Image-guided robotic neurosurgery—an in vitro and in vivo point accuracy evaluation experimental study

Stereotactic Neuro-Navigation Phantom Designs: A Systematic Review

Diverse stereotactic neuro-navigation systems are used daily in neurosurgery and novel systems are continuously being developed. Prior to clinical implementation of new surgical tools, methods or instruments, in vitro experiments on phantoms should be conducted. A stereotactic neuro-navigation phantom denotes a rigid or deformable structure resembling the cranium with the intracranial area. The use of phantoms is essential for the testing of complete procedures and their workflows, as well as for the final validation of the application accuracy. The aim of this study is to provide a systematic review of stereotactic neuro-navigation phantom designs, to identify their most relevant features, and to identify methodologies for measuring the target point error, the entry point error, and the angular error (α). The literature on phantom designs used for evaluating the accuracy of stereotactic neuro-navigation systems, i.e., robotic navigation systems, stereotactic frames, frameless navigation systems, and aiming devices, was searched. Eligible articles among the articles written in English in the period 2000–2020 were identified through the electronic databases PubMed, IEEE, Web of Science, and Scopus. The majority of phantom designs presented in those articles provide a suitable methodology for measuring the target point error, while there is a lack of objective measurements of the entry point error and angular error. We identified the need for a universal phantom design, which would be compatible with most common imaging techniques (e.g., computed tomography and magnetic resonance imaging) and suitable for simultaneous measurement of the target point, entry point, and angular errors.

Stereotactic Systems for MRI-Guided Neurosurgeries: A State-of-the-Art Review

Recent technological developments in magnetic resonance imaging (MRI) and stereotactic techniques have significantly improved surgical outcomes. Despite the advantages offered by the conventional MRI-guided stereotactic neurosurgery, the robotic-assisted stereotactic approach has potential to further improve the safety and accuracy of neurosurgeries. This review aims to provide an update on the potential and continued growth of the MRI-guided stereotactic neurosurgical techniques by describing the state of the art in MR conditional stereotactic devices including manual and robotic-assisted. The paper also presents a detailed overview of MRI-guided stereotactic devices, MR conditional actuators and encoders used in MR conditional robotic-assisted stereotactic devices. The review concludes with several research challenges and future perspectives, including actuator and sensor technique, MR image guidance, and robot design issues.

A novel miniature robotic device for frameless implantation of depth electrodes in refractory epilepsy

OBJECTIVE The authors' group recently published a novel technique for a navigation-guided frameless stereotactic approach for the placement of depth electrodes in epilepsy patients. To improve the accuracy of the trajectory and enhance the procedural workflow, the authors implemented the iSys1 miniature robotic device in the present study into this routine. METHODS As a first step, a preclinical phantom study was performed using a human skull model, and the accuracy and timing between 5 electrodes implanted with the manual technique and 5 with the aid of the robot were compared. After this phantom study showed an increased accuracy with robot-assisted electrode placement and confirmed the robot's ability to maintain stability despite the rotational forces and the leverage effect from drilling and screwing, patients were enrolled and analyzed for robot-assisted depth electrode placement at the authors' institution from January 2014 to December 2015. All procedures were performed with the S7 Surgical Navigation System with Synergy Cranial software and the iSys1 miniature robotic device. RESULTS Ninety-three electrodes were implanted in 16 patients (median age 33 years, range 3-55 years; 9 females, 7 males). The authors saw a significant increase in accuracy compared with their manual technique, with a median deviation from the planned entry and target points of 1.3 mm (range 0.1-3.4 mm) and 1.5 mm (range 0.3-6.7 mm), respectively. For the last 5 patients (31 electrodes) of this series the authors modified their technique in placing a guide for implantation of depth electrodes (GIDE) on the bone and saw a significant further increase in the accuracy at the entry point to 1.18 ± 0.5 mm (mean ± SD) compared with 1.54 ± 0.8 mm for the first 11 patients (p = 0.021). The median length of the trajectories was 45.4 mm (range 19-102.6 mm). The mean duration of depth electrode placement from the start of trajectory alignment to fixation of the electrode was 15.7 minutes (range 8.5-26.6 minutes), which was significantly faster than with the manual technique. In 12 patients, depth electrode placement was combined with subdural electrode placement. The procedure was well tolerated in all patients. The authors did not encounter any case of hemorrhage or neurological deficit related to the electrode placement. In 1 patient with a psoriasis vulgaris, a superficial wound infection was encountered. Adequate physiological recordings were obtained from all electrodes. No additional electrodes had to be implanted because of misplacement. CONCLUSIONS The iSys1 robotic device is a versatile and easy to use tool for frameless implantation of depth electrodes for the treatment of epilepsy. It increased the accuracy of the authors' manual technique by 60% at the entry point and over 30% at the target. It further enhanced and expedited the authors' procedural workflow.

