Neurosurgery Planning Research Articles

Step-by-Step Implementation of Three-Dimensional Print Technology in Preoperative Neurosurgery Planning.

This study presents a detailed methodology for integrating three-dimensional (3D) printing technology into preoperative planning in neurosurgery. The increasing capabilities of 3D printing over the last decade have made it a valuable tool in medical fields such as orthopedics and dental practices. Neurosurgery can similarly benefit from these advancements, though the creation of accurate 3D models poses a significant challenge due to the technical expertise required and the cost of specialized software. This paper demonstrates a step-by-step process for developing a 3Dphysical model for preoperative planning using free, open-source software. A case involving a 62-year-old male with a large infiltrating tumor in the sacrum, originating from renal cell carcinoma, is used to illustrate the method. The process begins with the acquisition of a CT scan, followed by image reconstruction using InVesalius 3, an open-source software. The resulting 3D model is then processed in Autodesk Meshmixer (Autodesk, Inc., San Francisco, CA), where individual anatomical structures are segmented and prepared for printing. The model is printed using the Bambu Lab X1 Carbon 3D printer (Bambu Lab, Austin, TX), allowing for multicolor differentiation of structures such as bones, tumors, and blood vessels. The study highlights the practical aspects of model creation, including artifact removal, surface separation, and optimization for print volume. It discusses the advantages of multicolor printing for visual clarity in surgical planning and compares it with monochromatic and segmented printing approaches. The findings underscore the potential of 3D printing to enhance surgical precision and planning, providing a replicable protocol that leverages accessible technology. This work supports the broader adoption of 3D printing in neurosurgery, emphasizing the importance of collaboration between medical and engineering professionals to maximize the utility of these models in clinical practice.

Cerebrovascular segmentation in phase-contrast magnetic resonance angiography by a Radon projection composition network.

Cerebrovascular segmentation based on phase-contrast magnetic resonance angiography (PC-MRA) provides patient-specific intracranial vascular structures for neurosurgery planning. However, the vascular complex topology and spatial sparsity make the task challenging. Inspired by the computed tomography reconstruction, this paper proposes a Radon Projection Composition Network (RPC-Net) for cerebrovascular segmentation in PC-MRA, aiming to enhance distribution probability of vessels and fully obtain the vascular topological information. Multi-directional Radon projections of the images are introduced and a two-stream network is used to learn the features of the 3D images and projections. The projection domain features are remapped to the 3D image domain by filtered back-projection transform to obtain the image-projection joint features for predicting vessel voxels. A four-fold cross-validation experiment was performed on a local dataset containing 128 PC-MRA scans. The average Dice similarity coefficient, precision and recall of the RPC-Net achieved 86.12%, 85.91% and 86.50%, respectively, while the average completeness and validity of the vessel structure were 85.50% and 92.38%, respectively. The proposed method outperformed the existing methods, especially with significant improvement on the extraction of small and low-intensity vessels. Moreover, the applicability of the segmentation for electrode trajectory planning was also validated. The results demonstrate that the RPC-Net realizes an accurate and complete cerebrovascular segmentation and has potential applications in assisting neurosurgery preoperative planning.

The “Canopy Approach”: Case Series Using Immersive Virtual Reality for Bottom-Up Target-Based Preoperative Planning in Pediatric Neurosurgery

BACKGROUND: Virtual reality (VR) is increasingly used for trajectory planning in neurosurgery. OBJECTIVE: To describe a case series showing the application of immersive VR involving both “top-down” from skull to lesion and “bottom-up” from lesion to skull approaches for trajectory planning in pediatric neurosurgical patients. METHODS: We detail the preoperative and intraoperative application of VR and clinical courses of 5 children (aged 7-14 years) with anatomically challenging intraparenchymal lesions that posed operative risks to nearby vascular anatomy and fiber tracts. Preoperative planning consisted of standard presurgical evaluation with computed tomography and magnetic resonance imaging used to render 3-dimensional models that could be viewed and manipulated using desktop software and immersive VR headsets and hand controllers by the surgeon and family. Patient satisfaction was evaluated by survey. Surgical outcomes were degree of seizure control or extent of resection. RESULTS: Three patients underwent lesion resection and 2 laser ablation. Modifications to 2-dimensional and “top-down” VR trajectory plans were made after “bottom-up” navigation in all cases. All families reported that the VR enhanced their understanding of the procedure. There were no complications, and no patients suffered permanent neurological deficits postoperatively. Gross total resection was achieved in all lesional cases, and patients with epilepsy achieved seizure freedom at 2 years postoperatively. CONCLUSION: Immersive VR allows operative corridors to be virtually traveled and viewed from a “top-down” and “bottom-up” perspective, as if looking up from under a forest canopy of overlying anatomy, for optimal trajectory planning and improvement of family understanding in pediatric neurosurgery.

