Augmented Reality Intraoperative Tractography for diffuse glioma microsurgical resection

Augmented Reality Intraoperative Tractography for diffuse glioma microsurgical resection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Augmented Reality Intraoperative Tractography for diffuse glioma microsurgical resection Anton Konovalov, Andrey Bykanov, Dmitry Okishev, Anton Artemyev, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5444302/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study explores the use of augmented reality (AR) for intraoperative guidance during the microsurgical resection of diffuse gliomas, especially those located near the critical corticospinal tract. AR provides surgeons with a three-dimensional view of essential brain structures in real time, overcoming the limitations of traditional navigation systems and potentially improving surgical precision. In our case series involving five patients, we combined AR-based visualization with neurophysiological monitoring, allowing precise mapping of the corticospinal tract relative to the tumor. This approach contributed to complete tumor removal in most cases, while also preserving motor function in all patients. Our findings suggest that AR technology can enhance spatial understanding during complex surgeries, minimizing the risk to critical neural pathways. While our initial results are promising, demonstrating reliable alignment accuracy and improved outcomes, further studies on larger patient groups are necessary to fully understand and validate AR’s role in neurosurgery. This research underscores AR’s potential to improve both safety and outcomes, adding valuable tools for intraoperative navigation. Health sciences/Neurology/Neurological disorders Health sciences/Anatomy Health sciences/Medical research Health sciences/Neurology Physical sciences/Engineering Physical sciences/Nanoscience and technology augmented reality intraoperative navigation tractography diffuse glioma corticospinal tract microsurgical resection neurophysiological monitoring Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Resection of the brain tumors located in the central gyrus region, adjacent to the pyramidal tract, is one of the most demanding tasks in neurosurgery. Because this region of the brain contains many functionally important neural structures, such as the pyramidal tract responsible for motor function, tumors in this area are challenging to excise safely. Damage to these structures during an operative procedure may lead to debilitating neurological deficits, including contralateral limb and facial muscle paresis, or even complete plegia. Above all, intraoperative navigation remains essential for the preservation of functionally relevant brain regions( 1 ). Conventional frameless neuronavigation methods including optical and magnetic neuronavigation cannot provide enough three-dimensional visualization of different brain structures and they are typically associated with a high risk of motor pathway damage( 2 ). In these cases, the gold standard is the use of neurophysiological monitoring. The ability to stimulate both in bipolar and monopolar fashion allows for continual information regarding the integrity of motor pathways and function as well as providing immediate feedback on the proximity of the surgical instruments to motor tracts( 2 ). Augmented reality (AR) represents a cutting-edge technology that enables the projection of 3D anatomical models directly onto the surgical field. In recent years, this technology has gained traction in both neurosurgery and broader surgical applications( 3 , 4 ). AR provides a way to visualize the position and size of lesions, along with nearby vascular structures(5). However, there is a lack of clinical series that explore the use of AR in identifying the pyramidal tract during the resection of diffuse brain tumors, particularly those located in the central gyrus( 6 , 7 ). Furthermore, data on the accuracy of AR in this context and its potential benefits when combined with neurophysiological monitoring are scarce( 3 – 7 ) This study seeks to evaluate the feasibility of AR tract visualization as an additional tool during the resection of diffuse tumors in the central gyrus, in conjunction with neurophysiological monitoring. Results The mean TRE across all patients was 2.68 mm, ranging from 2.4 to 3.2 mm, while the mean FRE was 2.4 mm, with a range of 2.2 to 2.7 mm (Table 1 ). These values demonstrate sufficient accuracy when using the AR model alignment method with the QR frame for planning and performing craniotomy, locating the tumor, and identifying vascular structures (sinus, parasinus veins, and convexity veins). In four cases, complete tumor removal (gross total resection, GTR) was achieved. For one patient (Patient #2), neurophysiological monitoring responses at a current of 5 mA were detected at the base of the tumor resection bed, prompting the decision to halt further tumor removal. Follow-up MRI performed one month postoperatively showed no residual tumor tissue in four patients (Fig. 1 ). A key aspect was the correspondence between the AR projection of the pyramidal tract and neurophysiological monitoring data. Intraoperative neurophysiological monitoring, utilizing monopolar stimulation at 10.6 mA ± 5.57 mA and bipolar stimulation at 17.0 mA ± 10.45 mA, confirmed the preservation of pyramidal tract functional integrity. During surgery, the AR projection of the pyramidal tract matched neurophysiological monitoring data and aligned with the stimulation vector, minimizing the risk of damage to critical structures. Based on neurophysiological monitoring data, the current intensity required to stimulate the corticospinal tract ranged from 3 mA to 30 mA, indicating an estimated distance to the pyramidal tract between 3 mm and 30 mm. The mean distance was 13.8 mm, with a standard deviation of 8.18 mm. These results demonstrate significant variability in the distance to the tract, likely due to individual anatomical characteristics and tumor location in each patient. Postoperative MRI tractography showed a distance to the corticospinal tract ranging from 2.4 mm to 20.1 mm, with an average value of 8.3 mm and a standard deviation of 5.36 mm. These data show a strong correlation (correlation coefficient of 0.87) between intraoperative neurophysiological monitoring results and postoperative tractography, confirming the high accuracy of the intraoperative method in assessing the proximity to functionally significant brain structures (Fig. 2 ). Table 1 Group Characteristics Patient # Gender, Age (y.o.) Tumor Location Histology TRE (mm) FRE (mm) Complete Tumor Resection NFM (Stimulation, mA) Tract Distance (Post-op MRI TG) Functional Outcome (MRC) Week 1 / Month 1 1 M,38 Left Frontal Lobe Anaplastic Astrocytoma Grade 3 3,2 2,7 (σ = 0.6) GTR Mono − 18 mA, Bi − 30 mA 9,3–17,2 mm 5/5 2 F,41 Right Parietal Lobe Astrocytoma Grade 2 2,6 2,4 (σ = 0.4) Subtotal Mono − 15 mA, Bi − 25 mA 5,3–20,1 mm 5/5 3 M, 38 Right Frontal Lobe Astrocytoma Grade 2 2,4 2,2 (σ = 0.5) GTR Mono − 3 mA, Bi − 5 mA 3,2–11,2 mm 4/5 4 M,49 Left Frontal Lobe Anaplastic Astrocytoma Grade 3 2,7 2,5 (σ = 0.5) GTR Mono − 5 mA, Bi − 7 mA 3,0–7,5 mm 4/5 5 M,23 Left Parietal Lobe Anaplastic Astrocytoma Grade 3 2,5 2,3 (σ = 0.4) GTR Mono − 12 mA, Bi − 18 mA 2,4–11,2 mm 5/5 Functional outcomes were assessed at one week and one month postoperatively using the MRC scale. At the one-week mark, three patients retained full motor function (scored 5/5 on the MRC scale), while two patients exhibited moderate motor deficits (scored 4/5 on the MRC scale). By the end of the first month, all patients showed complete recovery of muscle strength, achieving 5/5 on the MRC scale, indicating favorable outcomes and the absence of long-term motor impairments. No intraoperative or early postoperative complications related to the use of AR navigation were recorded in any of the patients. In all cases, significant vessels and functionally critical structures, including the pyramidal tract and cortical veins, were successfully preserved without damage. Discussion Augmented reality (AR) in neurosurgery is a promising tool that enables the real-time overlay of 3D neuroimaging data directly onto the surgical field. This eliminates the need for surgeons to shift their focus to monitors and mentally align 2D images with 3D anatomy, potentially reducing errors and enhancing precision ( 11 ). AR assists in visualizing complex tumors and their relationships with critical brain structures, improving spatial understanding of neuroanatomy and facilitating