Use of cortical surface vessel registration for image-guided neurosurgery

Fully automated segmentation of brain and scalp blood vessels on multi-parametric magnetic resonance imaging using multi-view cascaded networks

BACKGROUND AND OBJECTIVE

Neurosurgical navigation is a critical element of brain surgery, and accurate segmentation of brain and scalp blood vessels is crucial for surgical planning and treatment. However, conventional methods for segmenting blood vessels based on statistical or thresholding techniques have limitations. In recent years, deep learning-based methods have emerged as a promising solution for blood vessel segmentation, but the segmentation of small blood vessels and scalp blood vessels remains challenging. This study aimed to explore a solution to overcoming the challenges.

METHODS

This study proposes a multi-view cascaded deep learning network (MVPCNet) that combines multiple refinements, including multi-view learning, multi-parameter input, and a multi-view ensemble module. We evaluated the proposed method on a dataset of 155 patients, which included annotations for brain and scalp blood vessels. Five-fold cross-validation was conducted on the dataset to assess the performance of the network.

RESULTS

Ablation experiments showed that the proposed refinements in MVPCNet significantly improved the segmentation of small blood and low-contrast vessel performance, which segmented scalp blood vessels from the original images, increasing the Dice and the 95 % Hausdorf distance (HD), from 0.865 to 0.922 and from 1.28 mm to 0.47 mm, respectively, compared to the baseline model.

CONCLUSIONS

The proposed method in this study provided a fully automated and accurate segmentation of brain and scalp blood vessels, which is essential for neurosurgical navigation and has potential for clinical applications.

Augmented Reality in Extratemporal Lobe Epilepsy Surgery

Background: Epilepsy surgery for extratemporal lobe epilepsy (ETLE) is challenging, particularly when MRI findings are non-lesional and seizure patterns are complex. Invasive diagnostic techniques are crucial for accurately identifying the epileptogenic zone and its relationship with surrounding functional tissue. Microscope-based augmented reality (AR) support, combined with navigation, may enhance intraoperative orientation, particularly in cases involving subtle or indistinct lesions, thereby improving patient outcomes and safety (e.g., seizure freedom and preservation of neuronal integrity). Therefore, this study was conducted to prove the clinical advantages of microscope-based AR support in ETLE surgery. Methods: We retrospectively analyzed data from ten patients with pharmacoresistant ETLE who underwent invasive diagnostics with depth and/or subdural grid electrodes, followed by resective surgery. AR support was provided via the head-up displays of the operative microscope, with navigation based on automatic intraoperative computed tomography (iCT)-based registration. The surgical plan included the suspected epileptogenic lesion, electrode positions, and relevant surrounding functional structures, all of which were visualized intraoperatively. Results: Six patients reported complete seizure freedom following surgery (ILAE 1), one patient was seizure-free at the 2-year follow-up, and one patient experienced only auras (ILAE 2). Two patients developed transient neurological deficits that resolved shortly after surgery. Conclusions: Microscope-based AR support enhanced intraoperative orientation in all cases, contributing to improved patient outcomes and safety. It was highly valued by experienced surgeons and as a training tool for less experienced practitioners.

Use of Neuronavigation and Augmented Reality in Transsphenoidal Pituitary Adenoma Surgery

The aim of this study was to report on the clinical experience with microscope-based augmented reality (AR) in transsphenoidal surgery compared to the classical microscope-based approach. AR support was established using the head-up displays of the operating microscope, with navigation based on fiducial-/surface- or automatic intraoperative computed tomography (iCT)-based registration. In a consecutive single surgeon series of 165 transsphenoidal procedures, 81 patients underwent surgery without AR support and 84 patients underwent surgery with AR support. AR was integrated straightforwardly within the workflow. ICT-based registration increased AR accuracy significantly (target registration error, TRE, 0.76 ± 0.33 mm) compared to the landmark-based approach (TRE 1.85 ± 1.02 mm). The application of low-dose iCT protocols led to a significant reduction in applied effective dosage being comparable to a single chest radiograph. No major vascular or neurological complications occurred. No difference in surgical time was seen, time to set-up patient registration prolonged intraoperative preparation time on average by twelve minutes (32.33 ± 13.35 vs. 44.13 ± 13.67 min), but seems justifiable by the fact that AR greatly and reliably facilitated surgical orientation and increased surgeon comfort and patient safety, not only in patients who had previous transsphenoidal surgery but also in cases with anatomical variants. Automatic intraoperative imaging-based registration is recommended.

Minimally invasive supratentorial neurosurgical approaches guided by Smartphone app and compass

The precise location in the scalp of specifically planned points can help to achieve less invasive approaches. This study aims to develop a smartphone app, evaluate the precision and accuracy of the developed tool, and describe a series of cases using the referred technique. The application was developed with the React Native framework for Android and iOS. A phantom was printed based on the patient's CT scan, which was used for the calculation of accuracy and precision of the method. The points of interest were marked with an "x" on the patient's head, with the aid of the app and a compass attached to a skin marker pen. Then, two experienced neurosurgeons checked the plausibility of the demarcations based on the anatomical references. Both evaluators marked the frontal, temporal and parietal targets with a difference of less than 5 mm from the corresponding intended point, in all cases. The overall average accuracy observed was 1.6 ± 1.0 mm. The app was used in the surgical planning of trepanations for ventriculoperitoneal (VP) shunts and for drainage of abscesses, and in the definition of craniotomies for meningiomas, gliomas, brain metastases, intracranial hematomas, cavernomas, and arteriovenous malformation. The sample consisted of 88 volunteers who exhibited the following pathologies: 41 (46.6%) had brain tumors, 17 (19.3%) had traumatic brain injuries, 16 (18.2%) had spontaneous intracerebral hemorrhages, 2 (2.3%) had cavernomas, 1 (1.1%) had arteriovenous malformation (AVM), 4 (4.5%) had brain abscesses, and 7 (7.9%) had a VP shunt placement. In cases approached by craniotomy, with the exception of AVM, straight incisions and minicraniotomy were performed. Surgical planning with the aid of the NeuroKeypoint app is feasible and reliable. It has enabled neurological surgeries by craniotomy and trepanation in an accurate, precise, and less invasive manner.

