologies Annual Meetings

Introduction

Augmented reality (AR) is an advanced technology and emerging field that has been adopted into spine surgery to enhance care and outcomes. AR superimposes a three-dimensional computer-generated image over the normal anatomy of interest in order to facilitate visualization of deep structures without the ability to directly see them.

Objective

To summarize the latest literature and highlight AR from the annual “Spinal Navigation, Emerging Technologies and Systems Integration” meeting lectures presented by the Seattle Science Foundation (SSF) on the development and use of augmented reality in spinal surgery. 

Methods

 We performed a comprehensive literature review from 2016 to 2020 on PubMed to correlate with lectures given at the annual “Emerging Technologies” conferences. After the exclusion of papers that concerned non-spine surgery specialties, a total of 54 papers concerning AR in spinal applications were found. The articles were then categorized by content and focus.

Results

The 54 papers were divided into six major focused topics: training, proof of concept, feasibility and usability, clinical evaluation, state of technology, and nonsurgical applications. The greatest number of papers were published during 2020. Each paper discussed varied topics such as patient rehabilitation, proof of concept, workflow, applications in neurological and orthopedic spine surgery, and outcomes data.

Conclusions

The recent literature and SSF lectures on AR provide a solid base and demonstrate the emergence of an advanced technology that offers a platform for an advantageous technique that is superior, in that it allows the operating surgeon to focus directly on the patient rather than a guidance screen.

Examining the benefits of extended reality in neurosurgery: A systematic review

While well-established in other surgical subspecialties, the benefits of extended reality, consisting of virtual reality (VR), augmented reality (AR), and mixed reality (MR) technologies, remains underexplored in neurosurgery despite its increasing utilization. To address this gap, we conducted a systematic review of the effects of extended reality (XR) in neurosurgery with an emphasis on the perioperative period, to provide a guide for future clinical optimization. Seven primary electronic databases were screened following guidelines outlined by PRISMA and the Institute of Medicine. Reported data related to outcomes in the perioperative period and resident training were all examined, and a focused analysis of studies reporting controlled, clinical outcomes was completed. After removal of duplicates, 2548 studies were screened with 116 studies reporting measurable effects of XR in neurosurgery. The majority (82%) included cranial based applications related to tumor surgery with 34% showing improved resection rates and functional outcomes. A rise in high-quality studies was identified from 2017 to 2020 compared to all previous years (p = 0.004). Primary users of the technology were: 56% neurosurgeon (n = 65), 28% residents (n = 33) and 5% patients (n = 6). A final synthesis was conducted on 10 controlled studies reporting patient outcomes. XR technologies have demonstrated benefits in preoperative planning and multimodal neuronavigation especially for tumor surgery. However, few studies have reported patient outcomes in a controlled design demonstrating a need for higher quality data. XR platforms offer several advantages to improve patient outcomes and specifically, the patient experience for neurosurgery.

Spine and Individual Vertebrae Segmentation in Computed Tomography Images Using Geometric Flows and Shape Priors

The surgical treatment of injuries to the spine often requires the placement of pedicle screws. To prevent damage to nearby blood vessels and nerves, the individual vertebrae and their surrounding tissue must be precisely localized. To aid surgical planning in this context we present a clinically applicable geometric flow based method to segment the human spinal column from computed tomography (CT) scans. We first apply anisotropic diffusion and flux computation to mitigate the effects of region inhomogeneities and partial volume effects at vertebral boundaries in such data. The first pipeline of our segmentation approach uses a region-based geometric flow, requires only a single manually identified seed point to initiate, and runs efficiently on a multi-core central processing unit (CPU). A shape-prior formulation is employed in a separate second pipeline to segment individual vertebrae, using both region and boundary based terms to augment the initial segmentation. We validate our method on four different clinical databases, each of which has a distinct intensity distribution. Our approach obviates the need for manual segmentation, significantly reduces inter- and intra-observer differences, runs in times compatible with use in a clinical workflow, achieves Dice scores that are comparable to the state of the art, and yields precise vertebral surfaces that are well within the acceptable 2 mm mark for surgical interventions.

SYLVIUS: A multimodal and multidisciplinary platform for epilepsy surgery

BACKGROUND AND OBJECTIVE

We present SYLVIUS, a software platform intended to facilitate and improve the complex workflow required to diagnose and surgically treat drug-resistant epilepsies. In complex epilepsies, additional invasive information from exploration with stereoencephalography (SEEG) with deep electrodes may be needed, for which the input from different diagnostic methods and clinicians from several specialties is required to ensure diagnostic efficacy and surgical safety. We aim to provide a software platform with optimal data flow among the different stages of epilepsy surgery to provide smooth and integrated decision making.

METHODS

The SYLVIUS platform provides a clinical workflow designed to ensure seamless and safe patient data sharing across specialities. It integrates tools for stereo visualization, data registration, transfer of electrode plans referred to distinct datasets, automated postoperative contact segmentation, and novel DWI tractography analysis. Nineteen cases were retrospectively evaluated to track modifications from an initial plan to obtain a final surgical plan, using SYLVIUS.

RESULTS

The software was used to modify trajectories in all 19 consulted cases, which were then imported into the robotic system for the surgical intervention. When available, SYLVIUS provided extra multimodal information, which resulted in a greater number of trajectory modifications.

