Image-Guided Neurosurgery: History and Current Clinical Applications

How to objectively evaluate the impact of image-guided surgery technologies

PURPOSE

This manuscript aims to provide a better understanding of methods and techniques with which one can better quantify the impact of image-guided surgical technologies.

METHODS

A literature review was conducted with regard to economic and technical methods of medical device evaluation in various countries. Attention was focused on applications related to image-guided interventions that have enabled procedures to be performed in a minimally invasive manner, produced superior clinical outcomes, or have become standard of care.

RESULTS

The review provides examples of successful implementations and adoption of image-guided surgical techniques, mostly in the field of neurosurgery. Failures as well as newly developed technologies still undergoing cost-efficacy analysis are discussed.

CONCLUSION

The field of image-guided surgery has evolved from solely using preoperative images to utilizing highly specific tools and software to provide more information to the interventionalist in real time. While deformations in soft tissue often preclude the use of such instruments outside of neurosurgery, recent developments in optical and radioactive guidance have enabled surgeons to better account for organ motion and provide feedback to the surgeon as tissue is cut. These technologies are currently undergoing value assessments in many countries and hold promise to improve outcomes for patients, surgeons, care teams, payors, and society in general.

Charting the success of neuronavigation in brain tumor surgery: from inception to adoption and evolution

PURPOSE

Neuronavigation, explored as an intra-operative adjunct for brain tumor surgery three decades ago, has become globally utilized with a promising upward trajectory. This study aims to chart its success from idea to adoption and evolution within the US and globally.

METHODS

A three-pronged methodology included a systematic literature search, impact analysis using NIH relative citation ratio (RCR) and Altmetric scores, and assessment of patent holdings. Data was dichotomized for US and international contexts.

RESULTS

The first neuronavigation publication stemmed from Finland in 1993, marking its inception. Over three decades, the cumulative number of 323 studies, along with the significantly increasing publication trend (r = 0.74, p < 0.05) and distribution across 34 countries, underscored its progressive and global adoption. Neuronavigation, mostly optical systems (58%), was utilized in over 19,000 cases, predominantly for brain tumor surgery (84%). Literature impact showed a robust cumulative median RCR score surpassing that for NIH-funded studies (1.37 vs. 1.0), with US studies having a significantly higher median RCR than international (1.71 vs. 1.21, p < 0.05). Technological evolution was characterized by adjuncts, including micro/exo/endoscope (21%), MRI (17%), ultrasound (10%), and CT (7%). Patent analysis demonstrated academic and industrial representation with an interdisciplinary convergence of medical and computational sciences.

CONCLUSION

Since its inception thirty years ago, neuronavigation has been adopted worldwide, and it has evolved with adjunct technology integration to enhance its meaningful use. The current neuronavigation innovation pipeline is progressing, with academic and industry partnering to advance its further application in treating brain tumor patients.

Bony Surface-Matching Registration of Neuronavigation with Sectioned 3-Dimensional Skull in Prone Position

BACKGROUND

Neuronavigation has become an essential system for brain tumor resections. It is sometimes difficult to obtain accurate registration of the neuronavigation with the patient in the prone position. Bony surface-matching registration should be more precise than skin surface-matching registration; however, it is difficult to establish bony registration with limited exposed bone. We created a new bony surface-matching method to a sectioned 3-dimensional (3D) virtual skull in a neuronavigation system and registered with a sectioned 3D skull. In this study, the bony surface-matching with sectioned 3D registration is applied to provide precise registration for brain tumor resection in the prone position.

METHODS

From May 2023 to April 2024, 17 patients who underwent brain tumor resection in the prone position were enrolled. The navigation system StealthStation S8 (Medtronic, Dublin, Ireland) was used. Bony surface-matching registration with a whole 3D skull in a neuronavigation system was performed. Next, a sectioned 3D skull was made according to the surgical location to compare with the whole 3D skull registration. A phantom model was also used to validate the whole and sectioned 3D skull registration.

