Computational image analysis of distortion, sharpness, and depth of field in a next-generation hybrid exoscopic and microsurgical operative platform.

Objective: The development of surgical microscope-associated cameras has given rise to a new operating style embodied by hybrid microsurgical and exoscopic operative systems. These platforms utilize specialized camera systems to visualize cranial neuroanatomy at various depths. Our study aims to understand how different camera settings in a novel hybrid exoscope system influence image quality in the context of neurosurgical procedures. Methods: We built an image database using captured cadaveric dissection images obtained with a prototype version of a hybrid (microsurgical/exoscopic) operative platform. We performed comprehensive 4K-resolution image capture using 76 camera settings across three magnification levels and two working distances. Computer algorithms such as structural similarity (SSIM) and mean squared error (MSE) were used to measure image distortion across different camera settings. We utilized a Laplacian filter to compute the overall sharpness of the acquired images. Additionally, a monocular depth estimation deep learning model was used to examine the image's capability to visualize the depth of deeper structures accurately. Results: A total of 1,368 high-resolution pictures were captured. The SSIM index ranged from 0.63 to 0.85. The MSE was nearly zero for all image batches. It was determined that the exoscope could accurately detect both the sharpness and depth based on the Laplacian filter and depth maps, respectively. Our findings demonstrate that users can utilize the full range of camera settings available on the exoscope, including adjustments to aperture, color saturation, contrast, sharpness, and brilliance, without introducing significant image distortions relative to the standard mode. Conclusion: The evolution of the camera incorporated into a surgical microscope enables exoscopic visualization during cranial base surgery. Our result should encourage surgeons to take full advantage of the exoscope's extensive range of camera settings to match their personal preferences or specific clinical requirements of the surgical scenario. This places the exoscope as an invaluable asset in contemporary surgical practice, merging high-definition imaging with ergonomic design and adaptable operability.

Interactive microsurgical anatomy education using photogrammetry 3D models and an augmented reality cube.

OBJECTIVE: This study sought to assess the use of an augmented reality (AR) tool for neurosurgical anatomical education. METHODS: Three-dimensional models were created using advanced photogrammetry and registered onto a handheld AR foam cube imprinted with scannable quick response codes. A perspective analysis of the cube anatomical system was performed by loading a 3D photogrammetry model over a motorized turntable to analyze changes in the surgical window area according to the horizontal rotation. The use of the cube as an intraoperative reference guide for surgical trainees was tested during cadaveric dissection exercises. Neurosurgery trainees from international programs located in Ankara, Turkey; San Salvador, El Salvador; and Moshi, Tanzania, interacted with and assessed the 3D models and AR cube system and then completed a 17-item graded user experience survey. RESULTS: Seven photogrammetry 3D models were created and imported to the cube. Horizontal turntable rotation of the cube translated to measurable and realistic perspective changes in the surgical window area. The combined 3D models and cube system were used to engage trainees during cadaveric dissections, with satisfactory user experience. Thirty-five individuals (20 from Turkey, 10 from El Salvador, and 5 from Tanzania) agreed that the cube system could enhance the learning experience for neurosurgical anatomy. CONCLUSIONS: The AR cube combines tactile and visual sensations with high-resolution 3D models of cadaveric dissections. Inexpensive and lightweight, the cube can be effectively implemented to allow independent co-visualization of anatomical dissection and can potentially supplement neurosurgical education.

Step-by-Step Dissection of the Extreme Lateral Transodontoid Approach to the Anterior Craniovertebral Junction: Surgical Anatomy and Technical Nuances.