Development of Innovative Neurosurgical Operation Support Method Using Mixed-Reality Computer Graphics

Background

In neurosurgery, it is important to inspect the spatial correspondence between the preoperative medical image (virtual space), and the intraoperative findings (real space) to improve the safety of the surgery. Navigation systems and related modalities have been reported as methods for matching this correspondence. However, because of the influence of the brain shift accompanying craniotomy, registration accuracy is reduced. In the present study, to overcome these issues, we developed a spatially accurate registration method of medical fusion 3-dimensional computer graphics and the intraoperative brain surface photograph, and its registration accuracy was measured.

Methods

The subjects included 16 patients with glioma. Nonrigid registration using the landmarks and thin-plate spline methods was performed for the fusion 3-dimensional computer graphics and the intraoperative brain surface photograph, termed mixed-reality computer graphics. Regarding the registration accuracy measurement, the target registration error was measured by two neurosurgeons, with 10 points for each case at the midpoint of the landmarks.

Results

The number of target registration error measurement points was 160 in the 16 cases. The target registration error was 0.72 ± 0.04 mm. Aligning the intraoperative brain surface photograph and the fusion 3-dimensional computer graphics required ∼10 minutes on average. The average number of landmarks used for alignment was 24.6.

Conclusions

Mixed-reality computer graphics enabled highly precise spatial alignment between the real space and virtual space. Mixed-reality computer graphics have the potential to improve the safety of the surgery by allowing complementary observation of brain surface photographs and fusion 3-dimensional computer graphics.

Importance of Veins for Neurosurgery as Landmarks Against Brain Shifting Phenomenon: An Anatomical and 3D-MPRAGE MR Reconstruction of Superficial Cortical Veins

Modern neurosurgery uses preoperative imaging daily. Three-dimensional reconstruction of the cortical anatomy and of the superficial veins helps the surgeons plan and perform neurosurgical procedures much more safely. The target is always to give the patient maximum benefit in terms of outcome and minimize intraoperative and postoperative complications. This study aims to develop a method for the combined representation of the cerebral cortex anatomy and the superficial cerebral veins, whose integration is beneficial in daily practice. Only those patients who underwent surgical procedures with craniotomy and a large opening of the dura mater were included in this study, for a total of 23 patients, 13 females (56.5%) and 10 males (43.5%). The average age was 50.1 years. We used a magnetic resonance tomograph Magnetom Vision® 1.5T (Siemens AG). Two sequences were applied: a strongly T1-weighted magnetization-prepared rapid acquisition with gradient echo (MPRAGE) sequence to visualize cerebral anatomical structures, and a FLASH-2D-TOF angiography sequence to visualize the venous vessels on the cortical surface after the administration of a paramagnetic contrast agent. The two data sets were superimposed manually, co-registered in an interactive process, and merged to create a combined data set, segmented and visualized as a three-dimensional reconstruction. Furthermore, we present our method for visualizing superficial veins, which helps manage brain shift (BS). We also performed anatomical observations on the reconstructions. The reconstructions of the cortical and venous anatomy proved to be a valuable tool in surgical planning and positively influenced the surgical procedure. Due to the good correlation with the existing surgical site, this method should be validated on a larger cohort or in a multicentric study.

Three-dimensional neuronavigation in SEEG-guided epilepsy surgery

OBJECTIVES

Epilepsy surgery is mainly cortical surgery and the precise definition of the epileptogenic zone on the complex cortical surface is of paramount importance. Stereoelectroencephalography (SEEG) may delineate the epileptogenic zone even in cases of non-lesional epilepsy. The aim of our study was to present a technique of 3D neuronavigation based on the brain surface and SEEG electrodes reconstructions using FSL and 3DSlicer software.

PATIENTS AND METHODS

Our study included 26 consecutive patients operated on for drug-resistant epilepsy after SEEG exploration between January 2015 and December 2017. All patients underwent 1.5 T pre-SEEG MRI, post-SEEG CT, DICOM data post-processing using FSL and 3DSlicer, preoperative planning on 3DSlicer, and intraoperative 3D neuronavigation. Accuracy and precision of 3D SEEG reconstruction and 3D neuronavigation was assessed.

RESULTS

We identified 125 entry points of SEEG electrodes during 26 operations. The accuracy of 3D reconstruction was 0.8 mm (range, 0-2 mm) with a precision of 1.5 mm. The accuracy of 3D SEEG neuronavigation was 2.68 mm (range, 0-6 mm). The precision of 3D neuronavigation was 1.48 mm.

CONCLUSION

3D neuronavigation for SEEG-guided epilepsy surgery using free software for post-processing of common MRI sequences is possible and a reliable method even with navigation systems without a brain extraction tool.

Clinical Application of an Open-Source 3D Volume Rendering Software to Neurosurgical Approaches

BACKGROUND

Preoperative recognition of the anatomic individualities of each patient can help to achieve more precise and less invasive approaches. It also may help to anticipate potential complications and intraoperative difficulties. Here we describe the use, accuracy, and precision of a free tool for planning microsurgical approaches using 3-dimensional (3D) reconstructions from magnetic resonance imaging (MRI).

METHODS

We used the 3D volume rendering tool of a free open-source software program for 3D reconstruction of images of surgical sites obtained by MRI volumetric acquisition. We recorded anatomic reference points, such as the sulcus and gyrus, and vascularization patterns for intraoperative localization of lesions. Lesion locations were confirmed during surgery by intraoperative ultrasound and/or electrocorticography and later by postoperative MRI.