CONCLUSIONS

The architecture presented in this paper streamlines epilepsy surgery allowing clinicians to have a digital clinical tool that allows recording of the different stages of the procedure, in a common multimodal 2D/3D setting for participation of different clinicians in defining and validating surgical plans for SEEG cases.

Minimally invasive supratentorial neurosurgical approaches guided by Smartphone app and compass

The precise location in the scalp of specifically planned points can help to achieve less invasive approaches. This study aims to develop a smartphone app, evaluate the precision and accuracy of the developed tool, and describe a series of cases using the referred technique. The application was developed with the React Native framework for Android and iOS. A phantom was printed based on the patient's CT scan, which was used for the calculation of accuracy and precision of the method. The points of interest were marked with an "x" on the patient's head, with the aid of the app and a compass attached to a skin marker pen. Then, two experienced neurosurgeons checked the plausibility of the demarcations based on the anatomical references. Both evaluators marked the frontal, temporal and parietal targets with a difference of less than 5 mm from the corresponding intended point, in all cases. The overall average accuracy observed was 1.6 ± 1.0 mm. The app was used in the surgical planning of trepanations for ventriculoperitoneal (VP) shunts and for drainage of abscesses, and in the definition of craniotomies for meningiomas, gliomas, brain metastases, intracranial hematomas, cavernomas, and arteriovenous malformation. The sample consisted of 88 volunteers who exhibited the following pathologies: 41 (46.6%) had brain tumors, 17 (19.3%) had traumatic brain injuries, 16 (18.2%) had spontaneous intracerebral hemorrhages, 2 (2.3%) had cavernomas, 1 (1.1%) had arteriovenous malformation (AVM), 4 (4.5%) had brain abscesses, and 7 (7.9%) had a VP shunt placement. In cases approached by craniotomy, with the exception of AVM, straight incisions and minicraniotomy were performed. Surgical planning with the aid of the NeuroKeypoint app is feasible and reliable. It has enabled neurological surgeries by craniotomy and trepanation in an accurate, precise, and less invasive manner.

Evaluation of an Ultrasound-Based Navigation System for Spine Neurosurgery: A Porcine Cadaver Study

BACKGROUND

With the growing incidence of patients receiving surgical treatment for spinal metastatic tumours, there is a need for developing cost-efficient and radiation-free alternatives for spinal interventions. In this paper, we evaluate the capabilities and limitations of an image-guided neurosurgery (IGNS) system that uses intraoperative ultrasound (iUS) imaging for guidance.

METHODS

Using a lumbosacral section of a porcine cadaver, we explored the impact of CT image resolution, ultrasound depth and ultrasound frequency on system accuracy, robustness and effectiveness. Preoperative CT images with an isotropic resolution of , and were acquired. During surgery, vertebrae L1 to L6 were exposed. For each vertebra, five iUS scans were acquired using two depth parameters (5 cm and 7 cm) and two frequencies (6 MHz and 12 MHz). A total of 120 acquisition trials were evaluated. Ultrasound-based registration performance is compared to the standard alignment procedure using intraoperative CT. We report target registration error (TRE) and computation time. In addition, the scans' trajectories were analyzed to identify vertebral regions that provide the most relevant features for the alignment.

RESULTS

For all acquisitions, the median TRE ranged from 1.42 mm to 1.58 mm and the overall computation time was 9.04 s ± 1.58 s. Fourteen out of 120 iUS acquisitions (11.66%) yielded a level-to-level mismatch (and these are included in the accuracy measurements reported). No significant effect on accuracy was found with CT resolution (F (2,10) = 1.70, p = 0.232), depth (F (1,5) = 0.22, p= 0.659) nor frequency (F (1,5) = 1.02, p = 0.359). While misalignment increases linearly with the distance from the imaged vertebra, accuracy was satisfactory for directly adjacent levels. A significant relationship was found between iUS scan coverage of laminae and articular processes, and accuracy.

CONCLUSION

Intraoperative ultrasound can be used for spine surgery neuronavigation. We demonstrated that the IGNS system yield acceptable accuracy and high efficiency compared to the standard CT-based navigation procedure. The flexibility of the iUS acquisitions can have repercussions on the system performance, which are not fully identified. Further investigation is needed to understand the relationship between iUS acquisition and alignment performance.

Cerebral Anatomy Detection and Surgical Planning in Patients with Anterior Skull Base Meningiomas Using a Virtual Reality Technique

Anterior skull base meningiomas represent a wide cohort of tumors with different locations, extensions, configurations, and anatomical relationships. Diagnosis of these tumors and review of their therapies are inseparably connected with cranial imaging. We analyzed the influence of three-dimensional-virtual reality (3D-VR) reconstructions versus conventional computed tomography (CT) and magnetic resonance imaging (MRI) images (two-dimensional (2D) and screen 3D) on the identification of anatomical structures and on the surgical planning in patients with anterior skull base meningiomas. Medical files were retrospectively analyzed regarding patient- and disease-related data. Preoperative 2D-CT and 2D-MRI scans were retrospectively reconstructed to 3D-VR images and visualized via VR software to detect the characteristics of tumors. A questionnaire of experienced neurosurgeons evaluated the influence of the VR visualization technique on identification of tumor morphology and relevant anatomy and on surgical strategy. Thirty patients were included and 600 answer sheets were evaluated. The 3D-VR modality significantly influenced the detection of tumor-related anatomical structures (p = 0.002), recommended head positioning (p = 0.005), and surgical approach (p = 0.03). Therefore, the reconstruction of conventional preoperative 2D scans into 3D images and the spatial and anatomical presentation in VR models enabled greater understanding of anatomy and pathology, and thus influenced operation planning and strategy.