Dural Tail Sign and Middle Meningeal Artery Hypertrophy in Glioblastoma: A Rarity?

Dural tail sign and increased caliber of branches of the external carotid artery (ECA) are common findings in meningioma and they have been rarely reported in intra-axial lesions. Anyway, some cases of glioblastoma (GBM) are reported in the literature, mostly superficially localized, characterized by these 2 findings and therefore, misdiagnosed with meningioma. The aim of this study is to verify the prevalence of dural tail sign and hypertrophy of middle meningeal artery (MMA) in a large cohort of GBMs. 180 GBM patients were retrospectively evaluated. Deep or superficial localization of GBM was established and the presence of dural tail sign and hypertrophy of the ipsilateral MMA were assessed. The rate of tumor necrosis and the incidence of dural metastases during the radiological follow-up were also evaluated. Inter-rater reliability was calculated using Cohen's K-test. Dural tail sign and enlarged MMA were evident in 30% and 19% of 96 superficial GBM, respectively. Deep GBM did not present those signs. Only one patient developed dural metastasis at follow-up and no differences in terms of tumor necrosis and hypoxic biomarkers expression were evident among GBMs with and without dural and vessel signs. Dural tail sign and hypertrophy of the MMA in superficial GBM are more common than expected. They probably represent reactive rather than a neoplastic infiltration. Knowing these radiological signs may be important in terms of neurosurgery planning and avoiding excessive bleeding. Anyway, this hypothesis should be confirmed by a prospective neurosurgery studio.

Low-cost and open-source three-dimensional (3D) printing in neurosurgery: A pilot experiment using direct drive modification to produce multi-material neuroanatomical models

Background3-dimensional (3D) printing carries a genuine potential for pre-operative planning in neurosurgery. Entry-level 3D printers offer practicality in low resource settings, but are often limited by the range of filament materials as well as the capability of open-source segmentation software. ObjectiveWe intended to demonstrate that 3D printing of neuroanatomical structures is possible using an entry-level 3D printer equipped with the direct drive (DD) modification, which supported flexible filaments, with the models segmented using an open-source software. MethodsA DD system was installed onto the Ender 3 Pro printer. An attempt to print neurosurgical models using a low-cost 3D printer was conducted, where four patient-based neuroanatomical models were printed: skull base-vasculature, skull base-tumour, cervical spine, and ventricular system. The results were discussed and compared to similar endeavours in past literature. ResultsAlthough DD installation was challenging and led to vibration and longer print time, which ultimately warranted an inferior printing speed, DD system enabled the printing with thermoplastic polyurethane (TPU), a versatile elastomer; in addition to producing equal amount of detail to those printed with high-end printers and advanced image segmentation software. Fitting the frame well, changing infill type, and avoiding warping and stringing will improve print quality with the DD system. Conclusion3D printing with entry-level 3D printers equipped with DD system has been proven to be a reliable way of accurately reproducing patient-specific neuroanatomical constructs. Follow-up studies are necessary to implement 3D printing for neurosurgical planning in low-resource settings.