intraoperative orientation, which may ultimately improve surgical outcomes (Fig. 3 ). Gliomas in the precentral area are among the most challenging neurosurgical pathologies due to the high risk of functional deficits following surgery. In most cases, postoperative neurological deficits are linked to damage to the corticospinal tract, the primary motor pathway that connects motor neurons in the precentral gyrus to motor neurons in the spinal cord ( 12 ). Notably, only 40–60% of the fibers in the pyramidal tract originate in the precentral gyrus, with the remainder primarily arising from neurons in the postcentral gyrus and premotor cortex ( 13 , 14 ). Identifying and preserving corticospinal tract fibers is a complex task that requires a broad range of techniques, from neurophysiological monitoring to intraoperative MRI( 15 ). Intraoperative neurophysiological monitoring plays a critical role in identifying motor areas on the cortical surface and motor pathways. A meta-analysis of 90 publications showed that motor monitoring reduces the incidence of permanent neurological deficits from 8.2–3.4% and increases the extent of resection from 58–75% ( 16 ). Currently, two main techniques are used for intraoperative electrical stimulation of motor cortical areas and corticospinal tract fibers: 50-Hz stimulation, first described by Penfield in 1937, and high-frequency multipulse stimulation, described by Taniguchi in 1993( 17 ). In our study, we proposed a method for tract visualization using AR capabilities, specifically intraoperative AR tractography based on preoperative MRI and DTI segmentation. Enhanced intraoperative spatial understanding of tract trajectories allows for optimal resection volume by correlating neurophysiological monitoring data with AR tractography during surgery. AR visualization of the tumor, corticospinal tract, and their relationships in cortical and subcortical areas can reduce the need for intraoperative brain electrical stimulations, potentially lowering the risk of intraoperative seizures or post-stimulation motor deficits. AR tractography enables selective visualization of tracts, allowing 3D tract models to be added or removed from the surgical field as needed, thus adapting the course of the operation. Additionally, the technology supports data updates to account for brain shift, integration with other AR techniques (e.g., fluorescent navigation), and use in awake surgeries. From a technical standpoint, various studies have employed different approaches to image acquisition and processing, registration and tracking systems, and data visualization during surgery ( 7 , 18 ). The lack of a unified standard for assessing the accuracy of virtual object registration poses challenges for precise neurosurgery and affects the reliability of AR for the localization and resection of intracranial targets. Additionally, most software and hardware solutions used are experimental and not widely available for surgical applications, necessitating caution in interpreting results. In our study, we used MRI tractography as one of the confirmatory methods. However, this method has significant limitations, as the results depend heavily on the operator and the chosen imaging algorithm ( 19 – 22 ). A primary limiting factor in all intraoperative visualization techniques (except for intraoperative MRI and ultrasound) is the "brain shift" effect—brain displacement during surgery. In our study, we addressed this issue by recalculating the model and re-registering points, adding new anchor points (e.g., veins or arteries) that move along with the displaced brain, which is particularly relevant in the final stages of surgery. The current limitations of AR tractography should also be noted. First, it requires knowledge of 3D segmentation and modeling fundamentals, as well as tractography skills, which may impact the quality of information obtained. The diffusion tractography (DTI) method, involving intra-voxel signal encoding, may lack precision. Another significant drawback is parallax error, inherent to all projection-based AR techniques, where the 3D model projection on a real object varies with changes in viewing angle. Using a QR code for model alignment helps mitigate this issue, but slight lag can occur with rapid movements of AR devices. The oversaturation of fiber tract visualizations can also pose a problem, obscuring important structures under layered images. To address this, we adjust model transparency and reduce the brightness of the displayed AR tract, enhancing the visibility of critical structures. The small number of patients prevents statistically significant conclusions; however, preliminary results indicate the potential benefits of the proposed method. One possible solution to the challenges of standardization and evidence accumulation is the creation of a consortium focused on standardizing AR workflows in neurosurgery, including the development of open-source software and methodologies. This would enable the accumulation of larger and more homogeneous datasets, facilitating the development and implementation of machine learning algorithms to improve surgical precision. Conclusion The application of AR navigation for the pyramidal tract in the microsurgical resection of brain tumors in the motor area demonstrates high accuracy and safety. This innovative approach can enhance the perception of pathological anatomy and improve the surgeon's orientation during surgery. AR tractography provides an additional measure of control over the location of the corticospinal tract when used alongside neurophysiological monitoring and may potentially reduce the risk of adverse functional outcomes. Materials and Methods This is a prospective single-center consecutive case series study conducted on patients with diffusely growing tumors located in the central gyrus area, for whom surgery with neurophysiological monitoring was planned. All operations were performed by the second author. The study took place at the Burdenko Neurosurgery Center in Moscow, from May to August 2024. The study protocol was reviewed and approved by the local ethics committee under protocol No. 2024-03 dated June 28, 2024. Written informed consent was obtained from all patients participating in the study, in accordance with the Declaration of Helsinki. This approval ensured full compliance with ethical standards, prioritizing patient safety and rights throughout the study. The study included five patients: four men and one woman, with a mean age of 37.8 years (ranging from 23 to 49 years). All patients presented with diffuse brain tumors located in functionally significant areas adjacent to the pyramidal tract. Three patients had tumors located in the frontal lobe (two on the left, one on the right), and two had tumors in the parietal lobe (one on the right, one on the left). In all cases, AR was used for intraoperative navigation in addition to neurophysiological monitoring. The inclusion criteria were: · Age between 18 and 65 years · Confirmed diagnosis based on MRI findings · Patient consent for surgery · Absence of contraindications to surgery · Completion of MRI tractography pre- and post-operatively Performance of head CT with thin slices and a QR frame to ensure AR navigation accuracy Preoperative MRI was performed for each patient on a 3-Tesla scanner. All sequences were acquired with an isotropic voxel and a slice thickness not exceeding 1 mm, achieving a spatial resolution of at least 1x1x1 mm with a cubic matrix. The following sequences were used: T1-weighted images with contrast enhancement, T2-weighted images, T2-weighted FLAIR images (fluid-attenuated inversion recovery), and DTI (diffusion tensor imaging) tractography with a minimum of 20 vectors. Segmentation and construction of a three-dimensional model of the tumor, pyramidal tract, and other necessary anatomical structures represent a critical step in preoperative planning. This step was performed using Inobitec DICOM Viewer. DTI (diffusion tensor imaging) data were used to construct the pyramidal tract, with each voxel providing directional diffusion information, enabling the visualization of individual nerve fibers. Based on these data, three-dimensional tract models were created in Inobitec DICOM Viewer. The method involved defining regions of interest (ROIs) to allow the software to automatically highlight and trace the direction of the pyramidal tract: · The first ROI was set on the ipsilateral motor cortex where the pyramidal tract originates and where the tumor was located. · The second ROI was positioned on the internal capsule