Image-guided Neurosurgery Research Articles

MO-DE-202-03: Image-Guided Surgery and Interventions in the Advanced Multimodality Image-Guided Operating (AMIGO) Suite

At least three major trends in surgical intervention have emerged over the last decade: a move toward more minimally invasive (or non-invasive) approach to the surgical target; the development of high-precision treatment delivery techniques; and the increasing role of multi-modality intraoperative imaging in support of such procedures. This symposium includes invited presentations on recent advances in each of these areas and the emerging role for medical physics research in the development and translation of high-precision interventional techniques. The four speakers are: (1)Keyvan Farahani, “Image-guided focused ultrasound surgery and therapy” (2)Jeffrey H. Siewerdsen, “Advances in image registration and reconstruction for image-guided neurosurgery” (3)Tina Kapur, “Image-guided surgery and interventions in the advanced multimodality image-guided operating (AMIGO) suite” (4)Raj Shekhar, “Multimodality image-guided interventions: Multimodality for the rest of us” Learning Objectives: 1.Understand the principles and applications of HIFU in surgical ablation. 2.Learn about recent advances in 3D–2D and 3D deformable image registration in support of surgical safety and precision. 3.Learn about recent advances in model-based 3D image reconstruction in application to intraoperative 3D imaging. 4.Understand the multi-modality imaging technologies and clinical applications investigated in the AMIGO suite. 5.Understand the emerging need and techniques to implement multi-modality image guidance in surgical applications such as neurosurgery, orthopaedic surgery, vascular surgery, and interventional radiology. Research supported by the NIH and Siemens Healthcare.; J. Siewerdsen; Grant Support - National Institutes of Health; Grant Support - Siemens Healthcare; Grant Support - Carestream Health; Advisory Board - Carestream Health; Licensing Agreement - Carestream Health; Licensing Agreement - Elekta Oncology.; T. Kapur, P41EB015898; R. Shekhar, Funding: R42CA137886 and R41CA192504 Disclosure and CoI: IGI Technologies, small-business partner on the grants

MO-DE-202-04: Multimodality Image-Guided Surgery and Intervention: For the Rest of Us

At least three major trends in surgical intervention have emerged over the last decade: a move toward more minimally invasive (or non-invasive) approach to the surgical target; the development of high-precision treatment delivery techniques; and the increasing role of multi-modality intraoperative imaging in support of such procedures. This symposium includes invited presentations on recent advances in each of these areas and the emerging role for medical physics research in the development and translation of high-precision interventional techniques. The four speakers are: (1)Keyvan Farahani, “Image-guided focused ultrasound surgery and therapy” (2)Jeffrey H. Siewerdsen, “Advances in image registration and reconstruction for image-guided neurosurgery” (3)Tina Kapur, “Image-guided surgery and interventions in the advanced multimodality image-guided operating (AMIGO) suite” (4)Raj Shekhar, “Multimodality image-guided interventions: Multimodality for the rest of us” Learning Objectives: 1.Understand the principles and applications of HIFU in surgical ablation. 2.Learn about recent advances in 3D–2D and 3D deformable image registration in support of surgical safety and precision. 3.Learn about recent advances in model-based 3D image reconstruction in application to intraoperative 3D imaging. 4.Understand the multi-modality imaging technologies and clinical applications investigated in the AMIGO suite. 5.Understand the emerging need and techniques to implement multi-modality image guidance in surgical applications such as neurosurgery, orthopaedic surgery, vascular surgery, and interventional radiology. Research supported by the NIH and Siemens Healthcare.; J. Siewerdsen; Grant Support - National Institutes of Health; Grant Support - Siemens Healthcare; Grant Support - Carestream Health; Advisory Board - Carestream Health; Licensing Agreement - Carestream Health; Licensing Agreement - Elekta Oncology.; T. Kapur, P41EB015898; R. Shekhar, Funding: R42CA137886 and R41CA192504 Disclosure and CoI: IGI Technologies, small-business partner on the grants

