Cortical Surface Tracking Using a Stereoscopic Operating Microscope

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.

Stereovision Co-Registration in Image-Guided Spinal Surgery: Accuracy Assessment Using Explanted Porcine Spines

BACKGROUND

Current methods of spine registration for image guidance have a variety of limitations related to accuracy, efficiency, and cost.

OBJECTIVE

To define the accuracy of stereovision-mediated co-registration of a spinal surgical field.

METHODS

A total of 10 explanted porcine spines were used. Dorsal soft tissue was removed to a variable degree. Bone screw fiducials were placed in each spine and high-resolution computed tomography (CT) scanning performed. Stereoscopic images were then obtained using a tracked, calibrated stereoscopic camera system; images were processed, reconstructed, and segmented in a semi-automated manner. A multistart registration of the reconstructed spinal surface with preoperative CT was performed. Target registration error (TRE) in the region of the laminae and facets was then determined, using bone screw fiducials not included in the original registration process. Each spine also underwent multilevel laminectomy, and TRE was then recalculated for varying amounts of bone removal.

RESULTS

The mean TRE of stereovision registration was 2.19 ± 0.69 mm when all soft tissue was removed and 2.49 ± 0.74 mm when limited soft tissue removal was performed. Accuracy of the registration process was not adversely affected by laminectomy.

CONCLUSION

Stereovision offers a promising means of registering an open, dorsal spinal surgical field. In this study, overall mean accuracy of the registration was 2.21 mm, even when bony anatomy was partially obscured by soft tissue or when partial midline laminectomy had been performed.

Use of Stereovision for Intraoperative Coregistration of a Spinal Surgical Field: A Human Feasibility Study

BACKGROUND

The use of image guidance during spinal surgery has been limited by several anatomic factors such as intervertebral segment motion and ineffective spine immobilization. In its current form, the surgical field is coregistered with a preoperative computed tomography (CT), often obtained in a different spinal confirmation, or with intraoperative cross-sectional imaging. Stereovision offers an alternative method of registration.

OBJECTIVE

To demonstrate the feasibility of stereovision-mediated coregistration of a human spinal surgical field using a proof-of-principle study, and to provide preliminary assessments of the technique's accuracy.

METHODS

A total of 9 subjects undergoing image-guided pedicle screw placement also underwent stereovision-mediated coregistration with preoperative CT imaging. Stereoscopic images were acquired using a tracked, calibrated stereoscopic camera system mounted on an operating microscope. Images were processed, reconstructed, and segmented in a semi-automated manner. A multistart registration of the reconstructed spinal surface with preoperative CT was performed. Registration accuracy, measured as surface-to-surface distance error, was compared between stereovision registration and a standard registration.

RESULTS

The mean surface reconstruction error of the stereovision-acquired surface was 2.20 ± 0.89 mm. Intraoperative coregistration with stereovision was performed with a mean error of 1.48 ± 0.35 mm compared to 2.03 ± 0.28 mm using a standard point-based registration method. The average computational time for registration with stereovision was 95 ± 46 s (range 33-184 s) vs 10to 20 min for standard point-based registration.

CONCLUSION

Semi-automated registration of a spinal surgical field using stereovision is possible with accuracy that is at least comparable to current landmark-based techniques.

Utilizing Light-field Imaging Technology in Neurosurgery

Traditional still cameras can only focus on a single plane for each image while rendering everything outside of that plane out of focus. However, new light-field imaging technology makes it possible to adjust the focus plane after an image has already been captured. This technology allows the viewer to interactively explore an image with objects and anatomy at varying depths and clearly focus on any feature of interest by selecting that location during post-capture viewing. These images with adjustable focus can serve as valuable educational tools for neurosurgical residents. We explore the utility of light-field cameras and review their strengths and limitations compared to other conventional types of imaging. The strength of light-field images is the adjustable focus, as opposed to the fixed-focus of traditional photography and video. A light-field image also is interactive by nature, as it requires the viewer to select the plane of focus and helps with visualizing the three-dimensional anatomy of an image. Limitations include the relatively low resolution of light-field images compared to traditional photography and video. Although light-field imaging is still in its infancy, there are several potential uses for the technology to complement traditional still photography and videography in neurosurgical education.

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.