region through which the main pyramidal tract fibers pass. · The third ROI was located on the anterior surface of the medulla oblongata. This multi-level filtering allowed isolated visualization of key pyramidal fibers sufficient for surgical planning. During export, the software enabled adjustments to the thickness and density of the displayed fibers. The Microsoft HoloLens 2 system, equipped with Medgital software (medgital.org), was used for intraoperative navigation. AR allowed the surgeon to overlay 3D models of the pyramidal tract, tumor, and cortical veins onto the surgical field in real time, facilitating more detailed and practical planning of the surgical approach. To ensure AR navigation accuracy, model alignment with the patient was achieved using a QR code, followed by verification against predefined craniometric landmarks on the models, including the tip of the nose, lateral canthus of the eye, glabella, bregma, and stephanion (Figure 4). The accuracy of AR model alignment with the QR code was evaluated by calculating TRE (Target Registration Error—measured as the average deviation between the projected model and the actual anatomical structures) and FRE (Fiducial Registration Error—measured based on fixed anatomical landmarks), as outlined below. Each patient also underwent a head CT scan with a QR code. The QR code's position relative to the head was then transferred to the MRI by fusing (fusion) the CT and MRI images in the Inobitec DICOM Viewer software. Through the integration and co-registration of all MRI sequences using the layer fusion tool and the export of all segmented structures, a multi-component 3D model was created, incorporating segmented skin with the QR frame, skull, brain structures, tumor, vessels, and tracts. The final model, after simplification and necessary scaling, was saved in GLB format, compatible with the Microsoft HoloLens 2 augmented reality system running Medgital software. The average time to construct the final GLB model was 28.2 ± 5.4 minutes. Before surgery, the QR frame was secured to the patient’s head in the same way as during the preoperative CT scan. The QR code served as a reference marker to ensure precise alignment of the virtual model with the patient’s physical anatomy (Figure 5A). During the procedure, the HoloLens 2 device recognized the QR code marker and aligned the 3D model relative to the patient’s head. The position of the 3D model was tracked and adjusted in real time throughout the use of the system. The AR technology enabled continuous display of the 3D models over the patient’s head during the entire surgery. In addition to AR, traditional neuronavigation methods were used; the frameless electromagnetic navigation system, Fiagon (Fiagon GmbH, Germany), provided additional guidance. In all cases, intraoperative neurophysiological monitoring (Nicolet Viking Select device; Natus) was employed to confirm the functional locations of the pyramidal tract and motor area. Stimulation was performed using both monopolar and bipolar stimulation throughout all stages of tumor microsurgical resection. To evaluate the alignment accuracy for AR navigation, the following metrics were used: · Target Registration Error (TRE) — measured as the average deviation between the projected tumor model and the actual anatomical location. · Fiducial Registration Error (FRE) — measured based on anatomical landmarks. For FRE calculation, we used the following craniometric points: tip of the nose, lateral canthus of the eye, glabella, bregma, and stephanion. Measurements were based on the displacement of superimposed 3D points relative to actual craniometric landmarks, recorded in pixels and then converted to millimeters. Patient head tracking also employed a QR code mounted on a fixed frame on the patient’s head (Figure 5B). The accuracy of pyramidal tract navigation was assessed by comparing neurophysiological monitoring data, specifically the stimulation current intensity and direction, with the projected 3D AR model of the pyramidal tract. This approach is based on the established relationship between current intensity and distance to the corticospinal tract, defined by the formula 1mA = 1 mm. Thus, by obtaining the intraoperative current threshold that triggers a motor-evoked potential response, the distance from this point to the AR-projected tract can be compared(8–10). All patients underwent motor function assessments before and after surgery. Muscle strength was evaluated using the Medical Research Council (MRC) scale, with assessments conducted one week and one month postoperatively. Neurological examination data and MRI scans were reviewed to assess the postoperative condition of the pyramidal tract and the extent of tumor resection. Volumentric analysis In all cases the preoperative and postoperative MRI scans were obtained using high-resolution magnetic resonance imaging scanner (GE, Optima MR450w, Boston, Massachu- setts, USA). The extent of resection (EOR) was assessed depending on MRI scans performed in the first 48 hours after surgery. The extent of tumor resection for non-contrast tumors was estimated on T2 and FLAIR, for contrast enhancing tumors – on contrast enhanced T1 images by two independent neuroradiologists. The EOR was calculated using the following formula[(tumor volume before surgery minus tumor volume after surgery) divided by tumor volume before surgical treatment]. Since this study is a case series aimed at establishing proof of concept, primary emphasis was placed on descriptive analysis. TRE and FRE values are presented as mean values ± standard deviation (SD). The main focus was on evaluating the accuracy of the AR navigation method for tracts during surgery. Declarations Funding: The study was supported by the Ministry of Science and Higher Education of the Russian Federation under agreement No. 075-15-2024-561. Author Contribution A.N.K. (Anton Nikolaevich Konovalov) conceived the study, led the project, and was responsible for the manuscript’s main text and overall coordination. A.Y.B. (Andrey Yegorovich Bykanov) and D.N.O. (Dmitry Nikolaevich Okishev) contributed to data collection and processing, as well as drafted sections of the methodology. A.A.A. (Anton Alekseevich Artemyev) and A.V.K. (Alexander Viktorovich Knyazev) performed the data analysis and prepared figures 1-3. V.M.I. (Vladimir Mikhailovich Ivanov) and A.Y.S. (Anton Yurievich Smirnov) assisted with statistical analysis and data interpretation. S.V.S. (Sergey Vasilyevich Strelkov) and I.N.P. (Igor Nikolaevich Pronin) developed and implemented the augmented reality software. G.V.P. (Galina Valerievna Pavlova) and D.I.P. (David Ilyich Pithelauri) contributed to study design and patient recruitment. S.S.E. (Shalva Shalvovich Eliava) reviewed the manuscript for critical intellectual content. All authors reviewed and approved the final manuscript for submission. Acknowledgement The authors wish to thank the patients who participated in this study, as well as the clinical and technical staff at the N.N. Burdenko National Medical Research Center of Neurosurgery for their invaluable support in data collection and patient care. We extend our gratitude to the Institute for Bionic Technologies and Engineering at I.M. Sechenov First Moscow State Medical University for providing essential resources for this research. Special thanks go to the Medgital team for their assistance in developing the augmented reality software that made this study possible. We also acknowledge the support of Peter the Great St. Petersburg Polytechnic University in facilitating collaborative efforts across institutions. This work would not have been possible without the dedication and expertise of everyone involved. Data Availability The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. References Kosyrkova AV, Goryainov SA, Ogurtsova AA, Okhlopkov VA, Kravchuk AD, Batalov AI, et al. Comparative analysis of mono- and bipolar pyramidal tract mapping in patients with supratentorial tumors adjacent to motor areas: comparison of data at 64 stimulation points. Voprosy neirokhirurgii imeni NN Burdenko. 2020;84(5):29. https://doi.org/10.17116/neiro20208405129 Catani M, Thiebaut de Schotten M. A diffusion tensor imaging tractography atlas for virtual in vivo dissections. Cortex. 2008;44(8):1105–32. 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Principles and limitations of computational algorithms in clinical diffusion tensor MR tractography. Vol. 32, American Journal of Neuroradiology. 2011. p. 3–13. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5444302","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":385983625,"identity":"80ab8ab1-bdfd-4150-bfc1-a6cd86dffef6","order_by":0,"name":"Anton 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Petersburg Polytechnic University\", Saint Petersburg","correspondingAuthor":false,"prefix":"","firstName":"Vladimir","middleName":"","lastName":"Ivanov","suffix":""},{"id":385983631,"identity":"e1ad7ea0-0f52-414c-ac37-7ffff58b0871","order_by":6,"name":"Anton Smirnov","email":"","orcid":"","institution":"Federal State Autonomous Educational Institution of Higher Education \"Peter the Great St. Petersburg Polytechnic University\", Saint Petersburg","correspondingAuthor":false,"prefix":"","firstName":"Anton","middleName":"","lastName":"Smirnov","suffix":""},{"id":385983632,"identity":"6875b579-a112-43a7-9587-e753bb0ae27e","order_by":7,"name":"Sergey Strelkov","email":"","orcid":"","institution":"Llc «Medgital», Saint Petersburg, Russian Federation","correspondingAuthor":false,"prefix":"","firstName":"Sergey","middleName":"","lastName":"Strelkov","suffix":""},{"id":385983637,"identity":"e4c014e0-93c5-4ab8-bd02-6107fd02aff4","order_by":8,"name":"Igor Pronin","email":"","orcid":"","institution":"Burdenko Neurosurgery Institute","correspondingAuthor":false,"prefix":"","firstName":"Igor","middleName":"","lastName":"Pronin","suffix":""},{"id":385983639,"identity":"5395f887-16a8-4978-bb24-1c0f98fcf235","order_by":9,"name":"Galina Pavlova","email":"","orcid":"","institution":"Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Sciences (RAS)","correspondingAuthor":false,"prefix":"","firstName":"Galina","middleName":"","lastName":"Pavlova","suffix":""},{"id":385983641,"identity":"8129c4ee-9d7d-4662-a8b3-0a52db29022a","order_by":10,"name":"David Pithelauri","email":"","orcid":"","institution":"Burdenko Neurosurgery Institute","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Pithelauri","suffix":""},{"id":385983643,"identity":"952ced2d-1694-4c57-a213-0355f6a706c5","order_by":11,"name":"Shalva Eliava","email":"","orcid":"","institution":"Burdenko Neurosurgery Institute","correspondingAuthor":false,"prefix":"","firstName":"Shalva","middleName":"","lastName":"Eliava","suffix":""}],"badges":[],"createdAt":"2024-11-13 06:38:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5444302/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5444302/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71145213,"identity":"530d5a69-b0b9-4805-b6d2-60fe26b35d95","added_by":"auto","created_at":"2024-12-11 14:18:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1170656,"visible":true,"origin":"","legend":"\u003cp\u003ePanels A–E illustrate five patients who underwent microsurgical tumor resection using intraoperative tract visualization with augmented reality technologies. The top row of images shows preoperative MRI data for each patient, including MR tractography. The middle row displays intraoperative information captured during tumor resection, with visualizations of 3D models of the tumor, pyramidal tract, cortical bridging veins, superior sagittal sinus, and other relevant structures. The bottom row presents postoperative MRI and MR tractography data.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5444302/v1/4d020301f38e02f36fd339fe.png"},{"id":71145216,"identity":"c0feb69d-159d-420f-b0d5-e7e3d52be669","added_by":"auto","created_at":"2024-12-11 14:18:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":317327,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Distribution of current intensity during monopolar and bipolar stimulation of the corticospinal tract. (B) Distribution of distances to the corticospinal tract as measured by neurophysiological monitoring (NFM) and postoperative MRI tractography (MRI-TG). (C) Correlation plot with a regression line, illustrating the relationship between the distances to the corticospinal tract obtained from NFM and MRI-TG data.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5444302/v1/7c9b680dbf467868996443a2.png"},{"id":71148386,"identity":"c64a9dc8-a552-452e-b7e2-f49094015fd8","added_by":"auto","created_at":"2024-12-11 14:34:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":667747,"visible":true,"origin":"","legend":"\u003cp\u003e(A) T2-weighted MRI image of the tumor and pyramidal tract in the coronal projection. (B) Intraoperative view of AR model projection onto the surgical field during tumor resection. (C) Intraoperative view of the cortical surface with an overlaid AR model, displaying semi-transparent models of the pyramidal tract and venous vessels relative to the tumor prior to its resection. (D) Projection of the pyramidal tract and venous vessels onto the tumor resection cavity.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5444302/v1/669724d344209e8171d7a756.png"},{"id":71147917,"identity":"c5d3779c-20af-490a-8aab-5706998fb45e","added_by":"auto","created_at":"2024-12-11 14:26:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1195170,"visible":true,"origin":"","legend":"\u003cp\u003eStep-by-step creation of an AR model for a patient with a tumor in the right frontal lobe and the pyramidal tract. (A) T2-weighted image in the coronal plane shows segmented models of the tumor (in red) and the pyramidal tract (in green). (B) 3D visualization of the model and creation of a GLB project for import into the AR headset. A 3D model of the superior sagittal sinus and two parasinus veins, located anterior and posterior to the tumor (in blue), is also constructed. (C) Visualization of the 3D model in augmented reality mode using Microsoft HoloLens 2. (D) Overlay of the AR project onto the patient’s head and marking relative to the visualized structures.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5444302/v1/6b668c9136e96a377d434a9a.png"},{"id":71145221,"identity":"c57e5de7-bb6b-429d-8e27-59e47ca5cd74","added_by":"auto","created_at":"2024-12-11 14:18:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":303016,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Patient head registration method for augmented reality (AR) usage: QR code-based tracking. (B) Alignment of the left frontal lobe tumor model with the pyramidal tract using the QR frame.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5444302/v1/7261cdfd30bd5f44c1532eec.png"},{"id":79408471,"identity":"41315e47-368c-4ed9-a610-c5428740b774","added_by":"auto","created_at":"2025-03-28 05:01:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6136525,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5444302/v1/642f9043-3318-40e2-9fed-fe969b1e5fc4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Augmented Reality Intraoperative Tractography for diffuse glioma microsurgical resection","fulltext":[{"header":"Introduction","content":"\u003cp\u003eResection of the brain tumors located in the central gyrus region, adjacent to the pyramidal tract, is one of the most demanding tasks in neurosurgery. Because this region of the brain contains many functionally important neural structures, such as the pyramidal tract responsible for motor function, tumors in this area are challenging to excise safely. Damage to these structures during an operative procedure may lead to debilitating neurological deficits, including contralateral limb and facial muscle paresis, or even complete plegia. Above all, intraoperative navigation remains essential for the preservation of functionally relevant brain regions(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConventional frameless neuronavigation methods including optical and magnetic neuronavigation cannot provide enough three-dimensional visualization of different brain structures and they are typically associated with a high risk of motor pathway damage(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In these cases, the gold standard is the use of neurophysiological monitoring. The ability to stimulate both in bipolar and monopolar fashion allows for continual information regarding the integrity of motor pathways and function as well as providing immediate feedback on the proximity of the surgical instruments to motor tracts(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAugmented reality (AR) represents a cutting-edge technology that enables the projection of 3D anatomical models directly onto the surgical field. In recent years, this technology has gained traction in both neurosurgery and broader surgical applications(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). AR provides a way to visualize the position and size of lesions, along with nearby vascular structures(5). However, there is a lack of clinical series that explore the use of AR in identifying the pyramidal tract during the resection of diffuse brain tumors, particularly those located in the central gyrus(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Furthermore, data on the accuracy of AR in this context and its potential benefits when combined with neurophysiological monitoring are scarce(\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThis study seeks to evaluate the feasibility of AR tract visualization as an additional tool during the resection of diffuse tumors in the central gyrus, in conjunction with neurophysiological monitoring.