RESULTS

Between August 2015 and September 2016, a total of 23 surgeries were performed using this technique for 9 low-grade gliomas, 7 high-grade gliomas, 4 cortical dysplasias, and 3 arteriovenous malformations. The technique helped delineate lesions with an overall accuracy of 2.6 ± 1.0 mm. 3D reconstructions were successfully performed in all patients, and images showed sulcus, gyrus, and venous patterns corresponding to the intraoperative images. All lesion areas were confirmed both intraoperatively and at the postoperative evaluation.

CONCLUSIONS

With the technique described herein, it was possible to successfully perform 3D reconstruction of the cortical surface. This reconstruction tool may serve as an adjunct to neuronavigation systems or may be used alone when such a system is unavailable.

OsiriX software as a preoperative planning tool in cranial neurosurgery: A step-by-step guide for neurosurgical residents

Background:

OsiriX (Pixmeo, Switzerland) is an open-source Digital Imaging and Communications in Medicine (DICOM) viewer that is gaining more and more attention in the neurosurgical community because of its user-friendly interface, powerful three-dimensional (3D) volumetric rendering capabilities, and various options for data integration. This paper presents in detail the use of OsiriX software as a preoperative planning tool in cranial neurosurgery.

Methods:

In January 2013, OsiriX software was introduced into our clinical practice as a preoperative planning tool. Its capabilities are being evaluated on an ongoing basis in routine elective cranial cases.

Results:

The program has proven to be highly effective at volumetrically representing data from radiological examinations in 3D. Among its benefits in preoperative planning are simulating the position and exact location of the lesion in 3D, tailoring the skin incision and craniotomy bone flap, enhancing the representation of normal and pathological anatomy, and aiding in planning the reconstruction of the affected area.

Conclusion:

OsiriX is a useful tool for preoperative planning and visualization in neurosurgery. The software greatly facilitates the surgeon's understanding of the relationship between normal and pathological anatomy and can be used as a teaching tool.

Brain shift in neuronavigation of brain tumors: A review

PURPOSE

Neuronavigation based on preoperative imaging data is a ubiquitous tool for image guidance in neurosurgery. However, it is rendered unreliable when brain shift invalidates the patient-to-image registration. Many investigators have tried to explain, quantify, and compensate for this phenomenon to allow extended use of neuronavigation systems for the duration of surgery. The purpose of this paper is to present an overview of the work that has been done investigating brain shift.

METHODS

A review of the literature dealing with the explanation, quantification and compensation of brain shift is presented. The review is based on a systematic search using relevant keywords and phrases in PubMed. The review is organized based on a developed taxonomy that classifies brain shift as occurring due to physical, surgical or biological factors.

RESULTS

This paper gives an overview of the work investigating, quantifying, and compensating for brain shift in neuronavigation while describing the successes, setbacks, and additional needs in the field. An analysis of the literature demonstrates a high variability in the methods used to quantify brain shift as well as a wide range in the measured magnitude of the brain shift, depending on the specifics of the intervention. The analysis indicates the need for additional research to be done in quantifying independent effects of brain shift in order for some of the state of the art compensation methods to become useful.

CONCLUSION

This review allows for a thorough understanding of the work investigating brain shift and introduces the needs for future avenues of investigation of the phenomenon.

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

PURPOSE

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.

METHOD

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.

RESULTS

The minimum and maximum bias errors were 0.32 and 0.61 mm, respectively. The minimum and maximum precision errors were 0.025 and 0.30 mm, respectively. Quantitative validation with porcine brains showed that the average RMSE and TRE were 0.1 and 0.9 mm, respectively.

CONCLUSION

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.

A hybrid method for non-rigid registration of intra-operative ultrasound images with pre-operative MR images

In recent years intra-operative ultrasound images have been used for many procedures in neurosurgery. The registration of intra-operative ultrasound images with preoperative magnetic resonance images is still a challenging problem. In this study a new hybrid method based on residual complexity is proposed for this problem. A new two stages method based on the matching echogenic structures such as sulci is achieved by optimizing the residual complexity (RC) value with quantized coefficients between the ultrasound image and the probabilistic map of MR image. The proposed method is a compromise between feature-based and intensity-based approaches. The evaluation is performed on both a brain phantom and patient data set. The results of the phantom data set confirmed that the proposed method outperforms the accuracy of conventional RC by 39%. Also the mean of fiducial registration errors reached to 1.45, 1.94 mm when the method was applied on phantom and clinical data set, respectively. This hybrid method based on RC enables non-rigid multimodal image registration in a computational time compatible with clinical use as well as being accurate.

Three-dimensional reconstruction of the topographical cerebral surface anatomy for presurgical planning with free OsiriX Software

BACKGROUND

During surgery for intrinsic brain lesions, it is important to distinguish the pathological gyrus from the surrounding normal sulci and gyri. This task is usually tedious because of the pia-arachnoid membranes with their arterial and venous complexes that obscure the underlying anatomy. Moreover, most tumors grow in the white matter without initially distorting the cortical anatomy, making their direct visualization more difficult.

OBJECTIVE

To create and evaluate a simple and free surgical planning tool to simulate the anatomy of the surgical field with and without vessels.

METHODS

We used free computer software (OsiriX Medical Imaging Software) that allowed us to create 3-dimensional reconstructions of the cerebral surface with and without cortical vessels. These reconstructions made use of magnetic resonance images from 51 patients with neocortical supratentorial lesions operated on over a period of 21 months (June 2011 to February 2013). The 3-dimensional (3-D) anatomic images were compared with the true surgical view to evaluate their accuracy. In all patients, the landmarks determined by 3-D reconstruction were cross-checked during surgery with high-resolution ultrasonography; in select cases, they were also checked with indocyanine green videoangiography.