Evaluation of conoscopic holography for estimating tumor resection cavities in model-based image-guided neurosurgery

Surgical navigation relies on accurately mapping the intraoperative state of the patient to models derived from preoperative images. In image-guided neurosurgery, soft tissue deformations are common and have been shown to compromise the accuracy of guidance systems. In lieu of whole-brain intraoperative imaging, some advocate the use of intraoperatively acquired sparse data from laser-range scans, ultrasound imaging, or stereo reconstruction coupled with a computational model to drive subsurface deformations. Some authors have reported on compensating for brain sag, swelling, retraction, and the application of pharmaceuticals such as mannitol with these models. To date, strategies for modeling tissue resection have been limited. In this paper, we report our experiences with a novel digitization approach, called a conoprobe, to document tissue resection cavities and assess the impact of resection on model-based guidance systems. Specifically, the conoprobe was used to digitize the interior of the resection cavity during eight brain tumor resection surgeries and then compared against model prediction results of tumor locations. We should note that no effort was made to incorporate resection into the model but rather the objective was to determine if measurement was possible to study the impact on modeling tissue resection. In addition, the digitized resection cavity was compared with early postoperative MRI scans to determine whether these scans can further inform tissue resection. The results demonstrate benefit in model correction despite not having resection explicitly modeled. However, results also indicate the challenge that resection provides for model-correction approaches. With respect to the digitization technology, it is clear that the conoprobe provides important real-time data regarding resection and adds another dimension to our noncontact instrumentation framework for soft-tissue deformation compensation in guidance systems.

Persistent and automatic intraoperative 3D digitization of surfaces under dynamic magnifications of an operating microscope

One of the major challenges impeding advancement in image-guided surgical (IGS) systems is the soft-tissue deformation during surgical procedures. These deformations reduce the utility of the patient's preoperative images and may produce inaccuracies in the application of preoperative surgical plans. Solutions to compensate for the tissue deformations include the acquisition of intraoperative tomographic images of the whole organ for direct displacement measurement and techniques that combines intraoperative organ surface measurements with computational biomechanical models to predict subsurface displacements. The later solution has the advantage of being less expensive and amenable to surgical workflow. Several modalities such as textured laser scanners, conoscopic holography, and stereo-pair cameras have been proposed for the intraoperative 3D estimation of organ surfaces to drive patient-specific biomechanical models for the intraoperative update of preoperative images. Though each modality has its respective advantages and disadvantages, stereo-pair camera approaches used within a standard operating microscope is the focus of this article. A new method that permits the automatic and near real-time estimation of 3D surfaces (at 1 Hz) under varying magnifications of the operating microscope is proposed. This method has been evaluated on a CAD phantom object and on full-length neurosurgery video sequences (∼1 h) acquired intraoperatively by the proposed stereovision system. To the best of our knowledge, this type of validation study on full-length brain tumor surgery videos has not been done before. The method for estimating the unknown magnification factor of the operating microscope achieves accuracy within 0.02 of the theoretical value on a CAD phantom and within 0.06 on 4 clinical videos of the entire brain tumor surgery. When compared to a laser range scanner, the proposed method for reconstructing 3D surfaces intraoperatively achieves root mean square errors (surface-to-surface distance) in the 0.28-0.81 mm range on the phantom object and in the 0.54-1.35 mm range on 4 clinical cases. The digitization accuracy of the presented stereovision methods indicate that the operating microscope can be used to deliver the persistent intraoperative input required by computational biomechanical models to update the patient's preoperative images and facilitate active surgical guidance.

Intraoperative laser speckle contrast imaging with retrospective motion correction for quantitative assessment of cerebral blood flow

Although multiple intraoperative cerebral blood flow (CBF) monitoring techniques are currently available, a quantitative method that allows for continuous monitoring and that can be easily integrated into the surgical workflow is still needed. Laser speckle contrast imaging (LSCI) is an optical imaging technique with a high spatiotemporal resolution that has been recently demonstrated as feasible and effective for intraoperative monitoring of CBF during neurosurgical procedures. This study demonstrates the impact of retrospective motion correction on the quantitative analysis of intraoperatively acquired LSCI images. LSCI images were acquired through a surgical microscope during brain tumor resection procedures from 10 patients under baseline conditions and after a cortical stimulation in three of those patients. The patient's electrocardiogram (ECG) was recorded during acquisition for postprocess correction of pulsatile artifacts. Automatic image registration was retrospectively performed to correct for tissue motion artifacts, and the performance of rigid and nonrigid transformations was compared. In baseline cases, the original images had [Formula: see text] noise across 16 regions of interest (ROIs). ECG filtering moderately reduced the noise to [Formula: see text], while image registration resulted in a further noise reduction of [Formula: see text]. Combined ECG filtering and image registration significantly reduced the noise to [Formula: see text] ([Formula: see text]). Using the combined motion correction, accuracy and sensitivity to small changes in CBF were improved in cortical stimulation cases. There was also excellent agreement between rigid and nonrigid registration methods (15/16 ROIs with [Formula: see text] difference). Results from this study demonstrate the importance of motion correction for improved visualization of CBF changes in clinical LSCI images.

