Continuous Instrument Tracking in a Cerebral Corticectomy Ex Vivo Calf Brain Simulation Model: Face and Content Validation.

BACKGROUND AND OBJECTIVES: Subpial corticectomy involving complete lesion resection while preserving pial membranes and avoiding injury to adjacent normal tissues is an essential bimanual task necessary for neurosurgical trainees to master. We sought to develop an ex vivo calf brain corticectomy simulation model with continuous assessment of surgical instrument movement during the simulation. A case series study of skilled participants was performed to assess face and content validity to gain insights into the utility of this training platform, along with determining if skilled and less skilled participants had statistical differences in validity assessment. METHODS: An ex vivo calf brain simulation model was developed in which trainees performed a subpial corticectomy of three defined areas. A case series study assessed face and content validity of the model using 7-point Likert scale questionnaires. RESULTS: Twelve skilled and 11 less skilled participants were included in this investigation. Overall median scores of 6.0 (range 4.0-6.0) for face validity and 6.0 (range 3.5-7.0) for content validity were determined on the 7-point Likert scale, with no statistical differences between skilled and less skilled groups identified. CONCLUSION: A novel ex vivo calf brain simulator was developed to replicate the subpial resection procedure and demonstrated face and content validity.

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.

Estimating medical image registration error and confidence: A taxonomy and scoping review.

Given that image registration is a fundamental and ubiquitous task in both clinical and research domains of the medical field, errors in registration can have serious consequences. Since such errors can mislead clinicians during image-guided therapies or bias the results of a downstream analysis, methods to estimate registration error are becoming more popular. To give structure to this new heterogenous field we developed a taxonomy and performed a scoping review of methods that quantitatively and automatically provide a dense estimation of registration error. The taxonomy breaks down error estimation methods into Approach (Image- or Transformation-based), Framework (Machine Learning or Direct) and Measurement (error or confidence) components. Following the PRISMA guidelines for scoping reviews, the 570 records found were reduced to twenty studies that met inclusion criteria, which were then reviewed according to the proposed taxonomy. Trends in the field, advantages and disadvantages of the methods, and potential sources of bias are also discussed. We provide suggestions for best practices and identify areas of future research.

Excitatory-inhibitory balance within EEG microstates and resting-state fMRI networks: assessed via simultaneous trimodal PET-MR-EEG imaging.

The symbiosis of neuronal activities and glucose energy metabolism is reflected in the generation of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) signals. However, their association with the balance between neuronal excitation and inhibition (E/I-B), which is closely related to the activities of glutamate and γ-aminobutyric acid (GABA) and the receptor availability (RA) of GABAA and mGluR5, remains unexplored. This research investigates these associations during the resting state (RS) condition using simultaneously recorded PET/MR/EEG (trimodal) data. The trimodal data were acquired from three studies using different radio-tracers such as, [11C]ABP688 (ABP) (N = 9), [11C]Flumazenil (FMZ) (N = 10) and 2-[18F]fluoro-2-deoxy-D-glucose (FDG) (N = 10) targeted to study the mGluR5, GABAA receptors and glucose metabolism respectively. Glucose metabolism and neuroreceptor binding availability (non-displaceable binding potential (BPND)) of GABAA and mGluR5 were found to be significantly higher and closely linked within core resting-state networks (RSNs). The neuronal generators of EEG microstates and the fMRI measures were most tightly associated with the BPND of GABAA relative to mGluR5 BPND and the glucose metabolism, emphasising a predominance of inhibitory processes within in the core RSNs at rest. Changes in the neuroreceptors leading to an altered coupling with glucose metabolism may render the RSNs vulnerable to psychiatric conditions. The paradigm employed here will likely help identify the precise neurobiological mechanisms behind these alterations in fMRI functional connectivity and EEG oscillations, potentially benefitting individualised healthcare treatment measures.