Preoperative planning of patients with pituitary tumors (adenomas) according to neuroimaging data

The task of any neurosurgery for a tumor removal is the safest possible resection with minimal complications. For its implementation, it is of great importance to carry out preoperative planning using modern neuroimaging methods (MRI, MSCT, PET). It includes imaging of topographic and anatomical features of the area of interest, which may vary depending on the direction of tumor growth. Neuroimaging data allows assessing the properties of the tumor (size, spread, density, degree of vascularization) in a particular patient at the preoperative stage. For the procedure of neuro-radiosurgery treatment, a dosimetric plan is additionally created, which should be as selective and conformal as possible, taking into account the critical structures located nearby. Aim of the study. Based on our own research and literature data, to analyze and demonstrate the most informative neuroimaging methods when planning neurosurgery and neuro-radiosurgery interventions that will ensure safe and, if possible, radical treatment of pituitary adenomas. Materials and methods. The analysis included 29 foreign and 9 Russian scientific sources devoted to the study of the possibilities of MRI, CT, and PET in the diagnosis of pituitary adenomas, from the perspective of modern neuroimaging capabilities. The work is illustrated with images of the studied area obtained during the planning of neurosurgery or neuro-radiosurgery treatment. The analysis of our own observations based on neuroimaging data in 280 patients was carried out. Results. Based on the analysis of literary sources, the modern possibilities of neuroimaging in the planning of neurosurgeries and neuro-radiosurgeries are demonstrated. Optimal neuroimaging techniques have been determined to assess the anatomical features of the sellar region and the properties of the tumor, allowing to increase the effectiveness of preoperative planning in each specific case.

Graph Theory Measures and Their Application to Neurosurgical Eloquence.

Improving patient safety and preserving eloquent brain are crucial in neurosurgery. Since there is significant clinical variability in post-operative lesions suffered by patients who undergo surgery in the same areas deemed compensable, there is an unknown degree of inter-individual variability in brain 'eloquence'. Advances in connectomic mapping efforts through diffusion tractography allow for utilization of non-invasive imaging and statistical modeling to graphically represent the brain. Extending the definition of brain eloquence to graph theory measures of hubness and centrality may help to improve our understanding of individual variability in brain eloquence and lesion responses. While functional deficits cannot be immediately determined intra-operatively, there has been potential shown by emerging technologies in mapping of hub nodes as an add-on to existing surgical navigation modalities to improve individual surgical outcomes. This review aims to outline and review current research surrounding novel graph theoretical concepts of hubness, centrality, and eloquence and specifically its relevance to brain mapping for pre-operative planning and intra-operative navigation in neurosurgery.

The use of advanced technology for preoperative planning in cranial surgery – A survey by the EANS Young Neurosurgeons Committee

IntroductionTechnological advancements provided several preoperative tools allowing for precise preoperative planning in cranial neurosurgery, aiming to increase the efficacy and safety of surgery. However, little data are available regarding if and how young neurosurgeons are trained in using such technologies, how often they use them in clinical practice, and how valuable they consider these technologies. Research questionHow frequently these technologies are used during training and clinical practice as well as to how their perceived value can be qualitatively assessed. Materials and methodsThe Young Neurosurgeons’ Committee (YNC) of the European Association of Neurosurgical Societies (EANS) distributed a 14-items survey among young neurosurgeons between June 1st and August 31st, 2022. ResultsA total of 441 responses were collected. Most responders (42.34%) received “formal” training during their residency. Planning techniques were used mainly in neuro-oncology (90.86%), and 3D visualization of patients' DICOM dataset using open-source software was the most frequently used (>20 times/month, 20.34% of responders). Software for 3D visualization of patients’ DICOM dataset was the most valuable technology, especially for planning surgical approach (42.03%). Conversely, simulation based on augmented/mixed/virtual reality was considered the less valuable tool, being rated below sufficiency by 39.7% of responders. Discussion and conclusionTraining for using preoperative planning technologies in cranial neurosurgery is provided by neurosurgical residency programs. Software for 3D visualization of DICOM datasets is the most valuable and used tool, especially in neuro-oncology. Interestingly, simulation tools based on augmented/virtual/mixed reality are considered less valuable and, therefore, less used than other technologies.

Magnetic resonance imaging based neurosurgical planning on hololens 2: A feasibility study in a paediatric hospital.