RESULTS

The reconstructed neurovascular structures were confirmed intraoperatively in all patients. We found this technique to be extremely useful in achieving pure lesionectomy, as it defines tumor's borders precisely.

CONCLUSION

A 3-D reconstruction of the cortical surface can be easily created with free OsiriX software. This technique helps the surgeon perfect the mentally created 3-D picture of the tumor location to carry out cleaner, safer surgeries.

A projected landmark method for reduction of registration error in image-guided surgery systems

PURPOSE

Image-guided surgery systems are limited by registration error, so practical and effective methods to improve accuracy are necessary. A projection point-based method for reducing the surface registration error in image-guided surgery was developed and tested.

METHODS

Checkerboard patterns are projected on visible surfaces to create projected landmarks over a region of interest. Surface information thus becomes available in the form of point clouds of surface point coordinates with submillimeter resolution. The reconstructed 3D point cloud is registered using iterative closest point (ICP) approximation to a 3D point cloud extracted from preoperative CT images of the same region of interest. The projected landmark surface registration method was compared with two other methods using a facial surface phantom: (a) landmark registration using anatomical features, and (b) surface matching based on an additional 40 surface points.

RESULTS

The mean error for the projected landmark surface registration method was 0.64 mm, which was 47.4 and 35.3 % lower relative to mean errors of the anatomical landmark registration and the surface-matching methods, respectively. After applying the proposed method, using target registration error as a gold standard, the resulting mean error was 1.1 mm or a reduction of 61.2 % compared to the anatomical landmark registration.

CONCLUSION

Optical checkerboard pattern projection onto visible surfaces was used to acquire surface point clouds for image-guided surgery registration. A projected landmark method eliminated the effects of unwanted and overlapping points by acquiring the desired points at specific locations. The results were more accurate than conventional landmark or surface registration.

Liver deformation in an animal model due to pneumoperitoneum assessed by a vessel-based deformable registration

PURPOSE

Surgical navigation based on preoperative images partly overcomes some of the drawbacks of minimally invasive interventions - reduction of free sight, lack of dexterity and tactile feedback. The usefulness of preoperative images is limited in laparoscopic liver surgery, as the liver shifts due to respiration, induction of pneumoperitoneum and surgical manipulation. In this study, we evaluated the shift and deformation in an animal liver caused by respiration and pneumopertioneum using intraoperative cone beam CT.

MATERIAL AND METHODS

3D cone beam CT scans were acquired with arterial contrast. The centerlines of the segmented vessels were extracted from the images taken at different respiration and pressure settings. A non-rigid registration method was used to measure the shift and deformation. The mean Euclidean distance between the annotated landmarks was used for evaluation.

RESULTS

A shift and deformation of 44.6 mm on average was introduced due to the combined effect of respiration and pneumoperitoneum. On average 91% of the deformations caused by the respiration and pneumoperitoneum were recovered.

CONCLUSION

The results can contribute to the use of intraoperative imaging to correct for anatomic shift so that preoperative data can be used with greater confidence and accuracy during guidance of laparoscopic liver procedures.

Three-dimensional brain surface visualization for epilepsy surgery of focal cortical dysplasia

Focal cortical dysplasia (FCD) causes medically intractable seizures in 5-10% of adult epilepsy patients, but patients can become seizure free through surgical resection. The authors present the utility of three-dimensional surface visualization (3DSV) that expands on existing imaging datasets to highlight surface vasculature as a tool for achieving more successful resections in patients with FCD. In this prospective series of six patients, preoperative 3DSV was performed for planning the surgical approach to the lesion and for intraoperative guidance. Reconstructions involved volume rendering of a contrast-enhanced dataset to visualize surface venous vasculature. Postoperatively, five of the six patients had complete resections, with one patient having a subtotal resection due to proximity to crucial vasculature. We report that 3DSV is a useful tool for surgical planning, since topographical relationships between lesion location and surface vasculature landmarks are less likely to change with surgical progress.

Volumetric intraoperative brain deformation compensation: model development and phantom validation

During neurosurgery, nonrigid brain deformation may affect the reliability of tissue localization based on preoperative images. To provide accurate surgical guidance in these cases, preoperative images must be updated to reflect the intraoperative brain. This can be accomplished by warping these preoperative images using a biomechanical model. Due to the possible complexity of this deformation, intraoperative information is often required to guide the model solution. In this paper, a linear elastic model of the brain is developed to infer volumetric brain deformation associated with measured intraoperative cortical surface displacement. The developed model relies on known material properties of brain tissue, and does not require further knowledge about intraoperative conditions. To provide an initial estimation of volumetric model accuracy, as well as determine the model's sensitivity to the specified material parameters and surface displacements, a realistic brain phantom was developed. Phantom results indicate that the linear elastic model significantly reduced localization error due to brain shift, from > 16 mm to under 5 mm, on average. In addition, though in vivo quantitative validation is necessary, preliminary application of this approach to images acquired during neocortical epilepsy cases confirms the feasibility of applying the developed model to in vivo data.

Validation of a hybrid Doppler ultrasound vessel-based registration algorithm for neurosurgery

PURPOSE

We describe and validate a novel hybrid nonlinear vessel registration algorithm for intra-operative updating of preoperative magnetic resonance (MR) images using Doppler ultrasound (US) images acquired on the dura for the correction of brain-shift and registration inaccuracies. We also introduce an US vessel appearance simulator that generates vessel images similar in appearance to that acquired with US from MR angiography data.

METHODS

Our registration uses the minimum amount of preprocessing to extract vessels from the raw volumetric images. This prevents the removal of important registration information and minimizes the introduction of artifacts that may affect robustness, while reducing the amount of extraneous information in the image to be processed, thus improving the convergence speed of the algorithm. We then completed 3 rounds of validation for our vessel registration method for robustness and accuracy using (i) a large number of synthetic trials generated with our US vessel simulator, (ii) US images acquired from a real physical phantom made from polyvinyl alcohol cryogel, and (iii) real clinical data gathered intra-operatively from 3 patients.