Brain Shift in Neuronavigation of Brain Tumors: An Updated Review of Intra-Operative Ultrasound Applications

Neuronavigation using pre-operative imaging data for neurosurgical guidance is a ubiquitous tool for the planning and resection of oncologic brain disease. These systems are rendered unreliable when brain shift invalidates the patient-image registration. Our previous review in 2015, Brain shift in neuronavigation of brain tumours: A review offered a new taxonomy, classification system, and a historical perspective on the causes, measurement, and pre- and intra-operative compensation of this phenomenon. Here we present an updated review using the same taxonomy and framework, focused on the developments of intra-operative ultrasound-based brain shift research from 2015 to the present (2020). The review was performed using PubMed to identify articles since 2015 with the specific words and phrases: “Brain shift” AND “Ultrasound”. Since 2015, the rate of publication of intra-operative ultrasound based articles in the context of brain shift has increased from 2–3 per year to 8–10 per year. This efficient and low-cost technology and increasing comfort among clinicians and researchers have allowed unique avenues of development. Since 2015, there has been a trend towards more mathematical advancements in the field which is often validated on publicly available datasets from early intra-operative ultrasound research, and may not give a just representation to the intra-operative imaging landscape in modern image-guided neurosurgery. Focus on vessel-based registration and virtual and augmented reality paradigms have seen traction, offering new perspectives to overcome some of the different pitfalls of ultrasound based technologies. Unfortunately, clinical adaptation and evaluation has not seen as significant of a publication boost. Brain shift continues to be a highly prevalent pitfall in maintaining accuracy throughout oncologic neurosurgical intervention and continues to be an area of active research. Intra-operative ultrasound continues to show promise as an effective, efficient, and low-cost solution for intra-operative accuracy management. A major drawback of the current research landscape is that mathematical tool validation based on retrospective data outpaces prospective clinical evaluations decreasing the strength of the evidence. The need for newer and more publicly available clinical datasets will be instrumental in more reliable validation of these methods that reflect the modern intra-operative imaging in these procedures.

Retrospective Comparison of Minimally Invasive and Open Monosegmental Lumbar Fusion, and Impact of Virtual Reality on Surgical Planning and Strategy

BACKGROUND AND STUDY AIMS

 Spinal fusion for symptomatic lumbar spondylolisthesis can be accomplished using an open or minimally invasive surgical (MIS) technique. Evaluation of segmental spondylolisthesis and instabilities and review of their therapies are inseparably connected with lumbar tomographic imaging. We analyzed a cohort of patients who underwent MIS or open monosegmental dorsal fusion and compared surgical outcomes along with complication rates. We furthermore evaluated the influence of virtual reality (VR) visualization on surgical planning in lumbar fusion.

MATERIAL AND METHODS

 Patient files were retrospectively analyzed regarding patient- and disease-related data, operative performance, surgical outcomes, and perioperative surgical complications. Preoperative computed tomography (CT) and magnetic resonance imaging (MRI) scans were retrospectively visualized via VR software. A questionnaire evaluated the influence of three-dimensional (3D) VR images versus two-dimensional CT and MRI scans on therapy planning, fusion method, and surgical technique and procedure.

RESULTS

 Overall, 171 patients were included (MIS/open: 90/81). MIS was associated with less blood loss, shorter surgery time and hospital stay, lower complication rates, equivalent long-term patient-reported outcomes, but lower fusion rates and higher late reoperation rates than open surgery. Image presentation using VR significantly influenced the recommended surgical therapies (decompression only/decompression and fusion; p = 0.02), had no significant influence on the recommended fusion method (rigid/dynamic/stand-alone; p = 0.77), and, in cases of rigid fusion, a significant influence on the recommended technique (MIS/open; p = 0.03) and fusion procedure (p = 0.02).

CONCLUSION

 In patients with monosegmental degenerative or isthmic spondylolisthesis, MIS fusion was advantageous concerning perioperative complication rates and perioperative surgical outcomes, but disadvantageous regarding fusion and reoperation rates compared to open fusion. 3D-VR-based analysis of sectional images significantly influenced the recommended surgical planning.

The Essential Role of Open Data and Software for the Future of Ultrasound-Based Neuronavigation

With the recent developments in machine learning and modern graphics processing units (GPUs), there is a marked shift in the way intra-operative ultrasound (iUS) images can be processed and presented during surgery. Real-time processing of images to highlight important anatomical structures combined with in-situ display, has the potential to greatly facilitate the acquisition and interpretation of iUS images when guiding an operation. In order to take full advantage of the recent advances in machine learning, large amounts of high-quality annotated training data are necessary to develop and validate the algorithms. To ensure efficient collection of a sufficient number of patient images and external validity of the models, training data should be collected at several centers by different neurosurgeons, and stored in a standard format directly compatible with the most commonly used machine learning toolkits and libraries. In this paper, we argue that such effort to collect and organize large-scale multi-center datasets should be based on common open source software and databases. We first describe the development of existing open-source ultrasound based neuronavigation systems and how these systems have contributed to enhanced neurosurgical guidance over the last 15 years. We review the impact of the large number of projects worldwide that have benefited from the publicly available datasets “Brain Images of Tumors for Evaluation” (BITE) and “Retrospective evaluation of Cerebral Tumors” (RESECT) that include MR and US data from brain tumor cases. We also describe the need for continuous data collection and how this effort can be organized through the use of a well-adapted and user-friendly open-source software platform that integrates both continually improved guidance and automated data collection functionalities.

