A Regions of Interest Voxel-Based Morphometry Study of the Human Brain During High-Frequency Spinal Cord Stimulation in Patients With Failed Back Surgery Syndrome.

INTRODUCTION: The effectiveness of spinal cord stimulation (SCS) as pain-relieving treatment for failed back surgery syndrome (FBSS) has already been demonstrated. However, potential structural and functional brain alterations resulting from subsensory SCS are less clear. The aim of this study was to test structural volumetric changes in a priori chosen regions of interest related to chronic pain after 1 month and 3 months of high-frequency SCS in patients with FBSS. METHODS: Eleven patients with FBSS who were scheduled for SCS device implantation were included in this study. All patients underwent a magnetic resonance imaging protocol before SCS device implantation 1 and 3 months after high-frequency SCS. Pain intensity, pain catastrophizing, and sleep quality were also measured. Regions-of-interest voxel-based morphometry was used to explore grey matter volumetric changes over time. Additionally, volumetric changes were correlated with changes in pain intensity, catastrophizing, and sleep quality. RESULTS: Significant decreases were found in volume in the left and right hippocampus over time. More specifically, a significant difference was revealed between volumes before SCS implantation and after 3 months of SCS. Repeated-measures correlations revealed a significant positive correlation between volumetric changes in the left hippocampus and changes in back pain score over time and between volumetric changes in the right hippocampus and changes in back pain score over time. CONCLUSION: In patients with FBSS, high-frequency SCS influences structural brain regions over time. The volume of the hippocampus was decreased bilaterally after 3 months of high-frequency SCS with a positive correlation with back pain intensity.

Computer-vision based analysis of the neurosurgical scene - A systematic review.

Introduction: With increasing use of robotic surgical adjuncts, artificial intelligence and augmented reality in neurosurgery, the automated analysis of digital images and videos acquired over various procedures becomes a subject of increased interest. While several computer vision (CV) methods have been developed and implemented for analyzing surgical scenes, few studies have been dedicated to neurosurgery. Research question: In this work, we present a systematic literature review focusing on CV methodologies specifically applied to the analysis of neurosurgical procedures based on intra-operative images and videos. Additionally, we provide recommendations for the future developments of CV models in neurosurgery. Material and methods: We conducted a systematic literature search in multiple databases until January 17, 2023, including Web of Science, PubMed, IEEE Xplore, Embase, and SpringerLink. Results: We identified 17 studies employing CV algorithms on neurosurgical videos/images. The most common applications of CV were tool and neuroanatomical structure detection or characterization, and to a lesser extent, surgical workflow analysis. Convolutional neural networks (CNN) were the most frequently utilized architecture for CV models (65%), demonstrating superior performances in tool detection and segmentation. In particular, mask recurrent-CNN manifested most robust performance outcomes across different modalities. Discussion and conclusion: Our systematic review demonstrates that CV models have been reported that can effectively detect and differentiate tools, surgical phases, neuroanatomical structures, as well as critical events in complex neurosurgical scenes with accuracies above 95%. Automated tool recognition contributes to objective characterization and assessment of surgical performance, with potential applications in neurosurgical training and intra-operative safety management.

Neuro-oncological augmented reality planning for intracranial tumor resection.

Background: Before starting surgery for the resection of an intracranial tumor, its outlines are typically marked on the skin of the patient. This allows for the planning of the optimal skin incision, craniotomy, and angle of approach. Conventionally, the surgeon determines tumor borders using neuronavigation with a tracked pointer. However, interpretation errors can lead to important deviations, especially for deep-seated tumors, potentially resulting in a suboptimal approach with incomplete exposure. Augmented reality (AR) allows displaying of the tumor and critical structures directly on the patient, which can simplify and improve surgical preparation. Methods: We developed an AR-based workflow for intracranial tumor resection planning deployed on the Microsoft HoloLens II, which exploits the built-in infrared-camera for tracking the patient. We initially performed a phantom study to assess the accuracy of the registration and tracking. Following this, we evaluated the AR-based planning step in a prospective clinical study for patients undergoing resection of a brain tumor. This planning step was performed by 12 surgeons and trainees with varying degrees of experience. After patient registration, tumor outlines were marked on the patient's skin by different investigators, consecutively using a conventional neuronavigation system and an AR-based system. Their performance in both registration and delineation was measured in terms of accuracy and duration and compared. Results: During phantom testing, registration errors remained below 2.0 mm and 2.0° for both AR-based navigation and conventional neuronavigation, with no significant difference between both systems. In the prospective clinical trial, 20 patients underwent tumor resection planning. Registration accuracy was independent of user experience for both AR-based navigation and the commercial neuronavigation system. AR-guided tumor delineation was deemed superior in 65% of cases, equally good in 30% of cases, and inferior in 5% of cases when compared to the conventional navigation system. The overall planning time (AR = 119 ± 44 s, conventional = 187 ± 56 s) was significantly reduced through the adoption of the AR workflow (p < 0.001), with an average time reduction of 39%. Conclusion: By providing a more intuitive visualization of relevant data to the surgeon, AR navigation provides an accurate method for tumor resection planning that is quicker and more intuitive than conventional neuronavigation. Further research should focus on intraoperative implementations.