RESULTS

Resulting target registration errors (TRE) of less than 2.5 mm are achieved in more than 90 % of the synthetic trials when the initial TREs are less than 20 mm. TREs of less than 2 mm were achieved when the technique was applied to the physical phantom, and TREs of less than 3 mm were achieved on clinical data.

CONCLUSIONS

These test trials show that the proposed algorithm is not only accurate but also highly robust to noise and missing vessel segments when working with US images acquired in a wide range of real-world conditions.

Tracking of vessels in intra-operative microscope video sequences for cortical displacement estimation

This article presents a method designed to automatically track cortical vessels in intra-operative microscope video sequences. The main application of this method is the estimation of cortical displacement that occurs during tumor resection procedures. The method works in three steps. First, models of vessels selected in the first frame of the sequence are built. These models are then used to track vessels across frames in the video sequence. Finally, displacements estimated using the vessels are extrapolated to the entire image. The method has been tested retrospectively on images simulating large displacement, tumor resection, and partial occlusion by surgical instruments and on 21 video sequences comprising several thousand frames acquired from three patients. Qualitative results show that the method is accurate, robust to the appearance and disappearance of surgical instruments, and capable of dealing with large differences in images caused by resection. Quantitative results show a mean vessel tracking error (VTE) of 2.4 pixels (0.3 or 0.6 mm, depending on the spatial resolution of the images) and an average target registration error (TRE) of 3.3 pixels (0.4 or 0.8 mm).

Image-guided intraoperative cortical deformation recovery using game theory: application to neocortical epilepsy surgery

During neurosurgery, nonrigid brain deformation prevents preoperatively-acquired images from accurately depicting the intraoperative brain. Stereo vision systems can be used to track intraoperative cortical surface deformation and update preoperative brain images in conjunction with a biomechanical model. However, these stereo systems are often plagued with calibration error, which can corrupt the deformation estimation. In order to decouple the effects of camera calibration from the surface deformation estimation, a framework that can solve for disparate and often competing variables is needed. Game theory, which was developed to handle decision making in this type of competitive environment, has been applied to various fields from economics to biology. In this paper, game theory is applied to cortical surface tracking during neocortical epilepsy surgery and used to infer information about the physical processes of brain surface deformation and image acquisition. The method is successfully applied to eight in vivo cases, resulting in an 81% decrease in mean surface displacement error. This includes a case in which some of the initial camera calibration parameters had errors of 70%. Additionally, the advantages of using a game theoretic approach in neocortical epilepsy surgery are clearly demonstrated in its robustness to initial conditions.

A surface registration method for quantification of intraoperative brain deformations in image-guided neurosurgery

Intraoperative brain deformations decrease accuracy in image-guided neurosurgery. Approaches to quantify these deformations based on 3-D reconstruction of cortectomy surfaces have been described and have shown promising results regarding the extrapolation to the whole brain volume using additional prior knowledge or sparse volume modalities. Quantification of brain deformations from surface measurement requires the registration of surfaces at different times along the surgical procedure, with different challenges according to the patient and surgical step. In this paper, we propose a new flexible surface registration approach for any textured point cloud computed by stereoscopic or laser range approach. This method includes three terms: the first term is related to image intensities, the second to Euclidean distance, and the third to anatomical landmarks automatically extracted and continuously tracked in the 2-D video flow. Performance evaluation was performed on both phantom and clinical cases. The global method, including textured point cloud reconstruction, had accuracy within 2 mm, which is the usual rigid registration error of neuronavigation systems before deformations. Its main advantage is to consider all the available data, including the microscope video flow with higher temporal resolution than previously published methods.

Semiautomatic registration of pre- and postbrain tumor resection laser range data: method and validation

This paper presents a semiautomatic method for the registration of images acquired during surgery with a tracked laser range scanner (LRS). This method, which relies on the registration of vessels that can be visualized in the pre- and the postresection images, is a component of a larger system designed to compute brain shift that occurs during tumor resection cases. Because very large differences between pre- and postresection images are typically observed, the development of fully automatic methods to register these images is difficult. The method presented herein is semiautomatic and requires only the identification of a number of points along the length of the vessels. Vessel segments joining these points are then automatically identified using an optimal path finding algorithm that relies on intensity features extracted from the images. Once vessels are identified, they are registered using a robust point-based nonrigid registration algorithm. The transformation computed with the vessels is then applied to the entire image. This permits establishment of a complete correspondence between the pre- and post-3-D LRS data. Experiments show that the method is robust to operator errors in localizing homologous points and a quantitative evaluation performed on ten surgical cases shows submillimetric registration accuracy.

Laser range scanning for image-guided neurosurgery: investigation of image-to-physical space registrations

In this article a comprehensive set of registration methods is utilized to provide image-to-physical space registration for image-guided neurosurgery in a clinical study. Central to all methods is the use of textured point clouds as provided by laser range scanning technology. The objective is to perform a systematic comparison of registration methods that include both extracranial (skin marker point-based registration (PBR), and face-based surface registration) and intracranial methods (feature PBR, cortical vessel-contour registration, a combined geometry/intensity surface registration method, and a constrained form of that method to improve robustness). The platform facilitates the selection of discrete soft-tissue landmarks that appear on the patient's intraoperative cortical surface and the preoperative gadolinium-enhanced magnetic resonance (MR) image volume, i.e., true corresponding novel targets. In an 11 patient study, data were taken to allow statistical comparison among registration methods within the context of registration error. The results indicate that intraoperative face-based surface registration is statistically equivalent to traditional skin marker registration. The four intracranial registration methods were investigated and the results demonstrated a target registration error of 1.6 +/- 0.5 mm, 1.7 +/- 0.5 mm, 3.9 +/- 3.4 mm, and 2.0 +/- 0.9 mm, for feature PBR, cortical vessel-contour registration, unconstrained geometric/intensity registration, and constrained geometric/intensity registration, respectively. When analyzing the results on a per case basis, the constrained geometric/intensity registration performed best, followed by feature PBR, and finally cortical vessel-contour registration. Interestingly, the best target registration errors are similar to targeting errors reported using bone-implanted markers within the context of rigid targets. The experience in this study as with others is that brain shift can compromise extracranial registration methods from the earliest stages. Based on the results reported here, organ-based approaches to registration would improve this, especially for shallow lesions.