Comprehensive review of surgical microscopes: technology development and medical applications

SIGNIFICANCE

Surgical microscopes provide adjustable magnification, bright illumination, and clear visualization of the surgical field and have been increasingly used in operating rooms. State-of-the-art surgical microscopes are integrated with various imaging modalities, such as optical coherence tomography (OCT), fluorescence imaging, and augmented reality (AR) for image-guided surgery.

AIM

This comprehensive review is based on the literature of over 500 papers that cover the technology development and applications of surgical microscopy over the past century. The aim of this review is threefold: (i) providing a comprehensive technical overview of surgical microscopes, (ii) providing critical references for microscope selection and system development, and (iii) providing an overview of various medical applications.

APPROACH

More than 500 references were collected and reviewed. A timeline of important milestones during the evolution of surgical microscope is provided in this study. An in-depth technical overview of the optical system, mechanical system, illumination, visualization, and integration with advanced imaging modalities is provided. Various medical applications of surgical microscopes in neurosurgery and spine surgery, ophthalmic surgery, ear-nose-throat (ENT) surgery, endodontics, and plastic and reconstructive surgery are described.

RESULTS

Surgical microscopy has been significantly advanced in the technical aspects of high-end optics, bright and shadow-free illumination, stable and flexible mechanical design, and versatile visualization. New imaging modalities, such as hyperspectral imaging, OCT, fluorescence imaging, photoacoustic microscopy, and laser speckle contrast imaging, are being integrated with surgical microscopes. Advanced visualization and AR are being added to surgical microscopes as new features that are changing clinical practices in the operating room.

CONCLUSIONS

The combination of new imaging technologies and surgical microscopy will enable surgeons to perform challenging procedures and improve surgical outcomes. With advanced visualization and improved ergonomics, the surgical microscope has become a powerful tool in neurosurgery, spinal, ENT, ophthalmic, plastic and reconstructive surgeries.

Impact of Virtual Reality in Arterial Anatomy Detection and Surgical Planning in Patients with Unruptured Anterior Communicating Artery Aneurysms

Anterior-communicating artery (ACoA) aneurysms have diverse configurations and anatomical variations. The evaluation and operative treatment of these aneurysms necessitates a perfect surgical strategy based on review of three-dimensional (3D) angioarchitecture using several radiologic imaging methods. We analyzed the influence of 3D virtual reality (VR) reconstructions versus conventional computed tomography angiography (CTA) scans on the identification of vascular anatomy and on surgical planning in patients with unruptured ACoA aneurysms. Medical files were retrospectively analyzed regarding patient- and disease-related data. Preoperative CTA scans were retrospectively reconstructed to 3D-VR images and visualized via VR software to detect the characteristics of unruptured ACoA aneurysms. A questionnaire was used to evaluate the influence of VR on the identification of aneurysm morphology and relevant arterial anatomy and on surgical strategy. Twenty-six patients were included and 520 answer sheets were evaluated. The 3D-VR modality significantly influenced detection of the aneurysm-related vascular structure (p = 0.0001), the recommended head positioning (p = 0.005), and the surgical approach (p = 0.001) in the planning of microsurgical clipping. Thus, reconstruction of conventional preoperative CTA scans into 3D images and the spatial presentation in VR models enabled greater understanding of the anatomy and pathology, provided realistic haptic feedback for aneurysm surgery, and influenced operation planning and strategy.

Open-source software for ultrasound-based guidance in spinal fusion surgery

Spinal instrumentation and surgical manipulations may cause loss of navigation accuracy requiring an efficient re-alignment of the patient anatomy with pre-operative images during surgery. While intra-operative ultrasound (iUS) guidance has shown clear potential to reduce surgery time, compared with clinical computed tomography (CT) guidance, rapid registration aiming to correct for patient misalignment has not been addressed. In this article, we present an open-source platform for pedicle screw navigation using iUS imaging. The alignment method is based on rigid registration of CT to iUS vertebral images and has been designed for fast and fully automatic patient re-alignment in the operating room. Two steps are involved: first, we use the iUS probe's trajectory to achieve an initial coarse registration; then, the registration transform is refined by simultaneously optimizing gradient orientation alignment and mean of iUS intensities passing through the CT-defined posterior surface of the vertebra. We evaluated our approach on a lumbosacral section of a porcine cadaver with seven vertebral levels. We achieved a median target registration error of 1.47 mm (100% success rate, defined by a target registration error <2 mm) when applying the probe's trajectory initial alignment. The approach exhibited high robustness to partial visibility of the vertebra with success rates of 89.86% and 88.57% when missing either the left or right part of the vertebra and robustness to initial misalignments with a success rate of 83.14% for random starts within ±20° rotation and ±20 mm translation. Our graphics processing unit implementation achieves an efficient registration time under 8 s, which makes the approach suitable for clinical application.