Hyper- and viscoelastic modeling of needle and brain tissue interaction

Deep needle insertion into brain is important for both diagnostic and therapeutic clinical interventions. We have developed an automated system for robotically steering flexible needles within the brain to improve targeting accuracy. In this work, we have developed a finite element needle-tissue interaction model that allows for the investigation of safe parameters for needle steering. The tissue model implemented contains both hyperelastic and viscoelastic properties to simulate the instantaneous and time-dependent responses of brain tissue. Several needle models were developed with varying parameters to study the effects of the parameters on tissue stress, strain and strain rate during needle insertion and rotation. The parameters varied include needle radius, bevel angle, bevel tip fillet radius, insertion speed, and rotation speed. The results will guide the design of safe needle tips and control systems for intracerebral needle steering.

In vivo measurement of the frame-based application accuracy of the Neuromate neurosurgical robot

OBJECT

The application accuracy of the Neuromate neurosurgical robot has been validated in vitro but has not been evaluated in vivo for deep brain stimulation (DBS) electrode implantations. The authors conducted a study to evaluate this application accuracy in routine frame-based DBS procedures, with an independent system of measurement.

METHODS

The Euclidian distance was measured between the point theoretically targeted by the robot and the point actually reached, based on their respective stereotactic coordinates. The coordinates of the theoretical target were given by the robot's dedicated targeting software. The coordinates of the point actually reached were recalculated using the Stereoplan localizer system. This experiment was performed in vitro, with the frame fixed in the robot space without a patient, for 21 points spatially distributed. The in vivo accuracy was then measured in 30 basal ganglia targets in 17 consecutive patients undergoing DBS for movement disorders.

RESULTS

The mean in vitro application accuracy was 0.44 ± 0.23 mm. The maximal localization error was 1.0 mm. The mean (± SD) in vivo application accuracy was 0.86 ± 0.32 mm (Δx = 0.37 ± 0.34 mm, Δy = 0.32 ± 0.24 mm, Δz = 0.58 ± 0.31 mm). The maximal error was 1.55 mm.

CONCLUSIONS

The in vivo application accuracy of the Neuromate neurosurgical robot, measured with a system independent from the robot, in frame-based DBS procedures was better than 1 mm. This accuracy is at least similar to the accuracy of stereotactic frame arms and is compatible with the accuracy required in DBS procedures.

Robotics in keyhole transcranial endoscope-assisted microsurgery: a critical review of existing systems and proposed specifications for new robotic platforms

BACKGROUND

Over the past decade, advances in image guidance, endoscopy, and tube-shaft instruments have allowed for the further development of keyhole transcranial endoscope-assisted microsurgery, utilizing smaller craniotomies and minimizing exposure and manipulation of unaffected brain tissue. Although such approaches offer the possibility of shorter operating times, reduced morbidity and mortality, and improved long-term outcomes, the technical skills required to perform such surgery are inevitably greater than for traditional open surgical techniques, and they have not been widely adopted by neurosurgeons. Surgical robotics, which has the ability to improve visualization and increase dexterity, therefore has the potential to enhance surgical performance.

OBJECTIVE

To evaluate the role of surgical robots in keyhole transcranial endoscope-assisted microsurgery.

METHODS

The technical challenges faced by surgeons utilizing keyhole craniotomies were reviewed, and a thorough appraisal of presently available robotic systems was performed.

RESULTS

Surgical robotic systems have the potential to incorporate advances in augmented reality, stereoendoscopy, and jointed-wrist instruments, and therefore to significantly impact the field of keyhole neurosurgery. To date, over 30 robotic systems have been applied to neurosurgical procedures. The vast majority of these robots are best described as supervisory controlled, and are designed for stereotactic or image-guided surgery. Few telesurgical robots are suitable for keyhole neurosurgical approaches, and none are in widespread clinical use in the field.

CONCLUSION

New robotic platforms in minimally invasive neurosurgery must possess clear and unambiguous advantages over conventional approaches if they are to achieve significant clinical penetration.