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe mean TRE across all patients was 2.68 mm, ranging from 2.4 to 3.2 mm, while the mean FRE was 2.4 mm, with a range of 2.2 to 2.7 mm (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These values demonstrate sufficient accuracy when using the AR model alignment method with the QR frame for planning and performing craniotomy, locating the tumor, and identifying vascular structures (sinus, parasinus veins, and convexity veins).\u003c/p\u003e \u003cp\u003eIn four cases, complete tumor removal (gross total resection, GTR) was achieved. For one patient (Patient #2), neurophysiological monitoring responses at a current of 5 mA were detected at the base of the tumor resection bed, prompting the decision to halt further tumor removal. Follow-up MRI performed one month postoperatively showed no residual tumor tissue in four patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A key aspect was the correspondence between the AR projection of the pyramidal tract and neurophysiological monitoring data.\u003c/p\u003e \u003cp\u003eIntraoperative neurophysiological monitoring, utilizing monopolar stimulation at 10.6 mA\u0026thinsp;\u0026plusmn;\u0026thinsp;5.57 mA and bipolar stimulation at 17.0 mA\u0026thinsp;\u0026plusmn;\u0026thinsp;10.45 mA, confirmed the preservation of pyramidal tract functional integrity. During surgery, the AR projection of the pyramidal tract matched neurophysiological monitoring data and aligned with the stimulation vector, minimizing the risk of damage to critical structures.\u003c/p\u003e \u003cp\u003eBased on neurophysiological monitoring data, the current intensity required to stimulate the corticospinal tract ranged from 3 mA to 30 mA, indicating an estimated distance to the pyramidal tract between 3 mm and 30 mm. The mean distance was 13.8 mm, with a standard deviation of 8.18 mm. These results demonstrate significant variability in the distance to the tract, likely due to individual anatomical characteristics and tumor location in each patient. Postoperative MRI tractography showed a distance to the corticospinal tract ranging from 2.4 mm to 20.1 mm, with an average value of 8.3 mm and a standard deviation of 5.36 mm. These data show a strong correlation (correlation coefficient of 0.87) between intraoperative neurophysiological monitoring results and postoperative tractography, confirming the high accuracy of the intraoperative method in assessing the proximity to functionally significant brain structures (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGroup Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient #\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender, Age (y.o.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTumor Location\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTRE (mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFRE (mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eComplete Tumor Resection\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNFM (Stimulation, mA)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTract Distance (Post-op MRI TG)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFunctional Outcome (MRC) Week 1 / Month 1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM,38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLeft Frontal Lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnaplastic Astrocytoma Grade 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,7\u003c/p\u003e \u003cp\u003e(σ\u0026thinsp;=\u0026thinsp;0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMono \u0026minus;\u0026thinsp;18 mA, Bi \u0026minus;\u0026thinsp;30 mA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9,3\u0026ndash;17,2 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5/5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF,41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRight Parietal Lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAstrocytoma Grade 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,4\u003c/p\u003e \u003cp\u003e(σ\u0026thinsp;=\u0026thinsp;0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSubtotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMono \u0026minus;\u0026thinsp;15 mA, Bi \u0026minus;\u0026thinsp;25 mA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5,3\u0026ndash;20,1 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5/5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM, 38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRight Frontal Lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAstrocytoma Grade 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,2\u003c/p\u003e \u003cp\u003e(σ\u0026thinsp;=\u0026thinsp;0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMono \u0026minus;\u0026thinsp;3 mA, Bi \u0026minus;\u0026thinsp;5 mA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3,2\u0026ndash;11,2 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4/5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM,49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLeft Frontal Lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnaplastic Astrocytoma Grade 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,5 (σ\u0026thinsp;=\u0026thinsp;0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMono \u0026minus;\u0026thinsp;5 mA, Bi \u0026minus;\u0026thinsp;7 mA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3,0\u0026ndash;7,5 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4/5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM,23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLeft Parietal Lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnaplastic Astrocytoma Grade 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,3\u003c/p\u003e \u003cp\u003e(σ\u0026thinsp;=\u0026thinsp;0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMono \u0026minus;\u0026thinsp;12 mA, Bi \u0026minus;\u0026thinsp;18 mA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2,4\u0026ndash;11,2 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5/5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFunctional outcomes were assessed at one week and one month postoperatively using the MRC scale. At the one-week mark, three patients retained full motor function (scored 5/5 on the MRC scale), while two patients exhibited moderate motor deficits (scored 4/5 on the MRC scale). By the end of the first month, all patients showed complete recovery of muscle strength, achieving 5/5 on the MRC scale, indicating favorable outcomes and the absence of long-term motor impairments.\u003c/p\u003e \u003cp\u003eNo intraoperative or early postoperative complications related to the use of AR navigation were recorded in any of the patients. In all cases, significant vessels and functionally critical structures, including the pyramidal tract and cortical veins, were successfully preserved without damage.