Clinical validation of vessel-based registration for correction of brain-shift

In this paper, we have tested and validated a vessel-based registration technique for correction of brain-shift using retrospective clinical data from five patients: three patients with brain tumors, one patient with an aneurysm and one patient with an arteriovenous malformation. The algorithm uses vessel centerlines extracted from segmented pre-operative MRA data and intra-operative power Doppler ultrasound images to compute first a linear fit and then a thin-plate spline transform in order to achieve non-linear registration. The method was validated using (i) homologous landmarks identified in the original data, (ii) selected vessels, excluded from the fitting procedure and (iii) manually segmented, non-vascular structures. The tracking of homologous landmarks show that we are able to correct the deformation to within 1.25 mm, and the validation using excluded vessels and anatomical structures show an accuracy of 1mm. Pre-processing of the data can be completed in 30 s per dataset, and registrations can be performed in less than 30s. This makes the technique well suited for intra-operative use.

Image-to-patient registration techniques in head surgery

Frame-based stereotaxy was developed in neurosurgery at the beginning of the last century, evolving from atlas-based stereotaxy to stereotaxy based on the individual patient's image data. This established method is still in use in neurosurgery and radiotherapy. There have since been two main developments based on this concept: frameless stereotaxy and markerless registration. Frameless stereotactic systems ('navigation systems') replaced the cumbersome stereotactic frame by mechanically and later also optically or magnetically tracked instruments. Stereotaxy based on the individual patient's image data introduced the problem of patient-to-image data registration. The development of navigation systems based on frameless stereotaxy has dramatically increased its use in surgical disciplines other than neurosurgery, but image-guided surgery based on fiducial marker registration needs dedicated imaging for registration purposes, in addition to the diagnostic imaging that might have been performed. Markerless registration techniques can overcome the resulting additional cost and effort, and result in more widespread use of image-guided surgery techniques. In this review paper, the developments that led to today's navigation systems are outlined, and the applications and possibilities of these methods in the field of maxillofacial surgery are presented.

A comprehensive system for intraoperative 3D brain deformation recovery

During neurosurgery, brain deformation renders preoperative images unreliable for localizing pathologic structures. In order to visualize the current brain anatomy, it is necessary to nonrigidly warp these preoperative images to reflect the intraoperative brain. This can be accomplished using a biomechanical model driven by sparse intraoperative information. In this paper, a linear elastic model of the brain is developed which can infer volumetric brain deformation given the cortical surface displacement. This model was tested on both a realistic brain phantom and in vivo, proving its ability to account for large brain deformations. Also, an efficient semiautomatic strategy for preoperative cortical feature detection is outlined, since accurate segmentation of cortical features can aid intraoperative cortical surface tracking.

Intraoperative cortical surface characterization using laser range scanning: preliminary results

OBJECTIVE

To present a novel methodology that uses a laser range scanner (LRS) capable of generating textured (intensity-encoded) surface descriptions of the brain surface for use with image-to-patient registration and improved cortical feature recognition during intraoperative neurosurgical navigation.

METHODS

An LRS device was used to acquire cortical surface descriptions of eight patients undergoing neurosurgery for a variety of clinical presentations. Textured surface descriptions were generated from these intraoperative acquisitions for each patient. Corresponding textured surfaces were also generated from each patient's preoperative magnetic resonance tomograms. Each textured surface pair (LRS and magnetic resonance tomogram) was registered using only cortical surface information. Novel visualization of the combined surfaces allowed for registration assessment based on quantitative cortical feature alignment.

RESULTS

Successful textured LRS surface acquisition and generation was performed on all eight patients. The data acquired by the LRS accurately presented the intraoperative surface of the cortex and the associated features within the surgical field-of-view. Registration results are presented as overlays of the intraoperative data with respect to the preoperative data and quantified by comparing mean distances between cortical features on the magnetic resonance tomogram and LRS surfaces after registration. The overlays demonstrated that accurate registration can be provided between the preoperative and intraoperative data and emphasized a potential enhancement to cortical feature recognition within the operating room environment. Using the best registration result from each clinical case, the mean feature alignment error is 1.7 +/- 0.8 mm over all cases.

CONCLUSION

This study demonstrates clinical deployment of an LRS capable of generating textured surfaces of the surgical field of view. Data from the LRS was registered accurately to the corresponding preoperative data. Visual inspection of the registration results was provided by overlays that put the intraoperative data within the perspective of the whole brain's surface. These visuals can be used to more readily assess the fidelity of image-to-patient registration, as well as to enhance recognition of cortical features for assistance in comparing the neurotopography between magnetic resonance image volume and physical patient. In addition, the feature-rich data presented here provides considerable motivation for using LRS scanning to measure deformation during surgery.

Use of a volumetric target for image-guided surgery

A VIRTUAL REALITY system has been devised to superimpose a computer-generated rendering of a volumetric target to be surgically approached or resected on a real-time video image of the surgical field. A stereotactic frame is used to register the image from the video camera with the image of the target volume for accurate localization. The volumetric target is obtained from preoperative imaging studies and can be modified to adjust the intended line of resection or to avoid eloquent vascular or neural tissue. The computer-generated image is updated throughout surgery to visualize only that part of the tumor under resection so the surgeon may guide the resection along the border of the mass or intended preplanned line of resection. To date, 74 intracranial tumor resections have been performed under video virtual reality guidance. Postoperative scanning corresponds in every case with preoperative planning. This system is also designed to be adapted to frameless guidance, which can be further enhanced by the incorporation of an audible tone to signal the relationship of the tip of the resection instrument to the line of resection.