RESULTS

Whole 3D skull registration was successful for only 2 patients (11.8%). However, sectioned 3D skull registration was successful for 16 patients (94.1%). The examinations with a phantom skull model also showed superiority of sectioned 3D skull registration to whole 3D skull registration.

CONCLUSIONS

Sectioned 3D skull registration was superior to whole 3D skull registration. The sectioned 3D skull method could provide accurate registration with limited exposed bone.

A deep learning classification task for brain navigation in rodents using micro-Doppler ultrasound imaging

Positioning and navigation are essential components of neuroimaging as they improve the quality and reliability of data acquisition, leading to advances in diagnosis, treatment outcomes, and fundamental understanding of the brain. Functional ultrasound imaging is an emerging technology providing high-resolution images of the brain vasculature, allowing for the monitoring of brain activity. However, as the technology is relatively new, there is no standardized tool for inferring the position in the brain from the vascular images. In this study, we present a deep learning-based framework designed to address this challenge. Our approach uses an image classification task coupled with a regression on the resulting probabilities to determine the position of a single image. To evaluate its performance, we conducted experiments using a dataset of 51 rat brain scans. The training positions were extracted at intervals of 375 μm, resulting in a positioning error of 176 μm. Further GradCAM analysis revealed that the predictions were primarily driven by subcortical vascular structures. Finally, we assessed the robustness of our method in a cortical stroke where the brain vasculature is severely impaired. Remarkably, no specific increase in the number of misclassifications was observed, confirming the method's reliability in challenging conditions. Overall, our framework provides accurate and flexible positioning, not relying on a pre-registered reference but rather on conserved vascular patterns.

Extended-wavelength diffuse reflectance spectroscopy dataset of animal tissues for bone-related biomedical applications

Diffuse reflectance spectroscopy (DRS) has been extensively studied in both preclinical and clinical settings for multiple applications, notably as a minimally invasive diagnostic tool for tissue identification and disease delineation. In this study, extended-wavelength DRS (EWDRS) measurements of ex vivo tissues ranging from ultraviolet through visible to the short-wave infrared region (355-1919 nm) are presented in two datasets. The first dataset contains labelled EWDRS measurements collected from bone cement samples and ovine specimens including 10 tissue types commonly encountered in orthopedic surgeries for data curation purposes. The other dataset includes labelled EWDRS measurements of primarily bone structures at different depths during stepwise drilling into intact porcine skulls until plunging into the cranial cavity. The raw data with code for pre-processing and calibration is publicly available for reuse on figshare. The datasets can be utilized not only for exploratory purposes in machine learning model construction, but also for knowledge discovery in the orthopedic domain to identify important features for surgical guidance, extract physiological parameters and provide diagnostic insights.

Assessing the benefits of digital twins in neurosurgery: a systematic review

Digital twins are virtual replicas of their physical counterparts, and can assist in delivering personalized surgical care. This PRISMA guideline-based systematic review evaluates current literature addressing the effectiveness and role of digital twins in many stages of neurosurgical management. The aim of this review is to provide a high-quality analysis of relevant, randomized controlled trials and observational studies addressing the neurosurgical applicability of a variety of digital twin technologies. Using pre-specified criteria, we evaluated 25 randomized controlled trials and observational studies on the applications of digital twins, including navigation, robotics, and image-guided neurosurgeries. All 25 studies compared these technologies against usual surgical approaches. Risk of bias analyses using the Cochrane risk of bias tool for randomized trials (Rob 2) found "low" risk of bias in the majority of studies (23/25). Overall, this systematic review shows that digital twin applications have the potential to be more effective than conventional neurosurgical approaches when applied to brain and spinal surgery. Moreover, the application of these novel technologies may also lead to fewer post-operative complications.