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAugmented reality (AR) in neurosurgery is a promising tool that enables the real-time overlay of 3D neuroimaging data directly onto the surgical field. This eliminates the need for surgeons to shift their focus to monitors and mentally align 2D images with 3D anatomy, potentially reducing errors and enhancing precision (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). AR assists in visualizing complex tumors and their relationships with critical brain structures, improving spatial understanding of neuroanatomy and facilitating intraoperative orientation, which may ultimately improve surgical outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGliomas in the precentral area are among the most challenging neurosurgical pathologies due to the high risk of functional deficits following surgery. In most cases, postoperative neurological deficits are linked to damage to the corticospinal tract, the primary motor pathway that connects motor neurons in the precentral gyrus to motor neurons in the spinal cord (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Notably, only 40\u0026ndash;60% of the fibers in the pyramidal tract originate in the precentral gyrus, with the remainder primarily arising from neurons in the postcentral gyrus and premotor cortex (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIdentifying and preserving corticospinal tract fibers is a complex task that requires a broad range of techniques, from neurophysiological monitoring to intraoperative MRI(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Intraoperative neurophysiological monitoring plays a critical role in identifying motor areas on the cortical surface and motor pathways. A meta-analysis of 90 publications showed that motor monitoring reduces the incidence of permanent neurological deficits from 8.2\u0026ndash;3.4% and increases the extent of resection from 58\u0026ndash;75% (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Currently, two main techniques are used for intraoperative electrical stimulation of motor cortical areas and corticospinal tract fibers: 50-Hz stimulation, first described by Penfield in 1937, and high-frequency multipulse stimulation, described by Taniguchi in 1993(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our study, we proposed a method for tract visualization using AR capabilities, specifically intraoperative AR tractography based on preoperative MRI and DTI segmentation. Enhanced intraoperative spatial understanding of tract trajectories allows for optimal resection volume by correlating neurophysiological monitoring data with AR tractography during surgery. AR visualization of the tumor, corticospinal tract, and their relationships in cortical and subcortical areas can reduce the need for intraoperative brain electrical stimulations, potentially lowering the risk of intraoperative seizures or post-stimulation motor deficits. AR tractography enables selective visualization of tracts, allowing 3D tract models to be added or removed from the surgical field as needed, thus adapting the course of the operation. Additionally, the technology supports data updates to account for brain shift, integration with other AR techniques (e.g., fluorescent navigation), and use in awake surgeries.\u003c/p\u003e \u003cp\u003eFrom a technical standpoint, various studies have employed different approaches to image acquisition and processing, registration and tracking systems, and data visualization during surgery (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The lack of a unified standard for assessing the accuracy of virtual object registration poses challenges for precise neurosurgery and affects the reliability of AR for the localization and resection of intracranial targets. Additionally, most software and hardware solutions used are experimental and not widely available for surgical applications, necessitating caution in interpreting results.\u003c/p\u003e \u003cp\u003eIn our study, we used MRI tractography as one of the confirmatory methods. However, this method has significant limitations, as the results depend heavily on the operator and the chosen imaging algorithm (\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). A primary limiting factor in all intraoperative visualization techniques (except for intraoperative MRI and ultrasound) is the \"brain shift\" effect\u0026mdash;brain displacement during surgery. In our study, we addressed this issue by recalculating the model and re-registering points, adding new anchor points (e.g., veins or arteries) that move along with the displaced brain, which is particularly relevant in the final stages of surgery.\u003c/p\u003e \u003cp\u003eThe current limitations of AR tractography should also be noted. First, it requires knowledge of 3D segmentation and modeling fundamentals, as well as tractography skills, which may impact the quality of information obtained. The diffusion tractography (DTI) method, involving intra-voxel signal encoding, may lack precision. Another significant drawback is parallax error, inherent to all projection-based AR techniques, where the 3D model projection on a real object varies with changes in viewing angle. Using a QR code for model alignment helps mitigate this issue, but slight lag can occur with rapid movements of AR devices. The oversaturation of fiber tract visualizations can also pose a problem, obscuring important structures under layered images. To address this, we adjust model transparency and reduce the brightness of the displayed AR tract, enhancing the visibility of critical structures.\u003c/p\u003e \u003cp\u003eThe small number of patients prevents statistically significant conclusions; however, preliminary results indicate the potential benefits of the proposed method. One possible solution to the challenges of standardization and evidence accumulation is the creation of a consortium focused on standardizing AR workflows in neurosurgery, including the development of open-source software and methodologies. This would enable the accumulation of larger and more homogeneous datasets, facilitating the development and implementation of machine learning algorithms to improve surgical precision.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe application of AR navigation for the pyramidal tract in the microsurgical resection of brain tumors in the motor area demonstrates high accuracy and safety. This innovative approach can enhance the perception of pathological anatomy and improve the surgeon's orientation during surgery. AR tractography provides an additional measure of control over the location of the corticospinal tract when used alongside neurophysiological monitoring and may potentially reduce the risk of adverse functional outcomes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThis is a prospective single-center consecutive case series study conducted on patients with diffusely growing tumors located in the central gyrus area, for whom surgery with neurophysiological monitoring was planned. All operations were performed by the second author.\u003c/p\u003e\n\u003cp\u003eThe study took place at the Burdenko Neurosurgery Center in Moscow, from May to August 2024. The study protocol was reviewed and approved by the local ethics committee under protocol No. 2024-03 dated June 28, 2024. Written informed consent was obtained from all patients participating in the study, in accordance with the Declaration of Helsinki. This approval ensured full compliance with ethical standards, prioritizing patient safety and rights throughout the study.\u003c/p\u003e\n\u003cp\u003eThe study included five patients: four men and one woman, with a mean age of 37.8 years (ranging from 23 to 49 years). All patients presented with diffuse brain tumors located in functionally significant areas adjacent to the pyramidal tract. Three patients had tumors located in the frontal lobe (two on the left, one on the right), and two had tumors in the parietal lobe (one on the right, one on the left). In all cases, AR was used for intraoperative navigation in addition to neurophysiological monitoring. The inclusion criteria were:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Age between 18 and 65 years\u003c/p\u003e\n\u003cp\u003e\u0026middot; Confirmed diagnosis based on MRI findings\u003c/p\u003e\n\u003cp\u003e\u0026middot; Patient consent for surgery\u003c/p\u003e\n\u003cp\u003e\u0026middot; Absence of contraindications to surgery\u003c/p\u003e\n\u003cp\u003e\u0026middot; Completion of MRI tractography pre- and post-operatively\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Performance of head CT with thin slices and a QR frame to ensure AR navigation accuracy\u003c/p\u003e\n\u003cp\u003ePreoperative MRI was performed for each patient on a 3-Tesla scanner. All sequences were acquired with an isotropic voxel and a slice thickness not exceeding 1 mm, achieving a spatial resolution of at least 1x1x1 mm with a cubic matrix. The following sequences were used: T1-weighted images with contrast enhancement, T2-weighted images, T2-weighted FLAIR images (fluid-attenuated inversion recovery), and DTI (diffusion tensor imaging) tractography with a minimum of 20 vectors.\u003c/p\u003e\n\u003cp\u003eSegmentation and construction of a three-dimensional model of the tumor, pyramidal tract, and other necessary anatomical structures represent a critical step in preoperative planning. This step was performed using Inobitec DICOM Viewer. DTI (diffusion tensor imaging) data were used to construct the pyramidal tract, with each voxel providing directional diffusion information, enabling the visualization of individual nerve fibers. Based on these data, three-dimensional tract models were created in Inobitec DICOM Viewer. The method involved defining regions of interest (ROIs) to allow the software to automatically highlight and trace the direction of the pyramidal tract:\u003c/p\u003e\n\u003cp\u003e\u0026middot; The first ROI was set on the ipsilateral motor cortex where the pyramidal tract originates and where the tumor was located.