MO-DE-202-01: Image-Guided Focused Ultrasound Surgery and Therapy

At least three major trends in surgical intervention have emerged over the last decade: a move toward more minimally invasive (or non-invasive) approach to the surgical target; the development of high-precision treatment delivery techniques; and the increasing role of multi-modality intraoperative imaging in support of such procedures. This symposium includes invited presentations on recent advances in each of these areas and the emerging role for medical physics research in the development and translation of high-precision interventional techniques. The four speakers are: (1) Keyvan Farahani, “Image-guided focused ultrasound surgery and therapy” (2) Jeffrey H. Siewerdsen, “Advances in image registration and reconstruction for image-guided neurosurgery” (3) Tina Kapur, “Image-guided surgery and interventions in the advanced multimodality image-guided operating (AMIGO) suite” (4) Raj Shekhar, “Multimodality image-guided interventions: Multimodality for the rest of us” Learning Objectives: 1. Understand the principles and applications of HIFU in surgical ablation. 2. Learn about recent advances in 3D–2D and 3D deformable image registration in support of surgical safety and precision. 3. Learn about recent advances in model-based 3D image reconstruction in application to intraoperative 3D imaging. 4. Understand the multi-modality imaging technologies and clinical applications investigated in the AMIGO suite. 5. Understand the emerging need and techniques to implement multi-modality image guidance in surgical applications such as neurosurgery, orthopaedic surgery, vascular surgery, and interventional radiology. Research supported by the NIH and Siemens Healthcare.; J. Siewerdsen; Grant Support - National Institutes of Health; Grant Support - Siemens Healthcare; Grant Support - Carestream Health; Advisory Board - Carestream Health; Licensing Agreement - Carestream Health; Licensing Agreement - Elekta Oncology.; T. Kapur, P41EB015898; R. Shekhar, Funding: R42CA137886 and R41CA192504 Disclosure and CoI: IGI Technologies, small-business partner on the grants

MO-DE-202-02: Advances in Image Registration and Reconstruction for Image-Guided Neurosurgery

At least three major trends in surgical intervention have emerged over the last decade: a move toward more minimally invasive (or non-invasive) approach to the surgical target; the development of high-precision treatment delivery techniques; and the increasing role of multi-modality intraoperative imaging in support of such procedures. This symposium includes invited presentations on recent advances in each of these areas and the emerging role for medical physics research in the development and translation of high-precision interventional techniques. The four speakers are: (1) Keyvan Farahani, “Image-guided focused ultrasound surgery and therapy” (2) Jeffrey H. Siewerdsen, “Advances in image registration and reconstruction for image-guided neurosurgery” (3) Tina Kapur, “Image-guided surgery and interventions in the advanced multimodality image-guided operating (AMIGO) suite” (4) Raj Shekhar, “Multimodality image-guided interventions: Multimodality for the rest of us” Learning Objectives: 1. Understand the principles and applications of HIFU in surgical ablation. 2. Learn about recent advances in 3D–2D and 3D deformable image registration in support of surgical safety and precision. 3. Learn about recent advances in model-based 3D image reconstruction in application to intraoperative 3D imaging. 4. Understand the multi-modality imaging technologies and clinical applications investigated in the AMIGO suite. 5. Understand the emerging need and techniques to implement multi-modality image guidance in surgical applications such as neurosurgery, orthopaedic surgery, vascular surgery, and interventional radiology. Research supported by the NIH and Siemens Healthcare.; J. Siewerdsen; Grant Support - National Institutes of Health; Grant Support - Siemens Healthcare; Grant Support - Carestream Health; Advisory Board - Carestream Health; Licensing Agreement - Carestream Health; Licensing Agreement - Elekta Oncology.; T. Kapur, P41EB015898; R. Shekhar, Funding: R42CA137886 and R41CA192504 Disclosure and CoI: IGI Technologies, small-business partner on the grants

Integrating Structural MRI and DTI in Brain Deformation Simulation for Image-Guided Neurosurgery

In image-guided neurosurgery, pre-operative magnetic resonance (MR) images can be fused with intra-operative ones to enhance visualization and navigation to determine the resection margin. However, intra-operative brain deformation, after opening the skull, greatly degrades accurate matching between these images. Although direct registering these images aims to address such a deformation problem, simulating brain shifts can potentially improve registration accuracy. In this paper, we introduce the biomechanical finite element model (FEM) to compute brain deformation such as parenchyma, tumor, ventricle and white matter shifts during craniotomy using both MRI and diffusion tensor images (DTI). In experiments, we show the pipeline for brain shift simulation and compare the results using different loading and boundary conditions of the FEM model. This method can be potentially used for simulating brain shifts before non-rigid image registration between intra-operative and pre-operative images in image-guided neurosurgery. Keywords: Brain deformation, diffusion tenser imaging (DTI), finite-element model, image guided neurosurgery, magnetic resonance (MR), neurosurgery.