The goal of this work is to show how to implement a mixed reality application (app) for neurosurgery planning based on neuroimaging data, highlighting the strengths and weaknesses of its design. Our workflow explains how to handle neuroimaging data, including how to load morphological, functional and diffusion tensor imaging data into a mixed reality environment, thus creating a first guide of this kind. Brain magnetic resonance imaging data from a paediatric patient were acquired using a 3 T Siemens Magnetom Skyra scanner. Initially, this raw data underwent specific software pre-processing and were subsequently transformed to ensure seamless integration with the mixed reality app. After that, we created three-dimensional models of brain structures and the mixed reality environment using Unity™ engine together with Microsoft® HoloLens 2™ device. To get an evaluation of the app we submitted a questionnaire to four neurosurgeons. To collect data concerning the performance of a user session we used Unity Performance Profiler. The use of the interactive features, such as rotating, scaling and moving models and browsing through menus, provided by the app had high scores in the questionnaire, and their use can still be improved as suggested by the performance data collected. The questionnaire's average scores were high, so the overall experiences of using our mixed reality app were positive. We have successfully created a valuable and easy-to-use neuroimaging data mixed reality app, laying the foundation for more future clinical uses, as more models and data derived from various biomedical images can be imported.

Virtual interview training for neurosurgery applicants

Dear Editor, Entry into neurosurgical training in the UK is amongst the most competitive applications across all medical and surgical specialties. Entry points at ST1 and ST2 levels saw competition ratios of 16.13 and 59.00 in 2021 [1], respectively. In comparison, ratios for Core Surgical Training and Internal Medicine Training were 4.16 and 2.29, respectively. In addition, given the ongoing difficulties with workforce planning in Neurosurgery, the number of available posts have reduced dramatically over the last few years from 34 in 2018 to 15 in 2022. Despite increasing levels of competition, there is a paucity of formal interview guidance for prospective applicants at the undergraduate and early postgraduate stages. The result is a mandatory reliance on direct contact with the neurosurgical community, which is not always possible due to geographical constraints. To address this, and the additional complexity introduced by the advent of virtual interviews, we designed the first nationally run virtual interview course for neurosurgery applicants. The course consisted of morning and afternoon segments. The morning involved three didactic lecture sessions on the clinical, management, and portfolio stations. The afternoon consisted of three 1:1 interviews between applicants and faculty. Each station lasted 15 min with a 5-min break to mimic national selection standards. Each candidate was provided with a pre- and post-course survey questionnaire to identify further details regarding demographics, training grade, confidence, and perceived knowledge of common neurosurgical conditions. A total of 25 candidates attended the course, of which 44% were female; an encouraging figure when compared to the proportion of female consultants in the UK (3%) [2]. Gender parity needs to be encouraged by optimising course advertisement, course timings, and course content [3]. Most candidates were Foundation Year 2 level or senior (80%). 60% of participants did not have a neurosurgical centre attached to their medical school or base hospital. Overall, post-course feedback reported substantial benefit, with improvement in understanding of interview structure, confidence in approaching clinical and management scenarios, and appreciation of pertinent aspects of leadership and management within the NHS. Importantly, perceived confidence with interviews via virtual methods was very high. With national selection entry further constrained in 2022, there is increasing pressure to ensure a fair and transparent process for prospective applicants. Our course encouraged relatively high female participation and geographically democratised access to national selection preparation material. Furthermore, the curriculum encouraged wider knowledge of the NHS, and improved candidates understanding of neurosurgery as a speciality. Most importantly, the mock interviews displayed the candidate's strengths and weaknesses, thereby encouraging reflective practice. With the guided practice our course offered, we aim to increase the preparedness of candidates for the UK national selection process. Furthermore, we hope that this enhances the interview experience for both interviewers and interviewees alike. We aim to run annual courses, either face-to-face or virtual, with the help of the neurosurgical trainees across the UK to continue providing a point of contact and support for applicants. Funding No funding was sought for this study. Declaration of competing interest The authors report there are no competing interests to declare.

P158 - Geometric accuracy and precision of cone beam CT system in angiography used for radiosurgery and neurosurgery planning

P158 - Geometric accuracy and precision of cone beam CT system in angiography used for radiosurgery and neurosurgery planning

Performance improvement of weakly supervised fully convolutional networks by skip connections for brain structure segmentation.