BACKGROUND: Multicompartmental lesions of the anterior craniovertebral junction require aggressive management. However, the lesions can be difficult to reach, and the surgical procedure is difficult to understand. The aim of this study was to create a procedural, stepwise microsurgical educational resource for junior trainees to learn the surgical anatomy of the extreme lateral transodontoid approach (ELTOA). METHODS: Ten formalin-fixed, latex-injected cadaveric heads were dissected under an operative microscope. Dissections were performed under the supervision of a skull base fellowship-trained neurosurgeon who has advanced skull base experience. Key steps of the procedure were documented with a professional camera and a high-definition video system. A relevant clinical case example was reviewed to highlight the principles of the selected approach and its application. The clinical case example also describes a rare complication: a pseudoaneurysm of the vertebral artery. RESULTS: Key steps of the ELTOA include patient positioning, skin incision, superficial and deep muscle dissection, vertebral artery dissection and transposition, craniotomy, clivus drilling, odontoidectomy, and final extradural and intradural exposure. CONCLUSIONS: The ELTOA is a challenging approach, but it allows for significant access to the anterior craniovertebral junction, which increases the likelihood of gross total lesion resection. Given the complexity of the approach, substantial training in the dissection laboratory is required to develop the necessary anatomic knowledge and to minimize approach-related morbidity.

Infragalenic triangle as a gateway to dorsal midbrain and posteromedial thalamic lesions: descriptive and quantitative analysis of microsurgical anatomy.

OBJECTIVE: Anatomical triangles provide neurosurgeons with the specificity required to access deep targets, supplementing more general instructions, such as craniotomy and approach. The infragalenic triangle (IGT), bordered by the basal vein of Rosenthal (BVR), precentral cerebellar vein (PCV), and the quadrangular lobule of the cerebellum, is one of a system of anatomical triangles recently introduced to guide dissection to brainstem cavernous malformations and has not been described in detail. This study aimed to quantitatively analyze the anatomical parameters of the IGT and present key nuances for its microsurgical use. METHODS: A midline supracerebellar infratentorial (SCIT) approach through a torcular craniotomy was performed on 5 cadaveric heads, and the IGT was identified in each specimen bilaterally. Anatomical measurements were obtained with point coordinates collected using neuronavigation. Three cadaveric brains were used to illustrate relevant brainstem anatomy, and 3D virtual modeling was used to simulate various perspectives of the IGT through different approach angles. In addition, 2 illustrative surgical cases are presented. RESULTS: The longest edge of the IGT was the lateral edge formed by the BVR (mean ± SD length 19.1 ± 2.3 mm), and the shortest edge was the medial edge formed by the PCV (13.9 ± 3.6 mm). The mean surface area of the IGT was 110 ± 34.2 mm2 in the standard exposure. Full expansion of all 3 edges (arachnoid dissection, mobilization, and retraction) resulted in a mean area of 226.0 ± 48.8 mm2 and a 2.5-times increase in surface area exposure of deep structures (e.g., brainstem and thalamus). Thus, almost the entire tectal plate and its relevant safe entry zones can be exposed through an expanded unilateral IGT except for the contralateral inferior colliculus, access to which is usually hindered by PCV tributaries. Exposure of bilateral IGTs may be required to resect larger midline lesions to increase surgical maneuverability or to access the contralateral pulvinar. CONCLUSIONS: The IGT provides a safe access route to the dorsal midbrain and reliable intraoperative guidance in the deep and complex anatomy of the posterior tentorial incisura. Its potential for expansion makes it a versatile anatomical corridor not only for intrinsic brainstem lesions but also for tumors and vascular malformations of the pineal region, dorsal midbrain, and posteromedial thalamus.

Modified Puerto Rico Recurrence Scale for chronic subdural hematomas: augmenting the grading scale with postoperative pneumocephalus volume.