\u003c/p\u003e\n\u003cp\u003e\u0026middot; The second ROI was positioned on the internal capsule region through which the main pyramidal tract fibers pass.\u003c/p\u003e\n\u003cp\u003e\u0026middot; The third ROI was located on the anterior surface of the medulla oblongata. This multi-level filtering allowed isolated visualization of key pyramidal fibers sufficient for surgical planning. During export, the software enabled adjustments to the thickness and density of the displayed fibers.\u003c/p\u003e\n\u003cp\u003eThe Microsoft HoloLens 2 system, equipped with Medgital software (medgital.org), was used for intraoperative navigation. AR allowed the surgeon to overlay 3D models of the pyramidal tract, tumor, and cortical veins onto the surgical field in real time, facilitating more detailed and practical planning of the surgical approach. To ensure AR navigation accuracy, model alignment with the patient was achieved using a QR code, followed by verification against predefined craniometric landmarks on the models, including the tip of the nose, lateral canthus of the eye, glabella, bregma, and stephanion (Figure 4). The accuracy of AR model alignment with the QR code was evaluated by calculating TRE (Target Registration Error\u0026mdash;measured as the average deviation between the projected model and the actual anatomical structures) and FRE (Fiducial Registration Error\u0026mdash;measured based on fixed anatomical landmarks), as outlined below.\u003c/p\u003e\n\u003cp\u003eEach patient also underwent a head CT scan with a QR code. The QR code\u0026apos;s position relative to the head was then transferred to the MRI by fusing (fusion) the CT and MRI images in the Inobitec DICOM Viewer software. Through the integration and co-registration of all MRI sequences using the layer fusion tool and the export of all segmented structures, a multi-component 3D model was created, incorporating segmented skin with the QR frame, skull, brain structures, tumor, vessels, and tracts. The final model, after simplification and necessary scaling, was saved in GLB format, compatible with the Microsoft HoloLens 2 augmented reality system running Medgital software. The average time to construct the final GLB model was 28.2 \u0026plusmn; 5.4 minutes.\u003c/p\u003e\n\u003cp\u003eBefore surgery, the QR frame was secured to the patient\u0026rsquo;s head in the same way as during the preoperative CT scan. The QR code served as a reference marker to ensure precise alignment of the virtual model with the patient\u0026rsquo;s physical anatomy (Figure 5A). During the procedure, the HoloLens 2 device recognized the QR code marker and aligned the 3D model relative to the patient\u0026rsquo;s head. The position of the 3D model was tracked and adjusted in real time throughout the use of the system.\u003c/p\u003e\n\u003cp\u003eThe AR technology enabled continuous display of the 3D models over the patient\u0026rsquo;s head during the entire surgery. In addition to AR, traditional neuronavigation methods were used; the frameless electromagnetic navigation system, Fiagon (Fiagon GmbH, Germany), provided additional guidance.\u003c/p\u003e\n\u003cp\u003eIn all cases, intraoperative neurophysiological monitoring (Nicolet Viking Select device; Natus) was employed to confirm the functional locations of the pyramidal tract and motor area. Stimulation was performed using both monopolar and bipolar stimulation throughout all stages of tumor microsurgical resection.\u003c/p\u003e\n\u003cp\u003eTo evaluate the alignment accuracy for AR navigation, the following metrics were used:\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eTarget Registration Error (TRE)\u003c/strong\u003e \u0026mdash; measured as the average deviation between the projected tumor model and the actual anatomical location.\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eFiducial Registration Error (FRE)\u003c/strong\u003e \u0026mdash; measured based on anatomical landmarks. For FRE calculation, we used the following craniometric points: tip of the nose, lateral canthus of the eye, glabella, bregma, and stephanion. Measurements were based on the displacement of superimposed 3D points relative to actual craniometric landmarks, recorded in pixels and then converted to millimeters.\u003c/p\u003e\n\u003cp\u003ePatient head tracking also employed a QR code mounted on a fixed frame on the patient\u0026rsquo;s head (Figure 5B).\u003c/p\u003e\n\u003cp\u003eThe accuracy of pyramidal tract navigation was assessed by comparing neurophysiological monitoring data, specifically the stimulation current intensity and direction, with the projected 3D AR model of the pyramidal tract. This approach is based on the established relationship between current intensity and distance to the corticospinal tract, defined by the formula 1mA = 1 mm. Thus, by obtaining the intraoperative current threshold that triggers a motor-evoked potential response, the distance from this point to the AR-projected tract can be compared(8\u0026ndash;10).\u003c/p\u003e\n\u003cp\u003eAll patients underwent motor function assessments before and after surgery. Muscle strength was evaluated using the Medical Research Council (MRC) scale, with assessments conducted one week and one month postoperatively. Neurological examination data and MRI scans were reviewed to assess the postoperative condition of the pyramidal tract and the extent of tumor resection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVolumentric analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn all cases the preoperative and postoperative MRI scans were obtained using high-resolution magnetic resonance imaging scanner (GE, Optima MR450w, Boston, Massachu- setts, USA).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe extent of resection (EOR) was assessed depending on MRI scans performed in the first 48 hours after surgery. The extent of tumor resection for non-contrast \u0026nbsp;tumors was estimated on T2 and FLAIR, for contrast enhancing tumors \u0026ndash; on contrast enhanced \u0026nbsp;T1 images by two independent \u0026nbsp;neuroradiologists. The EOR was calculated using the following formula[(tumor volume before surgery minus tumor volume after surgery) divided by tumor volume before surgical treatment].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSince this study is a case series aimed at establishing proof of concept, primary emphasis was placed on descriptive analysis. TRE and FRE values are presented as mean values \u0026plusmn; standard deviation (SD). The main focus was on evaluating the accuracy of the AR navigation method for tracts during surgery.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThe study was supported by the Ministry of Science and Higher Education of the Russian Federation under agreement No. 075-15-2024-561.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.N.K. (Anton Nikolaevich Konovalov) conceived the study, led the project, and was responsible for the manuscript\u0026rsquo;s main text and overall coordination. A.Y.B. (Andrey Yegorovich Bykanov) and D.N.O. (Dmitry Nikolaevich Okishev) contributed to data collection and processing, as well as drafted sections of the methodology. A.A.A. (Anton Alekseevich Artemyev) and A.V.K. (Alexander Viktorovich Knyazev) performed the data analysis and prepared figures 1-3. V.M.I. (Vladimir Mikhailovich Ivanov) and A.Y.S. (Anton Yurievich Smirnov) assisted with statistical analysis and data interpretation. S.V.S. (Sergey Vasilyevich Strelkov) and I.N.P. (Igor Nikolaevich Pronin) developed and implemented the augmented reality software. G.V.P. (Galina Valerievna Pavlova) and D.I.P. (David Ilyich Pithelauri) contributed to study design and patient recruitment. S.S.E. (Shalva Shalvovich Eliava) reviewed the manuscript for critical intellectual content. All authors reviewed and approved the final manuscript for submission.