For the planning and navigation of neurosurgery, we have developed a fully convolutional network (FCN)-based method for brain structure segmentation on magnetic resonance (MR) images. The capability of an FCN depends on the quality of the training data (i.e., raw data and annotation data) and network architectures. The improvement of annotation quality is a significant concern because it requires much labor for labeling organ regions. To address this problem, we focus on skip connection architectures and reveal which skip connections are effective for training FCNs using sparsely annotated brain images. We tested 2D FCN architectures with four different types of skip connections. The first was a U-Net architecture with horizontal skip connections that transfer feature maps at the same scale from the encoder to the decoder. The second was a U-Net++ architecture with dense convolution layers and dense horizontal skip connections. The third was a full-resolution residual network (FRRN) architecture with vertical skip connections that pass feature maps between each downsampled scale path and the full-resolution scale path. The last one was a hybrid architecture with a combination of horizontal and vertical skip connections. We validated the effect of skip connections on medical image segmentation from sparse annotation based on these four FCN architectures, which were trained under the same conditions. For multiclass segmentation of the cerebrum, cerebellum, brainstem, and blood vessels from sparsely annotated MR images, we performed a comparative evaluation of segmentation performance among the above four FCN approaches: U-Net, U-Net++, FRRN, and hybrid architectures. The experimental results show that the horizontal skip connections in the U-Net architectures were effective for the segmentation of larger sized objects, whereas the vertical skip connections in the FRRN architecture improved the segmentation of smaller sized objects. The hybrid architecture with both horizontal and vertical skip connections achieved the best results of the four FCN architectures. We then performed an ablation study to explore which skip connections in the FRRN architecture contributed to the improved segmentation of blood vessels. In the ablation study, we compared the segmentation performance between architectures with a horizontal path (HP), an HP and vertical up paths (HP+VUPs), an HP and vertical down paths (HP+VDPs), and an HP and vertical up and down paths (FRRN). We found that the vertical up paths were effective in improving the segmentation of smaller sized objects. This paper investigated which skip connection architectures were effective for multiclass brain segmentation from sparse annotation. Consequently, using vertical skip connections with horizontal skip connections allowed FCNs to improve segmentation performance.

Neurosurgery planning based on automated image recognition and optimal path design

Abstract Stereotactic neurosurgery requires a careful planning of cannulae paths to spare eloquent areas of the brain that, if damaged, will result in loss of essential neurological function such as sensory processing, linguistic ability, vision, or motor function. We present an approach based on modelling, simulation, and optimization to set up a computational assistant tool. Thereby, we focus on the modeling of the brain topology, where we construct ellipsoidal approximations of voxel clouds based on processed MRI data. The outcome is integrated in a path-planning problem either via constraints or by penalization terms in the objective function. The surgical planning problem with obstacle avoidance is solved for different types of stereotactic cannulae using numerical simulations. We illustrate our method with a case study using real MRI data.

Augmented reality head-mounted display-based incision planning in cranial neurosurgery: a prospective pilot study.

Monitor and wand-based neuronavigation stations (MWBNSs) for frameless intraoperative neuronavigation are routinely used in cranial neurosurgery. However, they are temporally and spatially cumbersome; the OR must be arranged around the MWBNS, at least one hand must be used to manipulate the MWBNS wand (interrupting a bimanual surgical technique), and the surgical workflow is interrupted as the surgeon stops to "check the navigation" on a remote monitor. Thus, there is need for continuous, real-time, hands-free, neuronavigation solutions. Augmented reality (AR) is poised to streamline these issues. The authors present the first reported prospective pilot study investigating the feasibility of using the OpenSight application with an AR head-mounted display to map out the borders of tumors in patients undergoing elective craniotomy for tumor resection, and to compare the degree of correspondence with MWBNS tracing. Eleven consecutive patients undergoing elective craniotomy for brain tumor resection were prospectively identified and underwent circumferential tumor border tracing at the time of incision planning by a surgeon wearing HoloLens AR glasses running the commercially available OpenSight application registered to the patient and preoperative MRI. Then, the same patient underwent circumferential tumor border tracing using the StealthStation S8 MWBNS. Postoperatively, both tumor border tracings were compared by two blinded board-certified neurosurgeons and rated as having an excellent, adequate, or poor correspondence degree based on a subjective sense of the overlap. Objective overlap area measurements were also determined. Eleven patients undergoing craniotomy were included in the study. Five patient procedures were rated as having an excellent correspondence degree, 5 had an adequate correspondence degree, and 1 had poor correspondence. Both raters agreed on the rating in all cases. AR tracing was possible in all cases. In this small pilot study, the authors found that AR was implementable in the workflow of a neurosurgery OR, and was a feasible method of preoperative tumor border identification for incision planning. Future studies are needed to identify strategies to improve and optimize AR accuracy.

