Stereotactic Neurosurgical Robotics With Real-Time Patient Tracking: A Cadaveric Study.

BACKGROUND: Robotic neurosurgery may improve the accuracy, speed, and availability of stereotactic procedures. We recently developed a computer vision and artificial intelligence-driven frameless stereotaxy for nonimmobilized patients, creating an opportunity to develop accurate and rapidly deployable robots for bedside cranial intervention. OBJECTIVE: To validate a portable stereotactic surgical robot capable of frameless registration, real-time tracking, and accurate bedside catheter placement. METHODS: Four human cadavers were used to evaluate the robot's ability to maintain low surface registration and targeting error for 72 intracranial targets during head motion, ie, without rigid cranial fixation. Twenty-four intracranial catheters were placed robotically at predetermined targets. Placement accuracy was verified by computed tomography imaging. RESULTS: Robotic tracking of the moving cadaver heads occurred with a program runtime of 0.111 ± 0.013 seconds, and the movement command latency was only 0.002 ± 0.003 seconds. For surface error tracking, the robot sustained a 0.588 ± 0.105 mm registration accuracy during dynamic head motions (velocity of 6.647 ± 2.360 cm/s). For the 24 robotic-assisted intracranial catheter placements, the target registration error was 0.848 ± 0.590 mm, providing a user error of 0.339 ± 0.179 mm. CONCLUSION: Robotic-assisted stereotactic procedures on mobile subjects were feasible with this robot and computer vision image guidance technology. Frameless robotic neurosurgery potentiates surgery on nonimmobilized and awake patients both in the operating room and at the bedside. It can affect the field through improving the safety and ability to perform procedures such as ventriculostomy, stereo electroencephalography, biopsy, and potentially other novel procedures. If we envision catheter misplacement as a "never event," robotics can facilitate that reality.

Frameless neuronavigation with computer vision and real-time tracking for bedside external ventricular drain placement: a cadaveric study.

OBJECTIVE: A major obstacle to improving bedside neurosurgical procedure safety and accuracy with image guidance technologies is the lack of a rapidly deployable, real-time registration and tracking system for a moving patient. This deficiency explains the persistence of freehand placement of external ventricular drains, which has an inherent risk of inaccurate positioning, multiple passes, tract hemorrhage, and injury to adjacent brain parenchyma. Here, the authors introduce and validate a novel image registration and real-time tracking system for frameless stereotactic neuronavigation and catheter placement in the nonimmobilized patient. METHODS: Computer vision technology was used to develop an algorithm that performed near-continuous, automatic, and marker-less image registration. The program fuses a subject's preprocedure CT scans to live 3D camera images (Snap-Surface), and patient movement is incorporated by artificial intelligence-driven recalibration (Real-Track). The surface registration error (SRE) and target registration error (TRE) were calculated for 5 cadaveric heads that underwent serial movements (fast and slow velocity roll, pitch, and yaw motions) and several test conditions, such as surgical draping with limited anatomical exposure and differential subject lighting. Six catheters were placed in each cadaveric head (30 total placements) with a simulated sterile technique. Postprocedure CT scans allowed comparison of planned and actual catheter positions for user error calculation. RESULTS: Registration was successful for all 5 cadaveric specimens, with an overall mean (± standard deviation) SRE of 0.429 ± 0.108 mm for the catheter placements. Accuracy of TRE was maintained under 1.2 mm throughout specimen movements of low and high velocities of roll, pitch, and yaw, with the slowest recalibration time of 0.23 seconds. There were no statistically significant differences in SRE when the specimens were draped or fully undraped (p = 0.336). Performing registration in a bright versus a dimly lit environment had no statistically significant effect on SRE (p = 0.742 and 0.859, respectively). For the catheter placements, mean TRE was 0.862 ± 0.322 mm and mean user error (difference between target and actual catheter tip) was 1.674 ± 1.195 mm. CONCLUSIONS: This computer vision-based registration system provided real-time tracking of cadaveric heads with a recalibration time of less than one-quarter of a second with submillimetric accuracy and enabled catheter placements with millimetric accuracy. Using this approach to guide bedside ventriculostomy could reduce complications, improve safety, and be extrapolated to other frameless stereotactic applications in awake, nonimmobilized patients.

Immunoenrichment microwave and magnetic proteomics for quantifying CD47 in the experimental autoimmune encephalomyelitis model of multiple sclerosis.

We hypothesized that quantitative MS/MS-based proteomics at multiple time points, incorporating immunoenrichment prior to rapid microwave and magnetic (IM(2) ) sample preparation, might enable correlation of the relative expression of CD47 and other low abundance proteins to disease progression in the experimental autoimmune encephalomyelitis (EAE) animal model of multiple sclerosis. To test our hypothesis, anti-CD47 antibodies were used to enrich for low abundance CD47 prior to microwave and magnetic proteomics in EAE. Decoding protein expression at each time point, with CD47-immunoenriched samples and targeted proteomic analysis, enabled peptides from the low abundance proteins to be precisely quantified throughout disease progression, including: CD47: 86-99, corresponding to the "marker of self" overexpressed by myelin that prevents phagocytosis, or "cellular devouring," by microglia and macrophages; myelin basic protein: 223-228, corresponding to myelin basic protein; and migration inhibitory factor: 79-87, corresponding to a proinflammatory cytokine that inhibits macrophage migration. While validation in a larger cohort is underway, we conclude that IM(2) proteomics is a rapid method to precisely quantify peptides from CD47 and other low abundance proteins throughout disease progression in EAE. This is likely due to improvements in selectivity and sensitivity, necessary to partially overcome masking of low abundance proteins by high abundance proteins and improve dynamic range.

Microwave and magnetic (M(2) ) proteomics of the experimental autoimmune encephalomyelitis animal model of multiple sclerosis.

We hypothesized that quantitative MS/MS-based proteomics at multiple time points, incorporating rapid microwave and magnetic (M(2) ) sample preparation, could enable relative protein expression to be correlated to disease progression in the experimental autoimmune encephalomyelitis (EAE) animal model of multiple sclerosis. To test our hypothesis, microwave-assisted reduction/alkylation/digestion of proteins from brain tissue lysates bound to C8 magnetic beads and microwave-assisted isobaric chemical labeling were performed of released peptides, in 90 s prior to unbiased proteomic analysis. Disease progression in EAE was assessed by scoring clinical EAE disease severity and confirmed by histopathologic evaluation for central nervous system inflammation. Decoding the expression of 283 top-ranked proteins (p <0.05) at each time point relative to their expression at the peak of disease, from a total of 1191 proteins observed in four technical replicates, revealed a strong statistical correlation to EAE disease score, particularly for the following four proteins that closely mirror disease progression: 14-3-3ε (p = 3.4E-6); GPI (p = 2.1E-5); PLP1 (p = 8.0E-4); PRX1 (p = 1.7E-4). These results were confirmed by Western blotting, signaling pathway analysis, and hierarchical clustering of EAE risk groups. While validation in a larger cohort is underway, we conclude that M(2) proteomics is a rapid method to quantify putative prognostic/predictive protein biomarkers and therapeutic targets of disease progression in the EAE animal model of multiple sclerosis.