Rapid Eye Movement Sleep Sawtooth Waves Are Associated with Widespread Cortical Activations

Sawtooth waves (STW) are bursts of frontocentral slow oscillations recorded in the scalp electroencephalogram (EEG) during rapid eye movement (REM) sleep. Little is known about their cortical generators and functional significance. Stereo-EEG performed for presurgical epilepsy evaluation offers the unique possibility to study neurophysiology in situ in the human brain. We investigated intracranial correlates of scalp-detected STW in 26 patients (14 women) undergoing combined stereo-EEG/polysomnography. We visually marked STW segments in scalp EEG and selected stereo-EEG channels exhibiting normal activity for intracranial analyses. Channels were grouped in 30 brain regions. The spectral power in each channel and frequency band was computed during STW and non-STW control segments. Ripples (80-250 Hz) were automatically detected during STW and control segments. The spectral power in the different frequency bands and the ripple rates were then compared between STW and control segments in each brain region. An increase in 2-4 Hz power during STW segments was found in all brain regions, except the occipital lobe, with large effect sizes in the parietotemporal junction, the lateral and orbital frontal cortex, the anterior insula, and mesiotemporal structures. A widespread increase in high-frequency activity, including ripples, was observed concomitantly, involving the sensorimotor cortex, associative areas, and limbic structures. This distribution showed a high spatiotemporal heterogeneity. Our results suggest that STW are associated with widely distributed, but locally regulated REM sleep slow oscillations. By driving fast activities, STW may orchestrate synchronized reactivations of multifocal activities, allowing tagging of complex representations necessary for REM sleep-dependent memory consolidation.SIGNIFICANCE STATEMENT Sawtooth waves (STW) present as scalp electroencephalographic (EEG) bursts of slow waves contrasting with the low-voltage fast desynchronized activity of REM sleep. Little is known about their cortical origin and function. Using combined stereo-EEG/polysomnography possible only in the human brain during presurgical epilepsy evaluation, we explored the intracranial correlates of STW. We found that a large set of regions in the parietal, frontal, and insular cortices shows increases in 2-4 Hz power during scalp EEG STW, that STW are associated with a strong and widespread increase in high frequencies, and that these slow and fast activities exhibit a high spatiotemporal heterogeneity. These electrophysiological properties suggest that STW may be involved in cognitive processes during REM sleep.

Augmented reality in neurosurgical navigation: a survey

BACKGROUND

Neurosurgery has exceptionally high requirements for minimally invasive and safety. This survey attempts to analyze the practical application of AR in neurosurgical navigation. Also, this survey describes future trends in augmented reality neurosurgical navigation systems.

METHODS

In this survey, we searched related keywords "augmented reality", "virtual reality", "neurosurgery", "surgical simulation", "brain tumor surgery", "neurovascular surgery", "temporal bone surgery", and "spinal surgery" through Google Scholar, World Neurosurgery, PubMed and Science Direct. We collected 85 articles published over the past five years in areas related to this survey.

RESULTS

Detailed study has been conducted on the application of AR in neurosurgery and found that AR is constantly improving the overall efficiency of doctor training and treatment, which can help neurosurgeons learn and practice surgical procedures with zero risks.

CONCLUSIONS

Neurosurgical navigation is essential in neurosurgery. Despite certain technical limitations, it is still a necessary tool for the pursuit of maximum security and minimal intrusiveness. This article is protected by copyright. All rights reserved.

Image Guidance in Deep Brain Stimulation Surgery to Treat Parkinson's Disease: A Comprehensive Review

Deep brain stimulation (DBS) is an effective therapy as an alternative to pharmaceutical treatments for Parkinson's disease (PD). Aside from factors such as instrumentation, treatment plans, and surgical protocols, the success of the procedure depends heavily on the accurate placement of the electrode within the optimal therapeutic targets while avoiding vital structures that can cause surgical complications and adverse neurologic effects. Although specific surgical techniques for DBS can vary, interventional guidance with medical imaging has greatly contributed to the development, outcomes, and safety of the procedure. With rapid development in novel imaging techniques, computational methods, and surgical navigation software, as well as growing insights into the disease and mechanism of action of DBS, modern image guidance is expected to further enhance the capacity and efficacy of the procedure in treating PD. This article surveys the state-of-the-art techniques in image-guided DBS surgery to treat PD, and discusses their benefits and drawbacks, as well as future directions on the topic.

Virtual Reality in Neurosurgery: "Can You See It?"-A Review of the Current Applications and Future Potential

Virtual reality (VR) technology had its early development in the 1960s in the U.S. Air Force and has since evolved into a budding area of scientific research with many practical medical purposes. From medical education to resident training to the operating room, VR has provided tangible benefits to learners and trainees and has also improved surgery through enhanced preoperative planning and efficiency in the operating room. Neurosurgery is a particularly complex field of medicine, in which VR has blossomed into a tool with great usefulness and promise. In spinal surgery, VR simulation has allowed for the practice of innovative minimally invasive procedures. In cranial surgery, VR has excelled in helping neurosurgeons design unique patient-specific approaches to particularly challenging tumor excisions. In neurovascular surgery, VR has helped trainees practice and perfect procedures requiring high levels of dexterity to minimize intraoperative complications and patient radiation exposure. In peripheral nerve surgery, VR has allowed surgeons to gain increased practice and comfort with complex microsurgeries such as nerve decompression. Overall, VR continues to increase its potential in neurosurgery and is poised to benefit patients in a multitude of ways. Although cost-prohibiting, legal, and ethical challenges surrounding this technology must be considered, future research and more direct quantitative outcome comparisons between standard and VR-supplemented procedures would help provide more direction regarding the feasibility of widespread adoption of VR technology in neurosurgery.