Navigation-Assisted Surgery for Locally Advanced Primary and Recurrent Rectal Cancer

BACKGROUND

In some surgical disciplines, navigation-assisted surgery has become standard of care, but in rectal cancer, indications for navigation and the utility of different technologies remain undetermined.

METHODS

The NAVI-LARRC prospective study (NCT04512937; IDEAL Stage 2a) evaluated feasibility of navigation in patients with locally advanced primary (LARC) and recurrent rectal cancer (LRRC). Included patients had advanced tumours with high risk of incomplete (R1/R2) resection, and navigation was considered likely to improve the probability of complete resection (R0). Tumours were classified according to pelvic compartmental involvement, as suggested by the Royal Marsden group. The BrainlabTM navigation platform was used for preoperative segmentation of tumour and pelvic anatomy, and for intraoperative navigation with optical tracking. R0 resection rates, surgeons' experiences, and adherence to the preoperative resection plan were assessed.

RESULTS

Seventeen patients with tumours involving the posterior/lateral compartments underwent navigation-assisted procedures. Fifteen patients required abdominosacral resection, and 3 had resection of the sciatic nerve. R0 resection was obtained in 6/8 (75%) LARC and 6/9 (69%) LRRC cases. Preoperative segmentation was time-consuming (median 3.5 h), but intraoperative navigation was accurate. Surgeons reported navigation to be feasible, and adherence to the resection plan was satisfactory.

CONCLUSIONS

Navigation-assisted surgery using optical tracking was feasible. The preoperative planning was time-consuming, but intraoperative navigation was accurate and resulted in acceptable R0 resection rates. Selected patients are likely to benefit from navigation-assisted surgery.

Combining physics-based models with deep learning image synthesis and uncertainty in intraoperative cone-beam CT of the brain

BACKGROUND

Image-guided neurosurgery requires high localization and registration accuracy to enable effective treatment and avoid complications. However, accurate neuronavigation based on preoperative magnetic resonance (MR) or computed tomography (CT) images is challenged by brain deformation occurring during the surgical intervention.

PURPOSE

To facilitate intraoperative visualization of brain tissues and deformable registration with preoperative images, a 3D deep learning (DL) reconstruction framework (termed DL-Recon) was proposed for improved intraoperative cone-beam CT (CBCT) image quality.

METHODS

The DL-Recon framework combines physics-based models with deep learning CT synthesis and leverages uncertainty information to promote robustness to unseen features. A 3D generative adversarial network (GAN) with a conditional loss function modulated by aleatoric uncertainty was developed for CBCT-to-CT synthesis. Epistemic uncertainty of the synthesis model was estimated via Monte Carlo (MC) dropout. Using spatially varying weights derived from epistemic uncertainty, the DL-Recon image combines the synthetic CT with an artifact-corrected filtered back-projection (FBP) reconstruction. In regions of high epistemic uncertainty, DL-Recon includes greater contribution from the FBP image. Twenty paired real CT and simulated CBCT images of the head were used for network training and validation, and experiments evaluated the performance of DL-Recon on CBCT images containing simulated and real brain lesions not present in the training data. Performance among learning- and physics-based methods was quantified in terms of structural similarity (SSIM) of the resulting image to diagnostic CT and Dice similarity metric (DSC) in lesion segmentation compared to ground truth. A pilot study was conducted involving seven subjects with CBCT images acquired during neurosurgery to assess the feasibility of DL-Recon in clinical data.

RESULTS

CBCT images reconstructed via FBP with physics-based corrections exhibited the usual challenges to soft-tissue contrast resolution due to image non-uniformity, noise, and residual artifacts. GAN synthesis improved image uniformity and soft-tissue visibility but was subject to error in the shape and contrast of simulated lesions that were unseen in training. Incorporation of aleatoric uncertainty in synthesis loss improved estimation of epistemic uncertainty, with variable brain structures and unseen lesions exhibiting higher epistemic uncertainty. The DL-Recon approach mitigated synthesis errors while maintaining improvement in image quality, yielding 15%-22% increase in SSIM (image appearance compared to diagnostic CT) and up to 25% increase in DSC in lesion segmentation compared to FBP. Clear gains in visual image quality were also observed in real brain lesions and in clinical CBCT images.

