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.

SEEGAtlas: A framework for the identification and classification of depth electrodes using clinical images.

Objective.Accurate localization, classification, and visualization of intracranial electrodes are fundamental for analyzing intracranial electrographic recordings. While manual contact localization is the most common approach, it is time-consuming, prone to errors, and is particularly challenging and subjective in low quality images, which are common in clinical practice. Automatically locating and interactively visualizing where each of the 100-200 individual contacts records in the brain is essential for understanding the neural origins of intracranial EEG.Approach.We introduced the SEEGAtlas plugin for the IBIS system, an open-source software platform for image-guided neurosurgery and multi-modal image visualization. SEEGAtlas extends IBIS functionalities to semi-automatically locate depth-electrode contact coordinates and automatically label the tissue type and anatomical region in which each contact is located. To illustrate the capabilities of SEEGAtlas and to validate the algorithms, clinical magnetic resonance images (MRIs) before and after electrode implantation of ten patients with depth electrodes implanted to localize the origin of their epileptic seizures were analyzed.Main Results. Visually identified contact coordinates were compared with the coordinates obtained by SEEGAtlas, resulting in a median difference of 1.4 mm. The agreement was lower for MRIs with weak susceptibility artifacts than for high-quality images. The tissue type was classified with 86% agreement with visual inspection. The anatomical region was classified as having a median agreement across patients of 82%.Significance. The SEEGAtlas plugin is user-friendly and enables accurate localization and anatomical labeling of individual contacts along implanted electrodes, together with powerful visualization tools. Employing the open-source SEEGAtlas results in accurate analysis of the recorded intracranial electroencephalography (EEG), even when only suboptimal clinical imaging is available. A better understanding of the cortical origin of intracranial EEG would help improve clinical interpretation and answer fundamental questions of human neuroscience.

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.

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.

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.

Quantitation of Tissue Resection Using a Brain Tumor Model and 7-T Magnetic Resonance Imaging Technology.

BACKGROUND: Animal brain tumor models can be useful educational tools for the training of neurosurgical residents in risk-free environments. Magnetic resonance imaging (MRI) technologies have not used these models to quantitate tumor, normal gray and white matter, and total tissue removal during complex neurosurgical procedures. This pilot study was carried out as a proof of concept to show the feasibility of using brain tumor models combined with 7-T MRI technology to quantitatively assess tissue removal during subpial tumor resection. METHODS: Seven ex vivo calf brain hemispheres were used to develop the 7-T MRI segmentation methodology. Three brains were used to quantitate brain tissue removal using 7-T MRI segmentation methodology. Alginate artificial brain tumor was created in 4 calf brains to assess the ability of 7-T MRI segmentation methodology to quantitate tumor and gray and white matter along with total tissue volumes removal during a subpial tumor resection procedure. RESULTS: Quantitative studies showed a correlation between removed brain tissue weights and volumes determined from segmented 7-T MRIs. Analysis of baseline and postresection alginate brain tumor segmented 7-T MRIs allowed quantification of tumor and gray and white matter along with total tissue volumes removed and detection of alterations in surrounding gray and white matter. CONCLUSIONS: This pilot study showed that the use of animal tumor models in combination with 7-T MRI technology provides an opportunity to increase the granularity of data obtained from operative procedures and to improve the assessment and training of learners.

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.

Creating a Comprehensive Research Platform for Surgical Technique and Operative Outcome in Primary Brain Tumor Neurosurgery.

BACKGROUND: The operative environment poses many challenges to studying the relationship between surgical acts and patient outcomes in intracranial oncological neurosurgery. We sought to develop a framework in which neurosurgical performance and extent of resection could be precisely quantified in a controlled setting. METHODS: The stiffness of an alginate hydrogel-based tumor was modified with differing concentrations of the cross-linking agent calcium sulfate until biomechanical properties similar to those of human primary brain tumors measured at resection were achieved. The artificial tumor was subsequently incorporated into an ex-vivo animal brain as a final model. Magnetic resonance imaging enhancement and ultraviolet fluorescence was achieved by incorporating gadolinium and fluorescein solution, respectively. Video recordings from the operative microscope, ceiling cameras, and instrument-mounted fiducial markers within a surgical suite environment captured operative performance. RESULTS: A total of 24 rheometer measurements were conducted on alginate hydrogels containing 10-, 11-, and 12-mM concentrations of calcium sulfate. Sixty-eight stiffness measurements were conducted on eight patient tumor samples. No differences were found between the alginate and brain tumor stiffness values [Kruskal-Wallis χ2(4) = 9.187; P = 0.057]. Tumor was identified using ultraviolet fluorescence and ultrasonography. The volume and location of the resected white and gray matter and residual tumor could be quantified in 0.003-mm3 increments using a 7T magnetic resonance imaging coil. Ultrasonic aspirator and bipolar electrocautery movement data were successfully transformed into performance metrics. CONCLUSION: The developed framework can offer clinicians, learners, and researchers the ability to perform operative rehearsal, teaching, and studies involving brain tumor surgery in a controlled laboratory environment and represents a crucial step in the understanding and training of expertise in neurosurgery.