SciKit-Surgery: compact libraries for surgical navigation

Purpose

This paper introduces the SciKit-Surgery libraries, designed to enable rapid development of clinical applications for image-guided interventions. SciKit-Surgery implements a family of compact, orthogonal, libraries accompanied by robust testing, documentation, and quality control. SciKit-Surgery libraries can be rapidly assembled into testable clinical applications and subsequently translated to production software without the need for software reimplementation. The aim is to support translation from single surgeon trials to multicentre trials in under 2 years.

Methods

At the time of publication, there were 13 SciKit-Surgery libraries provide functionality for visualisation and augmented reality in surgery, together with hardware interfaces for video, tracking, and ultrasound sources. The libraries are stand-alone, open source, and provide Python interfaces. This design approach enables fast development of robust applications and subsequent translation. The paper compares the libraries with existing platforms and uses two example applications to show how SciKit-Surgery libraries can be used in practice.

Results

Using the number of lines of code and the occurrence of cross-dependencies as proxy measurements of code complexity, two example applications using SciKit-Surgery libraries are analysed. The SciKit-Surgery libraries demonstrate ability to support rapid development of testable clinical applications. By maintaining stricter orthogonality between libraries, the number, and complexity of dependencies can be reduced. The SciKit-Surgery libraries also demonstrate the potential to support wider dissemination of novel research.

Conclusion

The SciKit-Surgery libraries utilise the modularity of the Python language and the standard data types of the NumPy package to provide an easy-to-use, well-tested, and extensible set of tools for the development of applications for image-guided interventions. The example application built on SciKit-Surgery has a simpler dependency structure than the same application built using a monolithic platform, making ongoing clinical translation more feasible.

The human K-complex: Insights from combined scalp-intracranial EEG recordings

Sleep spindles and K-complexes (KCs) are a hallmark of N2 sleep. While the functional significance of spindles is comparatively well investigated, there is still ongoing debate about the role of the KC: it is unclear whether it is a cortical response to an arousing stimulus (either external or internal) or whether it has sleep-promoting properties. Invasive intracranial EEG recordings from individuals with drug-resistant epilepsy offer a unique opportunity to study in-situ human brain physiology. To better understand the function of the KC, we aimed to (i) investigate the intracranial correlates of spontaneous scalp KCs, and (ii) compare the intracranial activity of scalp KCs associated or not with arousals. Whole-night recordings from adults with drug-resistant focal epilepsy who underwent combined intracranial-scalp EEG for pre-surgical evaluation at the Montreal Neurological Institute between 2010 and 2018 were selected. KCs were visually marked in the scalp and categorized according to the presence of microarousals: (i) Pre-microarousal KCs; (ii) KCs during an ongoing microarousal; and (iii) KCs without microarousal. Power in different spectral bands was computed to compare physiological intracranial EEG activity at the time of scalp KCs relative to the background, as well as to compare microarousal subcategories. A total of 1198 scalp KCs selected from 40 subjects were analyzed, resulting in 32,504 intracranial KC segments across 992 channels. Forty-seven percent of KCs were without microarousal, 30% were pre-microarousal, and 23% occurred during microarousals. All scalp KCs were accompanied by widespread cortical increases in delta band power (0.3-4 ​Hz) relative to the background: the highest percentages were observed in the parietal (60-65%) and frontal cortices (52-58%). Compared to KCs without microarousal, pre-microarousal KCs were accompanied by increases (66%) in beta band power (16-30 ​Hz) in the motor cortex, which was present before the peak of the KC. In addition, spatial distribution of spectral power changes following each KC without microarousal revealed that certain brain regions were associated with increases in delta power (25-62%) or decreases in alpha/beta power (11-24%), suggesting a sleep-promoting pattern, whereas others were accompanied by increases of higher frequencies (12-27%), suggesting an arousal-related pattern. This study shows that KCs can be generated across widespread cortical areas. Interestingly, the motor cortex shows awake-like EEG activity before the onset of KCs followed by microarousals. Our findings also highlight region-specific sleep- or arousal-promoting responses following KCs, suggesting a dual role for the human KC.

Image Overlay Surgery Based on Augmented Reality: A Systematic Review

Augmented Reality (AR) applied to surgical guidance is gaining relevance in clinical practice. AR-based image overlay surgery (i.e. the accurate overlay of patient-specific virtual images onto the body surface) helps surgeons to transfer image data produced during the planning of the surgery (e.g. the correct resection margins of tissue flaps) to the operating room, thus increasing accuracy and reducing surgery times. We systematically reviewed 76 studies published between 2004 and August 2018 to explore which existing tracking and registration methods and technologies allow healthcare professionals and researchers to develop and implement these systems in-house. Most studies used non-invasive markers to automatically track a patient's position, as well as customised algorithms, tracking libraries or software development kits (SDKs) to compute the registration between patient-specific 3D models and the patient's body surface. Few studies combined the use of holographic headsets, SDKs and user-friendly game engines, and described portable and wearable systems that combine tracking, registration, hands-free navigation and direct visibility of the surgical site. Most accuracy tests included a low number of subjects and/or measurements and did not normally explore how these systems affect surgery times and success rates. We highlight the need for more procedure-specific experiments with a sufficient number of subjects and measurements and including data about surgical outcomes and patients' recovery. Validation of systems combining the use of holographic headsets, SDKs and game engines is especially interesting as this approach facilitates an easy development of mobile AR applications and thus the implementation of AR-based image overlay surgery in clinical practice.