BACKGROUND: Chronic subdural hematomas (CSDHs) are common in the elderly, with a relatively high rate of recurrence after initial surgical intervention. Our research team previously created a predictive grading system, the Puerto Rico Recurrence Scale (PRRS), to identify patients at high risk of CSDH recurrence. In this study, we introduce a modification of the (mPRRS) that includes pneumocephalus volume, which has been independently associated with recurrence. METHODS: A single-center Puerto Rican population-based retrospective study was performed to analyze data for patients treated for CSDH at 1 institution between July 1, 2017, and December 31, 2019. Univariate and multivariate analyses were used to create a grading scale predictive of recurrence. Retrospective validation was conducted for the cohort. RESULTS: Of 108 patients included in the study, 42 had recurrence, and 66 had nonrecurrence. Postoperative subdural space, postoperative midline shift, and pneumocephalus volume were all higher with recurrence (P = 0.002, P = 0.009, and P < 0.001, respectively). Multivariate analysis was used to create a 6-point grading scale comprising 3 variables (pneumocephalus volume [< 10, 10-20, 21-30, and > 30 cm3], postoperative midline shift [< 0.4, 0.41-1.0, and > 1.0 cm], and laterality [unilateral and bilateral]). Recurrence rates progressively increased in low-risk to high-risk groups (2/18 [11%] vs 21/34 [62%]; P < 0.003). CONCLUSION: The mPRRS incorporating pneumocephalus measurement improves CSDH recurrence prediction. The mPRRS indicated that patients with higher scores have a greater risk of recurrence and emphasized the importance of measuring postoperative variables for prediction. The mPRRS grading scale for CSDHs may be applicable not only to the Puerto Rican population but also to the general population.

Quantification of motion during microvascular anastomosis simulation using machine learning hand detection.

OBJECTIVE: Microanastomosis is one of the most technically demanding and important microsurgical skills for a neurosurgeon. A hand motion detector based on machine learning tracking technology was developed and implemented for performance assessment during microvascular anastomosis simulation. METHODS: A microanastomosis motion detector was developed using a machine learning model capable of tracking 21 hand landmarks without physical sensors attached to a surgeon's hands. Anastomosis procedures were simulated using synthetic vessels, and hand motion was recorded with a microscope and external camera. Time series analysis was performed to quantify the economy, amplitude, and flow of motion using data science algorithms. Six operators with various levels of technical expertise (2 experts, 2 intermediates, and 2 novices) were compared. RESULTS: The detector recorded a mean (SD) of 27.6 (1.8) measurements per landmark per second with a 10% mean loss of tracking for both hands. During 600 seconds of simulation, the 4 nonexperts performed 26 bites in total, with a combined excess of motion of 14.3 (15.5) seconds per bite, whereas the 2 experts performed 33 bites (18 and 15 bites) with a mean (SD) combined excess of motion of 2.8 (2.3) seconds per bite for the dominant hand. In 180 seconds, the experts performed 13 bites, with mean (SD) latencies of 22.2 (4.4) and 23.4 (10.1) seconds, whereas the 2 intermediate operators performed a total of 9 bites with mean (SD) latencies of 31.5 (7.1) and 34.4 (22.1) seconds per bite. CONCLUSIONS: A hand motion detector based on machine learning technology allows the identification of gross and fine movements performed during microanastomosis. Economy, amplitude, and flow of motion were measured using time series data analysis. Technical expertise could be inferred from such quantitative performance analysis.

Three-Dimensional Modeling and Extended Reality Simulations of the Cross-Sectional Anatomy of the Cerebrum, Cerebellum, and Brainstem.

BACKGROUND: Understanding the anatomy of the human cerebrum, cerebellum, and brainstem and their 3-dimensional (3D) relationships is critical for neurosurgery. Although 3D photogrammetric models of cadaver brains and 2-dimensional images of postmortem brain slices are available, neurosurgeons lack free access to 3D models of cross-sectional anatomy of the cerebrum, cerebellum, and brainstem that can be simulated in both augmented reality (AR) and virtual reality (VR). OBJECTIVE: To create 3D models and AR/VR simulations from 2-dimensional images of cross-sectionally dissected cadaveric specimens of the cerebrum, cerebellum, and brainstem. METHODS: The Klingler method was used to prepare 3 cadaveric specimens for dissection in the axial, sagittal, and coronal planes. A series of 3D models and AR/VR simulations were then created using 360° photogrammetry. RESULTS: High-resolution 3D models of cross-sectional anatomy of the cerebrum, cerebellum, and brainstem were obtained and used in creating AR/VR simulations. Eleven axial, 9 sagittal, and 7 coronal 3D models were created. The sections were planned to show important deep anatomic structures. These models can be freely rotated, projected onto any surface, viewed from all angles, and examined at various magnifications. CONCLUSION: To our knowledge, this detailed study is the first to combine up-to-date technologies (photogrammetry, AR, and VR) for high-resolution 3D visualization of the cross-sectional anatomy of the entire human cerebrum, cerebellum, and brainstem. The resulting 3D images are freely available for use by medical professionals and students for better comprehension of the 3D relationship of the deep and superficial brain anatomy.