CONCLUSIONS

DL-Recon leveraged uncertainty estimation to combine the strengths of DL and physics-based reconstruction and demonstrated substantial improvements in the accuracy and quality of intraoperative CBCT. The improved soft-tissue contrast resolution could facilitate visualization of brain structures and support deformable registration with preoperative images, further extending the utility of intraoperative CBCT in image-guided neurosurgery.

Themes in neuronavigation research: A machine learning topic analysis

Objective

To understand trends in neuronavigation we employed machine learning methods to perform a broad literature review which would be impractical by manual inspection.

Methods

PubMed was queried for articles with "Neuronavigation" in any field from inception-2020. Articles were designated neuronavigation-focused (NF) if "Neuronavigation" was a major MeSH. The latent dirichlet allocation topic modeling technique was used to identify themes of NF research.

Results

There were 3896 articles of which 1727 (44%) were designated as NF. Between 1999-2009 and 2010-2020, the number of NF publications experienced 80% growth. Between 2009-2014 and 2015-2020, there was a 0.3% decline. Eleven themes covered 1367 (86%) NF articles. "Resection of Eloquent Lesions" comprised the highest number of articles (243), followed by "Accuracy and Registration" (242), "Patient Outcomes" (156), "Stimulation and Mapping" (126), "Planning and Visualization" (123), "Intraoperative Tools" (104), "Placement of Ventricular Catheters" (86), "Spine Surgery" (85), "New Systems" (80), "Guided Biopsies" (61), and "Surgical Approach" (61). All topics except for "Planning and Visualization", "Intraoperative Tools", and "New Systems" exhibited a monotonic positive trend. When analyzing subcategories, there were a greater number of clinical assessments or usage of existing neuronavigation systems (77%) rather than modification or development of new apparatuses (18%).

Conclusion

NF research appears to focus on the clinical assessment of neuronavigation and to a lesser extent on the development of new systems. Although neuronavigation has made significant strides, NF research output appears to have plateaued in the last decade.

Feasibility of a lung airway navigation system using fiber-Bragg shape sensing and artificial intelligence for early diagnosis of lung cancer

Currently early diagnosis of malignant lesions at the periphery of lung parenchyma requires guidance of the biopsy needle catheter from the bronchoscope into the smaller peripheral airways via harmful X-ray radiation. Previously, we developed an image-guided system, iMTECH which uses electromagnetic tracking and although it increases the precision of biopsy collection and minimizes the use of harmful X-ray radiation during the interventional procedures, it only traces the tip of the biopsy catheter leaving the remaining catheter untraceable in real time and therefore increasing image registration error. To address this issue, we developed a shape sensing guidance system containing a fiber-Bragg grating (FBG) catheter and an artificial intelligence (AI) software, AIrShape to track and guide the entire biopsy instrument inside the lung airways, without radiation or electromagnetic navigation. We used a FBG fiber with one central and three peripheral cores positioned at 120° from each other, an array of 25 draw tower gratings with 1cm/3nm spacing, 2 mm grating length, Ormocer-T coating, and a total outer diameter of 0.2 mm. The FBG fiber was placed in the working channel of a custom made three-lumen catheter with a tip bending mechanism (FBG catheter). The AIrShape software determines the position of the FBG catheter by superimposing its position to the lung airway center lines using an AI algorithm. The feasibility of the FBG system was tested in an anatomically accurate lung airway model and validated visually and with the iMTECH platform. The results prove a viable shape-sensing hardware and software navigation solution for flexible medical instruments to reach the peripheral airways. During future studies, the feasibility of FBG catheter will be tested in pre-clinical animal models.