Nonrigid 3D brain registration using intensity/feature information

The brain deforms non-rigidly during neurosurgery, preventing preoperatively acquired images from accurately depicting the intraoperative brain. If the deformed brain surface can be detected, biomechanical models can be applied to calculate the resulting volumetric deformation. The reliability of this volumetric calculation is dependent on the accuracy of the surface detection. This work presents a surface tracking algorithm which relies on Bayesian analysis to track cortical surface movement. The inputs to the model are 3D preoperative brain images and intraoperative stereo camera images. The addition of a camera calibration optimization term creates a more robust model, capable of tracking the cortical surface in the presence of camera calibration error.

Strategies for brain shift evaluation

For the analysis of the brain shift phenomenon different strategies were applied. In 32 glioma cases pre- and intraoperative MR datasets were acquired in order to evaluate the maximum displacement of the brain surface and the deep tumor margin. After rigid registration using the software of the neuronavigation system, a direct comparison was made with 2D- and 3D visualizations. As a result, a great variability of the brain shift was observed ranging up to 24 mm for cortical displacement and exceeding 3 mm for the deep tumor margin in 66% of all cases. Following intraoperative imaging the neuronavigation system was updated in eight cases providing reliable guidance. For a more comprehensive analysis a voxel-based nonlinear registration was applied. Aiming at improved speed of alignment we performed all interpolation operations with 3D texture mapping based on OpenGL functions supported in graphics hardware. Further acceleration was achieved with an adaptive refinement of the underlying control point grid focusing on the main deformation areas. For a quick overview the registered datasets were evaluated with different 3D visualization approaches. Finally, the results were compared to the initial measurements contributing to a better understanding of the brain shift phenomenon. Overall, the experiments clearly demonstrate that deformations of the brain surface and deeper brain structures are uncorrelated.

A review on MR vascular image processing algorithms: acquisition and prefiltering: part I

Vascular segmentation has recently been given much attention. This review paper has two parts. Part I focuses on the physics of magnetic resonance angiography (MRA) generation and prefiltering techniques applied to MRA data sets. Part II of the review focuses on the vessel segmentation algorithms. The first section of this paper introduces the five different sets of receive coils used with the MRI system for magnetic resonance angiography data acquisition. This section then presents the five different types of the most popular data acquisition techniques: time-of-flight (TOF), phase-contrast, contrast-enhanced, black-blood, T2-weighted, and T2*-weighted, along with their pros and cons. Section II of this paper focuses on prefiltering algorithms for MRA data sets. This is necessary for removing the background nonvascular structures in the MRA data sets. Finally, the paper concludes with a clinical discussion on the challenges and the future of the data acquisition and the automated filtering algorithms.

Virtual Reality Neurosurgery: A Simulator Blueprint

OBJECTIVE

This article details preliminary studies undertaken to integrate the most relevant advancements across multiple disciplines in an effort to construct a highly realistic neurosurgical simulator based on a distributed computer architecture. Techniques based on modified computational modeling paradigms incorporating finite element analysis are presented, as are current and projected efforts directed toward the implementation of a novel bidirectional haptic device.

METHODS

Patient-specific data derived from noninvasive magnetic resonance imaging sequences are used to construct a computational model of the surgical region of interest. Magnetic resonance images of the brain may be coregistered with those obtained from magnetic resonance angiography, magnetic resonance venography, and diffusion tensor imaging to formulate models of varying anatomic complexity.

RESULTS

The majority of the computational burden is encountered in the presimulation reduction of the computational model and allows realization of the required threshold rates for the accurate and realistic representation of real-time visual animations.

CONCLUSION

Intracranial neurosurgical procedures offer an ideal testing site for the development of a totally immersive virtual reality surgical simulator when compared with the simulations required in other surgical subspecialties. The material properties of the brain as well as the typically small volumes of tissue exposed in the surgical field, coupled with techniques and strategies to minimize computational demands, provide unique opportunities for the development of such a simulator. Incorporation of real-time haptic and visual feedback is approached here and likely will be accomplished soon.

A Noncontacting 3-D Digitizer for Use in Image-Guided Neurosurgery

Current neuronavigational systems face two primary challenges: (1) automatic and robust registration between preoperative images and the operating room space, and (2) compensation for brain deformations that compromise the accuracy of the initial registration. To contend with these difficulties, we firstly estimate the three-dimensional (3-D) structure of the cortical surface using a noncontacting 3-D digitizer. This 3-D structure is then used to establish the initial registration, and to update the preoperative MR volume as the brain deforms. We show that this approach improves the accuracy of registration in a phantom study, and demonstrate the ability to capture cortical motion in six clinical cases.

Cortical surface registration for image-guided neurosurgery using laser-range scanning

In this paper, a method of acquiring intraoperative data using a laser range scanner (LRS) is presented within the context of model-updated image-guided surgery. Registering textured point clouds generated by the LRS to tomographic data is explored using established point-based and surface techniques as well as a novel method that incorporates geometry and intensity information via mutual information (SurfaceMI). Phantom registration studies were performed to examine accuracy and robustness for each framework. In addition, an in vivo registration is performed to demonstrate feasibility of the data acquisition system in the operating room. Results indicate that SurfaceMI performed better in many cases than point-based (PBR) and iterative closest point (ICP) methods for registration of textured point clouds. Mean target registration error (TRE) for simulated deep tissue targets in a phantom were 1.0 +/- 0.2, 2.0 +/- 0.3, and 1.2 +/- 0.3 mm for PBR, ICP, and SurfaceMI, respectively. With regard to in vivo registration, the mean TRE of vessel contour points for each framework was 1.9 +/- 1.0, 0.9 +/- 0.6, and 1.3 +/- 0.5 for PBR, ICP, and SurfaceMI, respectively. The methods discussed in this paper in conjunction with the quantitative data provide impetus for using LRS technology within the model-updated image-guided surgery framework.