Virtual neurosurgery anatomy laboratory: A collaborative and remote education experience in the metaverse.

Background: Advances in computer sciences, including novel 3-dimensional rendering techniques, have enabled the creation of cloud-based virtual reality (VR) interfaces, making real-time peer-to-peer interaction possible even from remote locations. This study addresses the potential use of this technology for microsurgery anatomy education. Methods: Digital specimens were created using multiple photogrammetry techniques and imported into a virtual simulated neuroanatomy dissection laboratory. A VR educational program using a multiuser virtual anatomy laboratory experience was developed. Internal validation was performed by five multinational neurosurgery visiting scholars testing and assessing the digital VR models. For external validation, 20 neurosurgery residents tested and assessed the same models and virtual space. Results: Each participant responded to 14 statements assessing the virtual models, categorized under realism (n = 3), usefulness (n = 2), practicality (n = 3), enjoyment (n = 3), and recommendation (n = 3). Most responses expressed agreement or strong agreement with the assessment statements (internal validation, 94% [66/70] total responses; external validation, 91.4% [256/280] total responses). Notably, most participants strongly agreed that this system should be part of neurosurgery residency training and that virtual cadaver courses through this platform could be effective for education. Conclusion: Cloud-based VR interfaces are a novel resource for neurosurgery education. Interactive and remote collaboration between instructors and trainees is possible in virtual environments using volumetric models created with photogrammetry. We believe that this technology could be part of a hybrid anatomy curriculum for neurosurgery education. More studies are needed to assess the educational value of this type of innovative educational resource.

Anatomic Depth Estimation and 3-Dimensional Reconstruction of Microsurgical Anatomy Using Monoscopic High-Definition Photogrammetry and Machine Learning.

BACKGROUND: Immersive anatomic environments offer an alternative when anatomic laboratory access is limited, but current three-dimensional (3D) renderings are not able to simulate the anatomic detail and surgical perspectives needed for microsurgical education. OBJECTIVE: To perform a proof-of-concept study of a novel photogrammetry 3D reconstruction technique, converting high-definition (monoscopic) microsurgical images into a navigable, interactive, immersive anatomy simulation. METHODS: Images were acquired from cadaveric dissections and from an open-access comprehensive online microsurgical anatomic image database. A pretrained neural network capable of depth estimation from a single image was used to create depth maps (pixelated images containing distance information that could be used for spatial reprojection and 3D rendering). Virtual reality (VR) experience was assessed using a VR headset, and augmented reality was assessed using a quick response code-based application and a tablet camera. RESULTS: Significant correlation was found between processed image depth estimations and neuronavigation-defined coordinates at different levels of magnification. Immersive anatomic models were created from dissection images captured in the authors' laboratory and from images retrieved from the Rhoton Collection. Interactive visualization and magnification allowed multiple perspectives for an enhanced experience in VR. The quick response code offered a convenient method for importing anatomic models into the real world for rehearsal and for comparing other anatomic preparations side by side. CONCLUSION: This proof-of-concept study validated the use of machine learning to render 3D reconstructions from 2-dimensional microsurgical images through depth estimation. This spatial information can be used to develop convenient, realistic, and immersive anatomy image models.