A comparison of thin-plate spline deformation and finite element modeling to compensate for brain shift during tumor resection

PURPOSE

Brain shift during tumor resection can progressively invalidate the accuracy of neuronavigation systems and affect neurosurgeons' ability to achieve optimal resections. This paper compares two methods that have been presented in the literature to compensate for brain shift: a thin-plate spline deformation model and a finite element method (FEM). For this comparison, both methods are driven by identical sparse data. Specifically, both methods are driven by displacements between automatically detected and matched feature points from intraoperative 3D ultrasound (iUS). Both methods have been shown to be fast enough for intraoperative brain shift correction (Machado et al. in Int J Comput Assist Radiol Surg 13(10):1525-1538, 2018; Luo et al. in J Med Imaging (Bellingham) 4(3):035003, 2017). However, the spline method requires no preprocessing and ignores physical properties of the brain while the FEM method requires significant preprocessing and incorporates patient-specific physical and geometric constraints. The goal of this work was to explore the relative merits of these methods on recent clinical data.

METHODS

Data acquired during 19 sequential tumor resections in Brigham and Women's Hospital's Advanced Multi-modal Image-Guided Operating Suite between December 2017 and October 2018 were considered for this retrospective study. Of these, 15 cases and a total of 24 iUS to iUS image pairs met inclusion requirements. Automatic feature detection (Machado et al. in Int J Comput Assist Radiol Surg 13(10):1525-1538, 2018) was used to detect and match features in each pair of iUS images. Displacements between matched features were then used to drive both the spline model and the FEM method to compensate for brain shift between image acquisitions. The accuracies of the resultant deformation models were measured by comparing the displacements of manually identified landmarks before and after deformation.

RESULTS

The mean initial subcortical registration error between preoperative MRI and the first iUS image averaged 5.3 ± 0.75 mm. The mean subcortical brain shift, measured using displacements between manually identified landmarks in pairs of iUS images, was 2.5 ± 1.3 mm. Our results showed that FEM was able to reduce subcortical registration error by a small but statistically significant amount (from 2.46 to 2.02 mm). A large variability in the results of the spline method prevented us from demonstrating either a statistically significant reduction in subcortical registration error after applying the spline method or a statistically significant difference between the results of the two methods.

CONCLUSIONS

In this study, we observed less subcortical brain shift than has previously been reported in the literature (Frisken et al., in: Miller (ed) Biomechanics of the brain, Springer, Cham, 2019). This may be due to the fact that we separated out the initial misregistration between preoperative MRI and the first iUS image from our brain shift measurements or it may be due to modern neurosurgical practices designed to reduce brain shift, including reduced craniotomy sizes and better control of intracranial pressure with the use of mannitol and other medications. It appears that the FEM method and its use of geometric and biomechanical constraints provided more consistent brain shift correction and better correction farther from the driving feature displacements than the simple spline model. The spline-based method was simpler and tended to give better results for small deformations. However, large variability in the spline results and relatively small brain shift prevented this study from demonstrating a statistically significant difference between the results of the two methods.

Atlas of the normal intracranial electroencephalogram: neurophysiological awake activity in different cortical areas

In contrast to scalp EEG, our knowledge of the normal physiological intracranial EEG activity is scarce. This multicentre study provides an atlas of normal intracranial EEG of the human brain during wakefulness. Here we present the results of power spectra analysis during wakefulness. Intracranial electrodes are placed in or on the brain of epilepsy patients when candidates for surgical treatment and non-invasive approaches failed to sufficiently localize the epileptic focus. Electrode contacts are usually in cortical regions showing epileptic activity, but some are placed in normal regions, at distance from the epileptogenic zone or lesion. Intracranial EEG channels defined using strict criteria as very likely to be in healthy brain regions were selected from three tertiary epilepsy centres. All contacts were localized in a common stereotactic space allowing the accumulation and superposition of results from many subjects. Sixty-second artefact-free sections during wakefulness were selected. Power spectra were calculated for 38 brain regions, and compared to a set of channels with no spectral peaks in order to identify significant peaks in the different regions. A total of 1785 channels with normal brain activity from 106 patients were identified. There were on average 2.7 channels per cm3 of cortical grey matter. The number of contacts per brain region averaged 47 (range 6-178). We found significant differences in the spectral density distributions across the different brain lobes, with beta activity in the frontal lobe (20-24 Hz), a clear alpha peak in the occipital lobe (9.25-10.25 Hz), intermediate alpha (8.25-9.25 Hz) and beta (17-20 Hz) frequencies in the parietal lobe, and lower alpha (7.75-8.25 Hz) and delta (0.75-2.25 Hz) peaks in the temporal lobe. Some cortical regions showed a specific electrophysiological signature: peaks present in >60% of channels were found in the precentral gyrus (lateral: peak frequency range, 20-24 Hz; mesial: 24-30 Hz), opercular part of the inferior frontal gyrus (20-24 Hz), cuneus (7.75-8.75 Hz), and hippocampus (0.75-1.25 Hz). Eight per cent of all analysed channels had more than one spectral peak; these channels were mostly recording from sensory and motor regions. Alpha activity was not present throughout the occipital lobe, and some cortical regions showed peaks in delta activity during wakefulness. This is the first atlas of normal intracranial EEG activity; it includes dense coverage of all cortical regions in a common stereotactic space, enabling direct comparisons of EEG across subjects. This atlas provides a normative baseline against which clinical EEGs and experimental results can be compared. It is provided as an open web resource (https://mni-open-ieegatlas.