Image-guided craniotomy for cerebral metastases: techniques and outcomes

OBJECTIVE

The purpose of the present study was to analyze the outcomes after craniotomies for brain metastases in a modern series using image-guided technologies either in the regular operating room or in the intraoperative magnetic resonance imaging unit.

METHODS

Neurosurgical outcomes were analyzed for 49 patients who underwent 55 image-guided craniotomies for excision of brain metastases during a 5-year period. Tumors were located in critical and noncritical function regions of the brain. A total of 23 craniotomies for tumors in critical brain were performed using intravenous sedation anesthesia; craniotomies for noncritical function brain regions were completed under general anesthesia. The patients were also divided into Radiation Therapy Oncology Group recursive partitioning analysis (RPA) classes on the basis of age, Karnofsky Performance Scale scores, state of primary disease, and presence or absence of extracranial metastases.

RESULTS

There was no perioperative mortality. Gross total resection, as verified by postoperative contrast-enhanced computed tomography or magnetic resonance imaging, was achieved in 96% of patients. The median anesthesia time was 4.25 hours, and the median length of hospital stay was 3 days. In 51 symptomatic cases, there was complete resolution of symptoms in 70% (n = 36), improvement in 14% (n = 7), and no change in 12% (n = 6) postoperatively. No patient who was neurologically intact preoperatively deteriorated after surgery, and 93% of patients maintained or improved their functional status. Only two patients (3.6%) with significant preoperative deficits had increased long-term deficits postoperatively. The mean follow-up was 1 year, and the local recurrence rate was 16%. The median survival of the entire group was 16.23 months (17.5 mo in RPA Class I, 22.9 mo in RPA Class II, and 9.8 mo in RPA Class III).

CONCLUSION

Gross total resection of brain metastases, including those involving critical function areas, can be safely achieved with a low morbidity rate using contemporary image-guided systems. RPA Class I and II patients with controlled primary disease benefit from aggressive treatment by surgery and radiation.

Modification of reference array attachment for image guided neurosurgery: direct cranial fixation

We have modified the cranial fixation technique of the reference array used for the Stealth (Medtronics Inc., Minneapolis, MN) image guided neurosurgical workstation to avoid rigid immobilization and to accommodate patients undergoing awake procedures. The modification allows attachment of a reference array directly to the skull prior to registration, avoiding the requirement for rigid cranial fixation. The accuracy of fiducial registration for the modified reference array was compared to the conventional reference array using a phantom system yielding similar registration results and target accuracy. The successful application of the modified system to two operative cases is described.

Integration of sulcal and functional information for multimodal neuronavigation

OBJECT

The authors present the use of cortical sulci, segmented from magnetic resonance (MR) imaging, and functional data from functional (f)MR imaging and magnetoencephalography (MEG) in the image-guided surgical management of lesions adjacent to the sensorimotor cortex.

METHODS

In an initial set of 11 patients, sulci near lesions were automatically segmented from MR imaging data sets, then MEG and fMR imaging examinations were performed. Relevant functional information was preoperatively interpreted and selected from MEG and fMR imaging and subsequently transferred to the navigation system for selected sulci. A neuronavigation system consisting of a surgical microscope with enhanced reality overlay display was used. Data were displayed as contours on the cut-plane images of a stereotactic workstation and as contours on the overlay screen of the head-up display within the optical path of the right eyepiece of the surgical microscope.

CONCLUSIONS

This method, in which both sulcal and functional mapping are used for surgery planning and neuronavigation, provides helpful information. It is a promising procedure for the treatment of patients who harbor lesions in areas around the eloquent cortex.

The process and development of image-guided procedures

Medical imaging has been used primarily for diagnosis. In the past 15 years there has been an emergence of the use of images for the guidance of therapy. This process requires three-dimensional localization devices, the ability to register medical images to physical space, and the ability to display position and trajectory on those images. This paper examines the development and state of the art in those processes.

Integration of interventional MRI with computer-assisted surgery

Interventional MRI (IMRI) has entered into a new stage in which computer-based techniques play an increasing role in planning, monitoring, and controlling the procedures. The use of interactive imaging, navigational image guidance techniques, and image processing methods is demonstrated in various applications. The integration of intraoperative MRI guidance and computer-assisted surgery will greatly accelerate the clinical utility of image-guided therapy in general and interventional MRI in particular. J. Magn. Reson. Imaging 2001;13:69-77.

Virtual laryngoscopy

Virtual endoscopy enables computer-generated 3-dimensional visualization of a cavity by reconstructing 2-dimensional computed tomographic or magnetic resonance data. The technique has been used experimentally to study the colon, bronchi, ears, and other structures. Here, virtual laryngoscopies were created from the cross-sectional image data of 3 patients. The cases represented a normal airway, a squamous cell carcinoma of the glottic fold, and a posterior glottic stenosis. These reconstructions included extraluminal anatomy that is not typical of current virtual endoscopic techniques. The 2-dimensional computed tomographic and magnetic resonance images of the patients underwent post-processing for 3-dimensional reconstruction. The resulting models were imported into an experimental virtual endoscopy program for 1) airway lumen generation and 2) interactive viewing. Though they could not be used for biopsy, the virtual laryngoscopies provided, in a noninvasive fashion, good simulation of endoscopy. Virtual endoscopy also gave the added benefits of the ability to assess the transmural extent of disease and view the airway distal to areas of luminal compromise. This technology may well provide clinical benefit in preoperative planning, staging, and intraprocedural guidance for head and neck disease and merits further study.