Risk assessment methodology for trajectory planning in keyhole neurosurgery using genetic algorithms.

Preoperative assessment to find the safest trajectory in keyhole neurosurgery can reduce post operative complications. We introduced a novel preoperative risk assessment semiautomated methodology based on the sum of N maximum risk values using a generic genetic algorithm for the safest trajectory search. A set of candidates trajectories were found for two surgical procedures. The trajectories search is done using a risk map considering the proximity of voxels within risk structures in multiple points and a genetic algorithm to avoid an exhaustive search. The trajectories were validated by a group of neurosurgeons. The trajectories obtained with the proposal method were shorter in 5% and have greater distance from the voxels within the blood vessels in 4.7%. The use of genetic algorithm (GA) speeds up the search for the safest trajectory, decreasing in 99.9% the time required for an exhaustive search.

Hybrid computed tomography and magnetic resonance imaging 3D printed models for neurosurgery planning.

In the last decade, the clinical applications of three-dimensional (3D) printed models, in the neurosurgery field among others, have expanded widely based on several technical improvements in 3D printers, an increased variety of materials, but especially in postprocessing software. More commonly, physical models are obtained from a unique imaging technique with potential utilization in presurgical planning, generation/creation of patient-specific surgical material and personalized prosthesis or implants. Using specific software solutions, it is possible to obtain a more accurate segmentation of different anatomical and pathological structures and a more precise registration between different medical image sources allowing generating hybrid computed tomography (CT) and magnetic resonance imaging (MRI) 3D printed models. The need of neurosurgeons for a better understanding of the complex anatomy of central nervous system (CNS) and spine is pushing the use of these hybrid models, which are able to combine morphological information from CT and MRI, and also able to add physiological data from advanced MRI sequences, such as diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion weighted imaging (PWI) and functional MRI (fMRI). The inclusion of physiopathological data from advanced MRI sequences enables neurosurgeons to identify those areas with increased biological aggressiveness within a certain lesion prior to surgery or biopsy procedures. Preliminary data support the use of this more accurate presurgical perspective, to select the better surgical approach, reduce the global length of surgery and minimize the rate of intraoperative complications, morbidities or patient recovery times after surgery. The use of 3D printed models in neurosurgery has also demonstrated to be a valid tool for surgeons training and to improve communication between specialists and patients. Further studies are needed to test the feasibility of this novel approach in common clinical practice and determine the degree of improvement the neurosurgeons receive and the potential impact on patient outcome.

Three-Dimensional Volume Rendering: An Underutilized Tool in Neurosurgery

Two-dimensional radiologic images of computed tomography (CT) and magnetic resonance imaging (MRI) examinations are insufficient to provide three-dimensional conception of intracranial or spinal lesions, especially for an inexperienced neurosurgeon. We share our experience of applications of volume rendering (VR) images of CT and MRI studies of brain and spine for the purpose of training and surgical planning in neurosurgery. Digital Imaging and Communications in Medicine viewer software was used on a personal computer/laptop to view CT and MRI examinations of various lesions of brain, spine, and spinal cord. Various user-friendly applications of the software enable users to edit the images acquired. Imaging technique (CT and MRI) incorporating continuous or overlapping slice with slice thickness of 1 mm with zero gap provides adequate three-dimensional image quality on personal computer/laptop acquired by VR. It was especially helpful for assessment of lesions of the skull base, intracranial vessels (aneurysm and arteriovenous malformation), craniovertebral junction and contrast-enhancing lesions of the brain. Displaying VR images in the operating room as per the patient's head position orients the surgeon to identify various structures in the surgical field. VR is underused in neurosurgery. It enables inexperienced neurosurgeons to understand morphologic details of a lesion and important osteoneurovascular structures around it. It is also helpful for training and preoperative planning in selected patients in neurosurgery.