RESEARCH

mcgill.ca).

Presurgical and Intraoperative Augmented Reality in Neuro-Oncologic Surgery: Clinical Experiences and Limitations

Virtual reality (VR) and augmented reality (AR) represent novel adjuncts for neurosurgical planning in neuro-oncology. In addition to established use in surgical and medical training, VR/AR are gaining traction for clinical use preoperatively and intraoperatively. To understand the utility of VR/AR in the clinical setting, we conducted a literature search in Ovid MEDLINE and EMBASE with various search terms designed to capture the use of VR/AR in neurosurgical procedures for resection of cranial tumors. The search retrieved 302 articles, of which 35 were subjected to full-text review; 19 full-text articles were included in the review. Key findings highlighted by the individual authors were extracted and summarized into themes to present the value of VR/AR in the clinical setting. These studies included various VR/AR systems applied to surgeries involving heterogeneous pathologies and outcome measures. Overall, VR/AR were found to be qualitatively advantageous due to enhanced visualization of complex anatomy and improved intraoperative lesion localization. When these technologies were compared with existing neuronavigation systems, quantitative clinical benefits were also reported. The capacity to visualize three-dimensional images superimposed on patient anatomy is a potentially valuable tool in complex neurosurgical environments. Surgical limitations may be addressed through future advances in image registration and tracking as well as intraoperatively acquired imaging with the ability to yield real-time virtual models.

Cognitive load associations when utilizing auditory display within image-guided neurosurgery

PURPOSE

The combination of data visualization and auditory display (e.g., sonification) has been shown to increase accuracy, and reduce perceived difficulty, within 3D navigation tasks. While accuracy within such tasks can be measured in real time, subjective impressions about the difficulty of a task are more elusive to obtain. Prior work utilizing electrophysiology (EEG) has found robust support that cognitive load and working memory can be monitored in real time using EEG data.

METHODS

In this study, we replicated a 3D navigation task (within the context of image-guided surgery) while recording data pertaining to participants' cognitive load through the use of EEG relative alpha-band weighting data. Specifically, 13 subjects navigated a tracked surgical tool to randomly placed 3D virtual locations on a CT cerebral angiography volume while being aided by visual, aural, or both visual and aural feedback. During the study EEG data were captured from the participants, and after the study a NASA TLX questionnaire was filled out by the subjects. In addition to replicating an existing experimental design on auditory display within image-guided neurosurgery, our primary aim sought to determine whether EEG-based markers of cognitive load mirrored subjective ratings of task difficulty RESULTS : Similar to existing literature, our study found evidence consistent with the hypothesis that auditory display can increase the accuracy of navigating to a specified target. We also found significant differences in cognitive working load across different feedback modalities, but none of which supported the experiments hypotheses. Finally, we found mixed results regarding the relationship between real-time measurements of cognitive workload and a posteriori subjective impressions of task difficulty.

CONCLUSIONS

Although we did not find a significant correlation between the subjective and physiological measurements, differences in cognitive working load were found. As well, our study further supports the use of auditory display in image-guided surgery.

Preliminary Results Comparing Thin Plate Splines with Finite Element Methods for Modeling Brain Deformation during Neurosurgery using Intraoperative Ultrasound

Brain shift compensation attempts to model the deformation of the brain which occurs during the surgical removal of brain tumors to enable mapping of presurgical image data into patient coordinates during surgery and thus improve the accuracy and utility of neuro-navigation. We present preliminary results from clinical tumor resections that compare two methods for modeling brain deformation, a simple thin plate spline method that interpolates displacements and a more complex finite element method (FEM) that models physical and geometric constraints of the brain and its material properties. Both methods are driven by the same set of displacements at locations surrounding the tumor. These displacements were derived from sets of corresponding matched features that were automatically detected using the SIFT-Rank algorithm. The deformation accuracy was tested using a set of manually identified landmarks. The FEM method requires significantly more preprocessing than the spline method but both methods can be used to model deformations in the operating room in reasonable time frames. Our preliminary results indicate that the FEM deformation model significantly out-performs the spline-based approach for predicting the deformation of manual landmarks. While both methods compensate for brain shift, this work suggests that models that incorporate biophysics and geometric constraints may be more accurate.

Virtual reality and augmented reality in the management of intracranial tumors: A review

Neurosurgeons are faced with the challenge of planning, performing, and learning complex surgical procedures. With improvements in computational power and advances in visual and haptic display technologies, augmented and virtual surgical environments can offer potential benefits for tests in a safe and simulated setting, as well as improve management of real-life procedures. This systematic literature review is conducted in order to investigate the roles of such advanced computing technology in neurosurgery subspecialization of intracranial tumor removal. The study would focus on an in-depth discussion on the role of virtual reality and augmented reality in the management of intracranial tumors: the current status, foreseeable challenges, and future developments.