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors wish to thank the patients who participated in this study, as well as the clinical and technical staff at the N.N. Burdenko National Medical Research Center of Neurosurgery for their invaluable support in data collection and patient care. We extend our gratitude to the Institute for Bionic Technologies and Engineering at I.M. Sechenov First Moscow State Medical University for providing essential resources for this research. Special thanks go to the Medgital team for their assistance in developing the augmented reality software that made this study possible. We also acknowledge the support of Peter the Great St. Petersburg Polytechnic University in facilitating collaborative efforts across institutions. This work would not have been possible without the dedication and expertise of everyone involved.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKosyrkova AV, Goryainov SA, Ogurtsova AA, Okhlopkov VA, Kravchuk AD, Batalov AI, et al. Comparative analysis of mono- and bipolar pyramidal tract mapping in patients with supratentorial tumors adjacent to motor areas: comparison of data at 64 stimulation points. Voprosy neirokhirurgii imeni NN Burdenko. 2020;84(5):29. https://doi.org/10.17116/neiro20208405129\u003c/li\u003e\n \u003cli\u003eCatani M, Thiebaut de Schotten M. A diffusion tensor imaging tractography atlas for virtual in vivo dissections. Cortex. 2008;44(8):1105\u0026ndash;32.\u003c/li\u003e\n \u003cli\u003eDadario, Nicholas \u0026amp; Quinoa, Travis \u0026amp; Khatri, Deepak \u0026amp; Boockvar, John \u0026amp; Langer, David \u0026amp; D\u0026apos;Amico, Randy. (2021). Examining the benefits of extended reality in neurosurgery: A systematic review. Journal of Clinical Neuroscience. 94. 41-53. 10.1016/j.jocn.2021.09.037.\u003c/li\u003e\n \u003cli\u003eContreras L\u0026oacute;pez WO, Navarro PA, Crispin S. Intraoperative clinical application of augmented reality in neurosurgery: A systematic review. Vol. 177, Clinical Neurology and Neurosurgery. Elsevier B.V.; 2019. p. 6\u0026ndash;11.\u003c/li\u003e\n \u003cli\u003eMikhail M, Mithani K, Ibrahim GM. Presurgical and Intraoperative Augmented Reality in Neuro-Oncologic Surgery: Clinical Experiences and Limitations. Vol. 128, World Neurosurgery. Elsevier Inc.; 2019. p. 268\u0026ndash;76.\u003c/li\u003e\n \u003cli\u003eLuzzi S, Simoncelli A, Galzio R. Impact of augmented reality fiber tractography on the extent of resection and functional outcome of primary motor area tumors. Neurosurg Focus. 2024;56(1).\u003c/li\u003e\n \u003cli\u003eChidambaram S, Anthony D, Jansen T, Vigo V, Fernandez Miranda JC. Intraoperative augmented reality fiber tractography complements cortical and subcortical mapping. World Neurosurg X. 2023 Oct 1;20.\u003c/li\u003e\n \u003cli\u003eSeidel K, Beck J, Stieglitz L, Schucht P, Raabe A. Low-threshold monopolar motor mapping for resection of primary motor cortex tumors. Neurosurgery. 2012 Sep;71(SUPPL.1).\u003c/li\u003e\n \u003cli\u003eNossek E, Korn A, Shahar T, Kanner AA, Yaffe H, Marcovici D, et al. Intraoperative mapping and monitoring of the corticospinal tracts with neurophysiological assessment and 3-dimensional ultrasonography-based navigation: Clinical article. J Neurosurg. 2011 Mar;114(3):738\u0026ndash;46.\u003c/li\u003e\n \u003cli\u003eGonz\u0026aacute;lez-Darder JM, Gonz\u0026aacute;lez-L\u0026oacute;pez P, Talamantes F, Quilis V, Cort\u0026eacute;s V, Garc\u0026iacute;a-March G, et al. Multimodal navigation in the functional microsurgical resection of intrinsic brain tumors located in eloquent motor areas: Role of tractography. Neurosurg Focus. 2010;28(2).\u003c/li\u003e\n \u003cli\u003eIlle S, Ohlerth AK, Colle D, Colle H, Dragoy O, Goodden J, et al. Augmented reality for the virtual dissection of white matter pathways. Available from: https://doi.org/10.1007/s00701-020-04545-w\u003c/li\u003e\n \u003cli\u003eFraser C. Henderson and Kalil G Abdullah and Ragini Verma and Steven Brem Tractography and the connectome in neurosurgical treatment of gliomas.\u003c/li\u003e\n \u003cli\u003eDavidoff RA. The pyramidal tract. Vol. 40, views heviews NEUROLOGY. 1990.\u003c/li\u003e\n \u003cli\u003eEbeling U, Reulen HJ. Acta Neurochir (Wien) (1988) 92:2%36 :Acta. . Ndurochlrurglca Neurosurgical Topography of the Optic Radiation in the Temporal Lobe.\u003c/li\u003e\n \u003cli\u003eFeigl GC, Hiergeist W, Fellner C, Schebesch KMM, Doenitz C, Finkenzeller T, et al. Magnetic resonance imaging diffusion tensor tractography: Evaluation of anatomic accuracy of different fiber tracking software packages. Vol. 81, World Neurosurgery. 2014. p. 144\u0026ndash;50.\u003c/li\u003e\n \u003cli\u003eDe Witt Hamer PC, Robles SG, Zwinderman AH, Duffau H, Berger MS. Impact of intraoperative stimulation brain mapping on glioma surgery outcome: A meta-analysis. Vol. 30, Journal of Clinical Oncology. 2012. p. 2559\u0026ndash;65.\u003c/li\u003e\n \u003cli\u003eSzel\u0026eacute;nyi A, Senft C, Jardan M, Forster MT, Franz K, Seifert V, et al. Intra-operative subcortical electrical stimulation: A comparison of two methods. Clinical Neurophysiology. 2011 Jul;122(7):1470\u0026ndash;5.\u003c/li\u003e\n \u003cli\u003eLuzzi S. Impact of augmented reality fiber tractography on the extent of resection and functional outcome of primary motor area tumors.\u003c/li\u003e\n \u003cli\u003eFeigl GC, Decker K, Wurms M, Krischek B, Ritz R, Unertl K, et al. Neurosurgical procedures in the semisitting position: Evaluation of the risk of paradoxical venous air embolism in patients with a patent foramen ovale. Vol. 81, World Neurosurgery. 2014. p. 159\u0026ndash;64.\u003c/li\u003e\n \u003cli\u003ePujol S, Wells W, Pierpaoli C, Brun C, Gee J, Cheng G, et al. The DTI Challenge: Toward Standardized Evaluation of Diffusion Tensor Imaging Tractography for Neurosurgery. Journal of Neuroimaging. 2015 Nov 1;25(6):875\u0026ndash;82.\u003c/li\u003e\n \u003cli\u003eJbabdi S, Behrens TEJ, Smith SM. Crossing fibres in tract-based spatial statistics. Neuroimage. 2010 Jan 1;49(1):249\u0026ndash;56.\u003c/li\u003e\n \u003cli\u003eChung HW, Chou MC, Chen CY. Principles and limitations of computational algorithms in clinical diffusion tensor MR tractography. Vol. 32, American Journal of Neuroradiology. 2011. p. 3\u0026ndash;13.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"augmented reality, intraoperative navigation, tractography, diffuse glioma, corticospinal tract, microsurgical resection, neurophysiological monitoring","lastPublishedDoi":"10.21203/rs.3.rs-5444302/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5444302/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study explores the use of augmented reality (AR) for intraoperative guidance during the microsurgical resection of diffuse gliomas, especially those located near the critical corticospinal tract. AR provides surgeons with a three-dimensional view of essential brain structures in real time, overcoming the limitations of traditional navigation systems and potentially improving surgical precision. In our case series involving five patients, we combined AR-based visualization with neurophysiological monitoring, allowing precise mapping of the corticospinal tract relative to the tumor. This approach contributed to complete tumor removal in most cases, while also preserving motor function in all patients. Our findings suggest that AR technology can enhance spatial understanding during complex surgeries, minimizing the risk to critical neural pathways. While our initial results are promising, demonstrating reliable alignment accuracy and improved outcomes, further studies on larger patient groups are necessary to fully understand and validate AR\u0026rsquo;s role in neurosurgery. This research underscores AR\u0026rsquo;s potential to improve both safety and outcomes, adding valuable tools for intraoperative navigation.\u003c/p\u003e","manuscriptTitle":"Augmented Reality Intraoperative Tractography for diffuse glioma microsurgical resection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-11 14:18:43","doi":"10.21203/rs.3.rs-5444302/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7d382f8f-e7f7-4a7b-90f5-6dbd343bded0","owner":[],"postedDate":"December 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":41122476,"name":"Health sciences/Neurology/Neurological disorders"},{"id":41122477,"name":"Health sciences/Anatomy"},{"id":41122478,"name":"Health sciences/Medical research"},{"id":41122479,"name":"Health sciences/Neurology"},{"id":41122480,"name":"Physical sciences/Engineering"},{"id":41122481,"name":"Physical sciences/Nanoscience and technology"}],"tags":[],"updatedAt":"2025-03-28T04:53:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-11 14:18:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5444302","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5444302","identity":"rs-5444302","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}