Assessment of an image-guided neurosurgery system using a head phantom

Objective: To acknowledge the challenges and limitations of image-guided neurosurgery systems, we compared the application accuracy of two different image registration methods for one commercial system. (VectorVision, BrainLab, Germany).Methods: We used an anthropomorphic head phantom for radiosurgery and a custom built add-on to simulate surgical targets inside the brain during an image-guided neurosurgery. We used two image registration methods, fiducial registration using attachable surface markers for computed tomography (CT) and surface registration using infrared laser face scanning. After simulation, we calculated the three-dimensional (3D) distance between the predicted position of a target, and its actual position using a registered pointer and an infrared camera. Deviations were measured for both superficial fiducial markers and internal surgical targets by five different users.Results: Deviations from the location of fiducial markers after each registration method were 2.15 ± 0.93 mm after CT surface marker registration and 1.25 ± 0.64 mm after infrared face scanner registration. The mean target registration errors were 2.95 ± 1.4 mm using fiducial registration and 2.90 ± 1.3 mm using surface registration. The largest deviations (6.2 mm) were found for the targets in the skull base and posterior cranial fossa. Fiducial deviations and target registration errors were statistically uncorrelated. The total application accuracy was 4.87 ± 0.97 mm after CT surface marker registration and 4.14 ± 0.64 mm after infrared face scanner registration.Conclusions: Despite others have reported differences, we did not find significant variations between both registration methods for the target registration error, although application accuracy was slightly better after surface face registration. Superficial registration errors, but not the target registration error, can be routinely evaluated in the operating room. Since both errors were uncorrelated, surgeons may neglect the achievable accuracy of the procedure. The described method is recommended to assess application accuracy in the operating room.

Marker-less tracking of brain surface deformations by non-rigid registration integrating surface and vessel/sulci features

To compensate for brain shift in image-guided neurosurgery, we propose a new non-rigid registration method that integrates surface and vessel/sulci feature to noninvasively track the brain surface. Textured brain surfaces were acquired using phase-shift three-dimensional (3D) shape measurement, which offers 2D image pixels and their corresponding 3D points directly. Measured brain surfaces were noninvasively tracked using the proposed method by minimizing a new energy function, which is a weighted combination of 3D point corresponding estimation and surface deformation constraints. Initially, the measured surfaces were divided into featured and non-featured parts using a Frangi filter. The corresponding feature/non-feature points between intraoperative brain surfaces were estimated using the closest point algorithm. Subsequently, smoothness and rigidity constraints were introduced in the energy function for a smooth surface deformation and local surface detail conservation, respectively. Our 3D shape measurement accuracy was evaluated using 20 spheres for bias and precision errors. In addition, the proposed method was evaluated based on root mean square error (RMSE) and target registration error (TRE) with five porcine brains for which deformations were produced by gravity and pushing with different displacements in both the vertical and horizontal directions. The minimum and maximum bias errors were 0.32 and 0.61mm, respectively. The minimum and maximum precision errors were 0.025 and 0.30mm, respectively. Quantitative validation with porcine brains showed that the average RMSE and TRE were 0.1 and 0.9mm, respectively. The proposed method appeared to be advantageous in integrating vessels/sulci feature, robust to changes in deformation magnitude and integrated feature numbers, and feasible in compensating for brain shift deformation in surgeries.

Image-guided Stereotactic Neurosurgery: Practices and Pitfalls

Image-guided neurosurgery (IGN) is a technique for localizing objects of surgical interest within the brain. In the past, its main use was placement of electrodes; however, the advent of computed tomography has led to a rebirth of IGN. Advances in computing techniques and neuroimaging tools allow improved surgical planning and intraoperative information. IGN influences many neurosurgical fields including neuro-oncology, functional disease, and radiosurgery. As development continues, several problems remain to be solved. This article provides a general overview of IGN with a brief discussion of future directions.

Improved sensitivity to fluorescence for cancer detection in wide-field image-guided neurosurgery.