The state-of-the-art in ultrasound-guided spine interventions.

During the last two decades, intra-operative ultrasound (iUS) imaging has been employed for various surgical procedures of the spine, including spinal fusion and needle injections. Accurate and efficient registration of pre-operative computed tomography or magnetic resonance images with iUS images are key elements in the success of iUS-based spine navigation. While widely investigated in research, iUS-based spine navigation has not yet been established in the clinic. This is due to several factors including the lack of a standard methodology for the assessment of accuracy, robustness, reliability, and usability of the registration method. To address these issues, we present a systematic review of the state-of-the-art techniques for iUS-guided registration in spinal image-guided surgery (IGS). The review follows a new taxonomy based on the four steps involved in the surgical workflow that include pre-processing, registration initialization, estimation of the required patient to image transformation, and a visualization process. We provide a detailed analysis of the measurements in terms of accuracy, robustness, reliability, and usability that need to be met during the evaluation of a spinal IGS framework. Although this review is focused on spinal navigation, we expect similar evaluation criteria to be relevant for other IGS applications.

Toward real-time rigid registration of intra-operative ultrasound with preoperative CT images for lumbar spinal fusion surgery.

PURPOSE: Accurate and effective registration of the vertebrae is crucial for spine surgical navigation procedures. Patient movement, surgical instrumentation or inadvertent contact with the tracked reference during the intervention may invalidate the registration, requiring a rapid correction of the misalignment. In this paper, we present a framework to rigidly align preoperative computed tomography (CT) with the intra-operative ultrasound (iUS) images of a single vertebra. METHODS: We use a single caudo-cranial axial sweep procedure to acquire iUS images, from which the scan trajectory is exploited to initialize the registration transform. To refine the transform, locations of the posterior vertebra surface are first extracted, then used to compute the CT-to-iUS image intensity gradient-based alignment. The approach was validated on a lumbosacral section of a porcine cadaver. RESULTS: We achieved an overall median accuracy of 1.48 mm (success rate of 84.42%) in [Formula: see text] 11 s of computation time, satisfying the clinically accepted accuracy threshold of 2 mm. CONCLUSION: Our approach using intra-operative ultrasound to register patient vertebral anatomy to preoperative images matches the clinical needs in terms of accuracy and computation time, facilitating its integration into the surgical workflow.

Latency Management in Scribble-Based Interactive Segmentation of Medical Images.

OBJECTIVE: During an interactive image segmentation task, the outcome is strongly influenced by human factors. In particular, a reduction in computation time does not guarantee an improvement in the overall segmentation time. This paper characterizes user efficiency during scribble-based interactive segmentation as a function of computation time. METHODS: We report a controlled experiment with users who experienced eight different levels of simulated latency (ranging from 100 to 2000 ms) with two techniques for refreshing visual feedback (either automatic, where the segmentation was recomputed and displayed continuously during label drawing, or user initiated, which was only computed and displayed each time the user hits a defined button). RESULTS: For short latencies, the user's attention is focused on the automatic visual feedback, slowing down his/her labeling performance. This effect is attenuated as the latency grows larger, and the two refresh techniques yield similar user performance at the largest latencies. Moreover, during the segmentation task, participants spent in average for automatic refresh and for user-initiated refresh of the overall segmentation time interpreting the results. CONCLUSION: The latency is perceived differently according to the refresh method used during the segmentation task. Therefore, it is possible to reduce its impact on the user performance. SIGNIFICANCE: This is the first time a study investigates the effects of latency in an interactive segmentation task. The analysis and recommendations provided in this paper help understanding the cognitive mechanisms in interactive image segmentation.