Construction of Neuroanatomical Volumetric Models Using 3-Dimensional Scanning Techniques: Technical Note and Applications

Visuospatial features of neuroanatomy are likely the most difficult concepts to learn in anatomy. Three-dimensional (3D) modalities have gradually begun to supplement traditional 2-dimensionanl representations of dissections and illustrations. We have introduced and described the workflow of 2 innovative methods-photogrammetry (PGM) and structured light scanning (SLS)-which have typically been used for reverse-engineering applications. In the present study, we have described a novel application of SLS and PGM that could enhance medical education and operative planning in neurosurgery. We have described the workflow of SLS and PGM for creating volumetric models (VMs) of neuroanatomical dissections, including the requisite equipment and software. We have also provided step-by-step procedures on how users can postprocess and refine these images according to their specifications. Finally, we applied both methods to 3 dissected hemispheres to demonstrate the quality of the VMs and their applications. Both methods yielded VMs with suitable clarity and structural integrity for anatomical education, surgical illustration, and procedural simulation. The application of 3D computer graphics to neurosurgical applications has shown great promise. SLS and PGM can facilitate the construction of VMs with high accuracy and quality that can be used and shared in a variety of 3D platforms. Similarly, the technical demands are not high; thus, it is plausible that neurosurgeons could become quickly proficient and enlist their use in education and surgical planning. Although SLS is preferable in settings in which high accuracy is required, PGM is a viable alternative with a short learning curve.

Reform and reflection on the cultivation of postgraduates in neurosurgery in "5+3" training plan

At present, the standardized training of neurosurgery resident is in the primary stage in our country, and the master's degree postgraduates of Neurosurgery major are the main part of the students of neurosurgery planning training in our department. At present, a systematic and sound training system and teaching mechanism hasn’t been formed in the standardized training of postgraduates in Neurosurgery. It is imperative to explore the training mode of postgraduates majoring in neurosurgery to keep pace with the times and meet the needs of the society. Based on the problems in the current training mode of neurosurgery in the First Affiliated Hospital of Heavy Medicine and combined with the existing problems, this paper focuses on the cultivation of professional basic theoretical knowledge, clinical practice ability, clinical thinking and innovative ability and aims to establish professional assessment team, so as to boost the education development of postgraduates with neurosurgery degree and improve the quality of standardized training. Key words: Neurosurgery; Professional degree graduate student; Standardized training of residents; Reform

Planning Method for Safety Neurosurgical and Computed Tomography Contrast-Data Set Visualization

Discussed the issues' associated with the development of a computed neurosurgery planning system. An important part is to determine the value of invasive surgical access. The study purpose is to design a methodology for finding the shortest distance between surgical target and peripheral point of the brain tissue with strict adherence considering the type of the brain anatomical structure existing in the path of surgical track (risk map), these two condition used in companion to determine the risk value of the surgical access. The study method consists of two algorithms for calculating the shortest surgical access to the target and assuring the safety by avoiding high-density tissues identification method “internal map” describing the anatomy of the brain such as bones. An algorithm for automatic identification of brain vascular system also was designed. The structural diagram of the contrast data visualization system, using computed tomography data, was thoroughly discussed. Also, trying to contribute in solving issues facing developers of modern medical imaging visualization systems to select the most appropriate method from the whole arsenal of algorithms and processing models concerning displaying brain surgical zone using image registration and optical tracking system. The visualization of the target zone is carried out according to an internal reference landmark points inside the center of the brain as well as an automatic algorithm for contour recognition was applied. Moreover, the optical tracking system was used to assess the navigation accuracy of determining the position of the surgical instrument outside the patient head. Algorithms necessary for operational planning also was included, and the proposed method was applied in a pilot study with simulation mode to human brain model, in order to target a specific surgical zone, and as a result, the system suggested (24) possible surgical track, among them, were selected the best and safest access. The total error of a surgical instrument targeting was less than 3 mm (in average 2.6 mm).