In glioma surgery, Protoporphyrin IX (PpIX) fluorescence may identify residual tumor that could be resected while minimizing damage to normal brain. We demonstrate that improved sensitivity for wide-field spectroscopic fluorescence imaging is achieved with minimal disruption to the neurosurgical workflow using an electron-multiplying charge-coupled device (EMCCD) relative to a state-of-the-art CMOS system. In phantom experiments the EMCCD system can detect at least two orders-of-magnitude lower PpIX. Ex vivo tissue imaging on a rat glioma model demonstrates improved fluorescence contrast compared with neurosurgical fluorescence microscope technology, and the fluorescence detection is confirmed with measurements from a clinically-validated spectroscopic probe. Greater PpIX sensitivity in wide-field fluorescence imaging may improve the residual tumor detection during surgery with consequent impact on survival.

Accurate and fast deformable medical image registration for brain tumor resection using image-guided neurosurgery

We present a parallel adaptive physics-based non-rigid registration framework for aligning pre-operative to intra-operative brain magnetic resonance images (MRI) of patients who have undergone a tumor resection. This framework extends our earlier work on the physics-based methods by using an adaptive, multi-material, parallel finite element biomechanical model to describe the physical deformations of the brain. Our registration technology incorporates fast image-to-mesh convertors for remeshing the brain model in real-time eliminating the poor-quality elements; various linear solvers to accurately estimate the volumetric deformations; efficient block-matching techniques to compensate for the missing/unrealistic matches induced by the tumor resection. Our evaluation is based on six clinical volume MRI data-sets including (i) isotropic and anisotropic image spacings, and (ii) partial and complete tumor resections. We compare our framework with four methods: a rigid and BSpline deformable registration implemented on 3D Slicer v4.4.0, a physics-based non-rigid registration available on ITK v4.7.0, and an adaptive physics-based non-rigid registration. We show that the proposed technology provides the finest MRI alignments among all the methods. The Hausdorff distance is on average up to 3.78 and 3.12 times more accurate compared to the rigid and the other non-rigid registration methods, respectively. Additionally, it brings the end-to-end execution within the real-time constraints imposed by the neurosurgical procedure. In a Linux Dell workstation with 12 Intel Xeon 3.47 GHz CPU cores and 96 GB of RAM, it registers the anisotropic volume data in less than 93 s and the isotropic data in less than 21 s.

Clinical evaluation of a model-updated image-guidance approach to brain shift compensation: experience in 16 cases.

Brain shift during neurosurgical procedures must be corrected for in order to reestablish accurate alignment for successful image-guided tumor resection. Sparse-data-driven biomechanical models that predict physiological brain shift by accounting for typical deformation-inducing events such as cerebrospinal fluid drainage, hyperosmotic drugs, swelling, retraction, resection, and tumor cavity collapse are an inexpensive solution. This study evaluated the robustness and accuracy of a biomechanical model-based brain shift correction system to assist with tumor resection surgery in 16 clinical cases. Preoperative computation involved the generation of a patient-specific finite element model of the brain and creation of an atlas of brain deformation solutions calculated using a distribution of boundary and deformation-inducing forcing conditions (e.g., sag, tissue contraction, and tissue swelling). The optimum brain shift solution was determined using an inverse problem approach which linearly combines solutions from the atlas to match the cortical surface deformation data collected intraoperatively. The computed deformations were then used to update the preoperative images for all 16 patients. The mean brain shift measured ranged on average from 2.5 to 21.3 mm, and the biomechanical model-based correction system managed to account for the bulk of the brain shift, producing a mean corrected error ranging on average from 0.7 to 4.0 mm. Biomechanical models are an inexpensive means to assist intervention via correction for brain deformations that can compromise surgical navigation systems. To our knowledge, this study represents the most comprehensive clinical evaluation of a deformation correction pipeline for image-guided neurosurgery.

X-ray and optical stereo-based 3D sensor fusion system for image-guided neurosurgery.