Visualising spatial heterogeneity in glioblastoma using imaging habitats

Glioblastoma is a high-grade aggressive neoplasm characterised by significant intra-tumoral spatial heterogeneity. Personalising therapy for this tumour requires non-invasive tools to visualise its heterogeneity to monitor treatment response on a regional level. To date, efforts to characterise glioblastoma's imaging features and heterogeneity have focussed on individual imaging biomarkers, or high-throughput radiomic approaches that consider a vast number of imaging variables across the tumour as a whole. Habitat imaging is a novel approach to cancer imaging that identifies tumour regions or 'habitats' based on shared imaging characteristics, usually defined using multiple imaging biomarkers. Habitat imaging reflects the evolution of imaging biomarkers and offers spatially preserved assessment of tumour physiological processes such perfusion and cellularity. This allows for regional assessment of treatment response to facilitate personalised therapy. In this review, we explore different methodologies to derive imaging habitats in glioblastoma, strategies to overcome its technical challenges, contrast experiences to other cancers, and describe potential clinical applications.

The Current State of Visualization Techniques in Endoscopic Skull Base Surgery

Skull base surgery has undergone significant progress following key technological developments. From early candle-lit devices to the modern endoscope, refinements in visualization techniques have made endoscopic skull base surgery (ESBS) a standard practice for treating a variety of conditions. The endoscope has also been integrated with other technologies to enhance visualization, including fluorescence agents, intraoperative neuronavigation with augmented reality, and the exoscope. Endoscopic approaches have allowed neurosurgeons to reevaluate skull base neuroanatomy from new perspectives. These advances now serve as the foundation for future developments in ESBS. In this narrative review, we discuss the history and development of ESBS, current visualization techniques, and future innovations.

Simulation of surgery for supratentorial gliomas in virtual reality using a 3D volume rendering technique: a poor man's neuronavigation

OBJECTIVE

Different techniques of performing image-guided neurosurgery exist, namely, neuronavigation systems, intraoperative ultrasound, and intraoperative MRI, each with its limitations. Except for ultrasound, other methods are expensive. Three-dimensional virtual reconstruction and surgical simulation using 3D volume rendering (VR) is an economical and excellent technique for preoperative surgical planning and image-guided neurosurgery. In this article, the authors discuss several nuances of the 3D VR technique that have not yet been described.

METHODS

The authors included 6 patients with supratentorial gliomas who underwent surgery between January 2019 and March 2021. Preoperative clinical data, including patient demographics, preoperative planning details (done using the VR technique), and intraoperative details, including relevant photos and videos, were collected. RadiAnt software was used for generating virtual 3D images using the VR technique on a computer running Microsoft Windows.

RESULTS

The 3D VR technique assists in glioma surgery with a preoperative simulation of the skin incision and craniotomy, virtual cortical surface marking and navigation for deep-seated gliomas, preoperative visualization of morbid cortical surface and venous anatomy in surfacing gliomas, identifying the intervenous surgical corridor in both surfacing and deep-seated gliomas, and pre- and postoperative virtual 3D images highlighting the exact spatial geometric residual tumor location and extent of resection for low-grade gliomas (LGGs).

CONCLUSIONS

Image-guided neurosurgery with the 3D VR technique using RadiAnt software is an economical, easy-to-learn, and user-friendly method of simulating glioma surgery, especially in resource-constrained countries where expensive neuronavigation systems are not readily available. Apart from cortical sulci/gyri anatomy, FLAIR sequences are ideal for the 3D visualization of nonenhancing diffuse LGGs using the VR technique. In addition to cortical vessels (especially veins), contrast MRI sequences are perfect for the 3D visualization of contrast-enhancing high-grade gliomas.