Advances in diagnostic and treatment modalities for intracranial tumors

Intracranial neoplasia is a common clinical condition in domestic companion animals, particularly in dogs. Application of advances in standard diagnostic and therapeutic modalities together with a broad interest in the development of novel translational therapeutic strategies in dogs has resulted in clinically relevant improvements in outcome for many canine patients. This review highlights the status of current diagnostic and therapeutic approaches to intracranial neoplasia and areas of novel treatment currently in development.

Analytical methods for brain targeted delivery system in vivo: perspectives on imaging modalities and microdialysis

Since the introduction of microdialysis in 1974, the semi-invasive analytical method has grown exponentially. Microdialysis is one of the most potential analysis technologies of pharmacological drug delivery to the brain. In recent decades, analysis of chemicals targeting the brain has led to many improvements. It seems likely that fluorescence imaging was limited to ex vivo and in vitro applications with the exception of several intravital microscopy and photographic imaging approaches. X-ray computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) have been commonly utilized for visualization of distribution and therapeutic effects of drugs. The efficient analytical methods for studies of brain-targeting delivery system is a major challenge in detecting the disposition as well as the variances of the factors that regulate the substances delivery into the brain. In this review, we highlight some of the ongoing trends in imaging modalities and the most recent developments in the field of microdialysis of live animals and present insights into exploiting brain disease for therapeutic and diagnostics purpose.

Advances in medical robotic systems with specific applications in surgery--a review

Although robotics was started as a form of entertainment, it gradually became used in different branches of science. Medicine, particularly in the operating room, has been influenced significantly by this field. Robotic technologies have offered valuable enhancements to medical or surgical processes through improved precision, stability and dexterity. In this paper we review different robotics and computer-assisted systems developed with medical and surgical applications. We cover early and recently developed systems in different branches of surgery. In addition to the united operational systems, we provide a review of miniature robotic, diagnostic and sensory systems developed to assist or collaborate with a main operator system. At the end of the paper, a discussion is given with the aim of summarizing the proposed points and predicting the future of robotics in medicine.

Robotic and artificial intelligence for keyhole neurosurgery: the ROBOCAST project, a multi-modal autonomous path planner

The robot and sensors integration for computer-assisted surgery and therapy (ROBOCAST) project (FP7-ICT-2007-215190) is co-funded by the European Union within the Seventh Framework Programme in the field of information and communication technologies. The ROBOCAST project focuses on robot- and artificial-intelligence-assisted keyhole neurosurgery (tumour biopsy and local drug delivery along straight or turning paths). The goal of this project is to assist surgeons with a robotic system controlled by an intelligent high-level controller (HLC) able to gather and integrate information from the surgeon, from diagnostic images, and from an array of on-field sensors. The HLC integrates pre-operative and intra-operative diagnostics data and measurements, intelligence augmentation, multiple-robot dexterity, and multiple sensory inputs in a closed-loop cooperating scheme including a smart interface for improved haptic immersion and integration. This paper, after the overall architecture description, focuses on the intelligent trajectory planner based on risk estimation and human criticism. The current status of development is reported, and first tests on the planner are shown by using a real image stack and risk descriptor phantom. The advantages of using a fuzzy risk description are given by the possibility of upgrading the knowledge on-field without the intervention of a knowledge engineer.

Data-guide for brain deformation in surgery: comparison of linear and nonlinear models

Background

Pre-operative imaging devices generate high-resolution images but intra-operative imaging devices generate low-resolution images. To use high-resolution pre-operative images during surgery, they must be deformed to reflect intra-operative geometry of brain.

Methods

We employ biomechanical models, guided by low resolution intra-operative images, to determine location of normal and abnormal regions of brain after craniotomy. We also employ finite element methods to discretize and solve the related differential equations. In the process, pre- and intra-operative images are utilized and corresponding points are determined and used to optimize parameters of the models. This paper develops a nonlinear model and compares it with linear models while our previous work developed and compared linear models (mechanical and elastic).

Results

Nonlinear model is evaluated and compared with linear models using simulated and real data. Partial validation using intra-operative images indicates that the proposed models reduce the localization error caused by brain deformation after craniotomy.

Conclusions

The proposed nonlinear model generates more accurate results than the linear models. When guided by limited intra-operative surface data, it predicts deformation of entire brain. Its execution time is however considerably more than those of linear models.