In neurosurgery, an image-guided operation is performed to confirm that the surgical instruments reach the exact lesion position. Among the multiple imaging modalities, an X-ray fluoroscope mounted on C- or O-arm is widely used for monitoring the position of surgical instruments and the target position of the patient. However, frequently used fluoroscopy can result in relatively high radiation doses, particularly for complex interventional procedures. The proposed system can reduce radiation exposure and provide the accurate three-dimensional (3D) position information of surgical instruments and the target position. X-ray and optical stereo vision systems have been proposed for the C- or O-arm. Two subsystems have same optical axis and are calibrated simultaneously. This provides easy augmentation of the camera image and the X-ray image. Further, the 3D measurement of both systems can be defined in a common coordinate space. The proposed dual stereoscopic imaging system is designed and implemented for mounting on an O-arm. The calibration error of the 3D coordinates of the optical stereo and X-ray stereo is within 0.1 mm in terms of the mean and the standard deviation. Further, image augmentation with the camera image and the X-ray image using an artificial skull phantom is achieved. As the developed dual stereoscopic imaging system provides 3D coordinates of the point of interest in both optical images and fluoroscopic images, it can be used by surgeons to confirm the position of surgical instruments in a 3D space with minimum radiation exposure and to verify whether the instruments reach the surgical target observed in fluoroscopic images.

Towards estimating fiducial localization error of point-based registration in image-guided neurosurgery.

Fiducial Localization Error (FLE) is one of the major reasons of inaccuracy in point-based spatial registration of Image-Guided Neurosurgery System (IGNS), and minimizing FLE is the fundamental way to improve spatial registration accuracy. A reliable estimation of FLE is needed, as it cannot be measured directly in real application of IGNS. In this paper, we propose a method to estimate the FLE in a point-based registration of IGNS. Test fiducial point sets were generated in one coordinate system around the given fiducial point set by utilizing simple random sampling. Further, these points were registered to the fiducial point set in the other coordinate system. The average position of the test fiducial point sets with small FRE are calculated and its displacement from the given fiducial point set is the parameter used to estimate the FLE of each fiducial point. The correlation between the displacement and the FLE of each fiducial point is greater than 0.75 when nine or more fiducial points were utilized. This correlation gradually increases up to 0.9 with the increase of the number of fiducial points.

A novel registration method for image-guided neurosurgery system based on stereo vision.

This study presents a novel spatial registration method of Image-guided neurosurgery system (IGNS) based on stereo-vision. Images of the patient's head are captured by a video camera, which is calibrated and tracked by an optical tracking system. Then, a set of sparse facial data points are reconstructed from them by stereo vision in the patient space. Surface matching method is utilized to register the reconstructed sparse points and the facial surface reconstructed from preoperative images of the patient. Simulation experiments verified the feasibility of the proposed method. The proposed method it is a new low-cost and easy-to-use spatial registration method for IGNS, with good prospects for clinical application.

Intraoperative MR Imaging in Neurosurgery.

Intraoperative magnetic resonance imaging (iMRI) has dramatically expanded and nowadays presents state-of-the-art technique for image-guided neurosurgery, facilitating critical precision and effective surgical treatment of various brain pathologies. Imaging hardware providing basic imaging sequences as well as advanced MRI can be seamlessly integrated into routine surgical environments, which continuously leads to emerging indications for iMRI-assisted surgery. Besides the obvious intraoperative diagnostic yield, the initial clinical benefits have to be confirmed by future-controlled long-term studies.

Simulated field maps for susceptibility artefact correction in interventional MRI.

Intraoperative MRI (iMRI) is a powerful modality for acquiring images of the brain to facilitate precise image-guided neurosurgery. Diffusion-weighted MRI (DW-MRI) provides critical information about location, orientation and structure of nerve fibre tracts, but suffers from the "susceptibility artefact" stemming from magnetic field perturbations due to the step change in magnetic susceptibility at air-tissue boundaries in the head. An existing approach to correcting the artefact is to acquire a field map by means of an additional MRI scan. However, to recover true field maps from the acquired field maps near air-tissue boundaries is challenging, and acquired field maps are unavailable in historical MRI data sets. This paper reports a detailed account of our method to simulate field maps from structural MRI scans that was first presented at IPCAI 2014 and provides a thorough experimental and analysis section to quantitatively validate our technique. We perform automatic air-tissue segmentation of intraoperative MRI scans, feed the segmentation into a field map simulation step and apply the acquired and the simulated field maps to correct DW-MRI data sets. We report results for 12 patient data sets acquired during anterior temporal lobe resection surgery for the surgical management of focal epilepsy. We find a close agreement between acquired and simulated field maps and observe a statistically significant reduction in the susceptibility artefact in DW-MRI data sets corrected using simulated field maps in the vicinity of the resection. The artefact reduction obtained using acquired field maps remains better than that using the simulated field maps in all evaluated regions of the brain. The proposed simulated field maps facilitate susceptibility artefact reduction near the resection. Accurate air-tissue segmentation is key to achieving accurate simulation. The proposed simulation approach is adaptable to different iMRI and neurosurgical applications.