Augmented reality-assisted roadmaps during periventricular brain surgery

Visualizing major periventricular anatomical landmarks intraoperatively during brain tumor removal is a decisive measure toward preserving such structures and thus the patient's postoperative quality of life. The aim of this study was to describe potential standardized preoperative planning using standard landmarks and procedures and to demonstrate the feasibility of using augmented reality (AR) to assist in performing surgery according to these "roadmaps." The authors have depicted stepwise AR surgical roadmaps applied to periventricular brain surgery with the aim of preserving major cognitive function. In addition to the technological aspects, this study highlights the importance of using emerging technologies as potential tools to integrate information and to identify and visualize landmarks to be used during tumor removal.

A robust automated surface-matching registration method for neuronavigation

PURPOSE

The surface-matching registration method in the current neuronavigation completes the coarse registration mainly by manually selecting anatomical landmarks, which increases the registration time, makes the automatic registration impossible and sometimes results in mismatch. It may be more practical to use a fast, accurate, and automatic spatial registration method for the patient-to-image registration.

METHODS

A coarse-to-fine spatial registration method to automatically register the patient space to the image space without placing any markers on the head of the patient was proposed. Three-dimensional (3D) keypoints were extracted by 3D Harris corner detector from the point clouds in the patient and image spaces, and used as input to the 4-points congruent sets (4PCS) algorithm which automatically registered the keypoints in the patient space with the keypoints in the image space without any assumptions about initial alignment. Coarsely aligned point clouds in the patient and image space were then fine-registered with a variant of the iterative closest point (ICP) algorithm. Two experiments were designed based on one phantom and five patients to validate the efficiency and effectiveness of the proposed method.

RESULTS

Keypoints were extracted within 7.0 s with a minimum threshold 0.001. In the phantom experiment, the mean target registration error (TRE) of 15 targets on the surface of the elastic phantom in the five experiments was 1.17 ± 0.04 mm, and the average registration time was 17.4 s. In the clinical experiments, the mean TRE of the targets on the first, second, third, fourth, and fifth patient's head surface were 1.70 ± 0.32 mm, 1.83 ± 0.38 mm, 1.64 ± 0.3 mm, 1.67 ± 0.35 mm, and 1.72 ± 0.31 mm, respectively, and the average registration time was 21.4 s. Compared with the method only based on the 4PCS and ICP algorithm and the current clinical method, the proposed method has obvious speed advantage while ensuring the registration accuracy.

CONCLUSIONS

The proposed method greatly improves the registration speed while guaranteeing the equivalent or higher registration accuracy, and avoids a tedious manual process for the coarse registration.

Surface-Registration Frameless Stereotactic Navigation Is Less Accurate During Prone Surgeries: Intraoperative Near-Infrared Visualization Using Second Window Indocyanine Green Offers an Adjunct

BACKGROUND

Frameless neuronavigation allows neurosurgeons to visualize and relate the position of surgical instruments to intracranial pathologies based on preoperative tomographic imaging. However, neuronavigation can often be inaccurate. Multiple factors have been proposed as potential causes, and new technologies are needed to overcome these challenges.

OBJECTIVE

To evaluate the accuracy of neuronavigation systems compared to near-infrared (NIR) fluorescence imaging using Second Window Indocyanine Green, a novel technique, and to determine factors that lead to neuronavigation errors.

METHODS

A retrospective analysis was conducted on 56 patients who underwent primary resections of intracranial tumors. Patients received 5 mg/kg ICG approximately 24 h preoperatively. Intraoperatively, neuronavigation was used to plan craniotomies to place the tumors in the center. After craniotomy, NIR imaging visualized tumor-specific NIR signals. The accuracy of neuronavigation and NIR fluorescence imaging for delineating the tumor boundary prior to durotomy was compared.

RESULTS

The neuronavigation centers and NIR centers were 23.0 ± 7.7 % and 2.6 ± 1.1 % deviated from the tumor centers, respectively, relative to the craniotomy sizes. In 12 cases, significant changes were made to the planned durotomy based on NIR imaging. Patient position was a significant predictor of neuronavigation inaccuracy on both univariate and multivariate analysis, with the prone position having significantly higher inaccuracy (29.2 ± 8.1 %) compared to the supine (16.2 ± 8.1 %, p value < 0.001) or the lateral (17.9 ± 5.1 %, p value = 0.003) positions.

CONCLUSION

Patient position significantly affects neuronavigation accuracy. Intraoperative NIR fluorescence imaging before durotomy offers an opportunity to readjust the neuronavigation image space to better align with the patient space.

An Automatic Spatial Registration Method for Image-Guided Neurosurgery System

OBJECTIVE

This study aimed to investigate the feasibility of an automatic marker-free patient-to-image spatial registration method based on the 4-points congruent sets (4PCS) and iterative closest point (ICP) algorithm for the image-guided neurosurgery system (IGNS).

METHODS

A portable scanner was used to obtain the point cloud of the patient's entire head. The 4PCS algorithm, which is resilient to noise and outliers, automatically registered the point cloud in the patient space to the surface reconstructed from the patient's preoperative images in the image space without any assumptions about initial alignment. A variant of the ICP algorithm was then used to finish the fine registration. Two phantoms and 3 patients' experiments were performed to demonstrate the effectiveness of the proposed method.

RESULTS

In the phantom experiments, the mean target registration error of 15 targets on the surface of the rigid and the elastic phantoms were 1.02 ± 0.18 mm and 1.27 ± 0.36 mm, respectively. In the clinical experiments, the mean target registration error of 7 targets on the first, second and third patient's head were 1.88 ± 0.19 mm, 1.84 ± 0.19 mm, and 1.89 ± 0.18 mm, respectively, which was sufficient to meet clinical requirements. The registration accuracy and registration time using the proposed method are better than that using the method based on manually coarse registration and automatic fine registration.

CONCLUSIONS

It is feasible to use the automatic spatial registration method based on the 4PCS and ICP algorithm for the IGNS. Moreover, it can replace the spatial registration method based on manually selected anatomical landmarks combined with the automatic fine registration in the currently used IGNS.

Improved Surgical Safety via Intraoperative Navigation for Transnasal Transsphenoidal Resection of Pituitary Adenomas

Objectives  Intraoperative navigation during neurosurgery can aid in the detection of critical structures and target lesions. The safety and efficacy of intraoperative, stereotactic computed tomography (CT) in the transnasal transsphenoidal resection of pituitary adenomas were explored. Design  Retrospective chart review Setting  Tertiary care hospital Participants  Patients who underwent transsphenoidal resection of pituitary adenomas from February 2002 to May 2017. Intraoperative stereotactic CT navigation was used for all patients after mid-October 2013. Main Outcome Measures  Operative time, estimated blood loss, gross total resection rate. Results  Of 634 patients included, 175 underwent surgery with intraoperative navigation and 444 had no intraoperative navigation during surgery. There was no difference in mean age, sex, tumor type, or tumor size between the two groups. Operative time, endoscope use, cerebrospinal fluid diversion, and estimated blood loss were also similar. Two patients showed intraoperative, iatrogenic misdirection in the absence of stereotactic CT navigation ( p  = 0.99) but similar numbers of patients having navigated and non-navigated surgery returned to the operating room, underwent gross total resection, and showed endocrinological normalization. Conclusions  These results suggest that intraoperative navigation can reduce injury without resulting in increased operative time, estimated blood loss, or reduction in gross total resection.

Image-Guided Neurosurgery

Image-guided surgery provides more precise targeting, is less invasive, and has improved outcomes when compared with conventional surgical approaches. Imaging is used to plan, monitor progress, and assess results. Because no one modality offers real-time physiological and anatomical information, a wide range of imaging modalities are used at each phase of the surgery. This article will discuss how various modalities are used in image-guided neurosurgery for common brain pathologies.