Intraoperative fiducial-less patient registration using volumetric 3D ultrasound: a prospective series of 32 neurosurgical cases.

Fiducial-based registration (FBR) is used widely for patient registration in image-guided neurosurgery. The authors of this study have developed an automatic fiducial-less registration (FLR) technique to find the patient-to-image transformation by directly registering 3D ultrasound (3DUS) with MR images without incorporating prior information. The purpose of the study was to evaluate the performance of the FLR technique when used prospectively in the operating room and to compare it with conventional FBR. In 32 surgical patients who underwent conventional FBR, preoperative T1-weighted MR images (pMR) with attached fiducial markers were acquired prior to surgery. After craniotomy but before dural opening, a set of 3DUS images of the brain volume was acquired. A 2-step registration process was executed immediately after image acquisition: 1) the cortical surfaces from pMR and 3DUS were segmented, and a multistart sum-of-squared-intensity-difference registration was executed to find an initial alignment between down-sampled binary pMR and 3DUS volumes; and 2) the alignment was further refined by a mutual information-based registration between full-resolution grayscale pMR and 3DUS images, and a patient-to-image transformation was subsequently extracted. To assess the accuracy of the FLR technique, the following were quantified: 1) the fiducial distance error (FDE); and 2) the target registration error (TRE) at anterior commissure and posterior commissure locations; these were compared with conventional FBR. The results showed that although the average FDE (6.42 ± 2.05 mm) was higher than the fiducial registration error (FRE) from FBR (3.42 ± 1.37 mm), the overall TRE of FLR (2.51 ± 0.93 mm) was lower than that of FBR (5.48 ± 1.81 mm). The results agreed with the intent of the 2 registration techniques: FBR is designed to minimize the FRE, whereas FLR is designed to optimize feature alignment and hence minimize TRE. The overall computational cost of FLR was approximately 4-5 minutes and minimal user interaction was required. Because the FLR method directly registers 3DUS with MR by matching internal image features, it proved to be more accurate than FBR in terms of TRE in the 32 patients evaluated in this study. The overall efficiency of FLR in terms of the time and personnel involved is also improved relative to FBR in the operating room, and the method does not require additional image scans immediately prior to surgery. The performance of FLR and these results suggest potential for broad clinical application.

New Protocol for Skin Landmark Registration in Image-Guided Neurosurgery: Technical Note.

Newer versions of the commercial Medtronic StealthStation allow the use of only 8 landmark pairs for patient-to-image registration as opposed to 9 landmarks in older systems. The choice of which landmark pair to drop in these newer systems can have an effect on the quality of the patient-to-image registration. To investigate 4 landmark registration protocols based on 8 landmark pairs and compare the resulting registration accuracy with a 9-landmark protocol. Four different protocols were tested on both phantoms and patients. Two of the protocols involved using 4 ear landmarks and 4 facial landmarks and the other 2 involved using 3 ear landmarks and 5 facial landmarks. Both the fiducial registration error and target registration error were evaluated for each of the different protocols to determine any difference between them and the 9-landmark protocol. No difference in fiducial registration error was found between any of the 8-landmark protocols and the 9-landmark protocol. A significant decrease (P < .05) in target registration error was found when using a protocol based on 4 ear landmarks and 4 facial landmarks compared with the other protocols based on 3 ear landmarks. When using 8 landmarks to perform the patient-to-image registration, the protocol using 4 ear landmarks and 4 facial landmarks greatly outperformed the other 8-landmark protocols and 9-landmark protocol, resulting in the lowest target registration error.

Intraoperative CT Scanner Combined With Image-Guided Neurosurgery

This article discusses the use of intraoperative computed tomography (CT) during neurosurgery. This intraoperative CT scanner is integrated with an image-guided surgery system. The authors’ intent is to present information on how these technologies are implemented in the operating room and to describe the improvements in surgical accuracy. Two case studies demonstrate the workflow during spinal procedures and the advantages for the surgeons and patients.

David Roberts: Advancements in image-guided neurosurgery

David Roberts: Advancements in image-guided neurosurgery