Near Real-Time Computer Assisted Surgery for Brain Shift Correction Using Biomechanical Models

Conventional image-guided neurosurgery relies on preoperative images to provide surgical navigational information and visualization. However, these images are no longer accurate once the skull has been opened and brain shift occurs. To account for changes in the shape of the brain caused by mechanical (e.g., gravity-induced deformations) and physiological effects (e.g., hyperosmotic drug-induced shrinking, or edema-induced swelling), updated images of the brain must be provided to the neuronavigation system in a timely manner for practical use in the operating room. In this paper, a novel preoperative and intraoperative computational processing pipeline for near real-time brain shift correction in the operating room was developed to automate and simplify the processing steps. Preoperatively, a computer model of the patient's brain with a subsequent atlas of potential deformations due to surgery is generated from diagnostic image volumes. In the case of interim gross changes between diagnosis, and surgery when reimaging is necessary, our preoperative pipeline can be generated within one day of surgery. Intraoperatively, sparse data measuring the cortical brain surface is collected using an optically tracked portable laser range scanner. These data are then used to guide an inverse modeling framework whereby full volumetric brain deformations are reconstructed from precomputed atlas solutions to rapidly match intraoperative cortical surface shift measurements. Once complete, the volumetric displacement field is used to update, i.e., deform, preoperative brain images to their intraoperative shifted state. In this paper, five surgical cases were analyzed with respect to the computational pipeline and workflow timing. With respect to postcortical surface data acquisition, the approximate execution time was 4.5 min. The total update process which included positioning the scanner, data acquisition, inverse model processing, and image deforming was ~11-13 min. In addition, easily implemented hardware, software, and workflow processes were identified for improved performance in the near future.

Integrating Retraction Modeling Into an Atlas-Based Framework for Brain Shift Prediction

In recent work, an atlas-based statistical model for brain shift prediction, which accounts for uncertainty in the intraoperative environment, has been proposed. Previous work reported in the literature using this technique did not account for local deformation caused by surgical retraction. It is challenging to precisely localize the retractor location prior to surgery and the retractor is often moved in the course of the procedure. This paper proposes a technique that involves computing the retractor-induced brain deformation in the operating room through an active model solve and linearly superposing the solution with the precomputed deformation atlas. As a result, the new method takes advantage of the atlas-based framework's accounting for uncertainties while also incorporating the effects of retraction with minimal intraoperative computing. This new approach was tested using simulation and phantom experiments. The results showed an improvement in average shift correction from 50% (ranging from 14 to 81%) for gravity atlas alone to 80% using the active solve retraction component (ranging from 73 to 85%). This paper presents a novel yet simple way to integrate retraction into the atlas-based brain shift computation framework.

Tissue localization during resective epilepsy surgery

Object

Imaging-guided surgery (IGS) systems are widely used in neurosurgical practice. During epilepsy surgery, the authors routinely use IGS landmarks to localize intracranial electrodes and/or specific brain regions. The authors have developed a technique to coregister these landmarks with pre- and postoperative scans and the Montreal Neurological Institute (MNI) standard space brain MRI to allow 1) localization and identification of tissue anatomy; and 2) identification of Brodmann areas (BAs) of the tissue resected during epilepsy surgery. Tracking tissue in this fashion allows for better correlation of patient outcome to clinical factors, functional neuroimaging findings, and pathological characteristics and molecular studies of resected tissue.

Methods

Tissue samples were collected in 21 patients. Coordinates from intraoperative tissue localization were downloaded from the IGS system and transformed into patient space, as defined by preoperative high-resolution T1-weighted MRI volume. Tissue landmarks in patient space were then transformed into MNI standard space for identification of the BAs of the tissue samples.

Results

Anatomical locations of resected tissue were identified from the intraoperative resection landmarks. The BAs were identified for 17 of the 21 patients. The remaining patients had abnormal brain anatomy that could not be meaningfully coregistered with the MNI standard brain without causing extensive distortion.

Conclusions

This coregistration and landmark tracking technique allows localization of tissue that is resected from patients with epilepsy and identification of the BAs for each resected region. The ability to perform tissue localization allows investigators to relate preoperative, intraoperative, and postoperative functional and anatomical brain imaging to better understand patient outcomes, improve patient safety, and aid in research.

Design and evaluation of an optically-tracked single-CCD laser range scanner

PURPOSE

Acquisition of laser range scans of an organ surface has the potential to efficiently provide measurements of geometric changes to soft tissue during a surgical procedure. A laser range scanner design is reported here which has been developed to drive intraoperative updates to conventional image-guided neurosurgery systems.

METHODS

The scanner is optically-tracked in the operating room with a multiface passive target. The novel design incorporates both the capture of surface geometry (via laser illumination) and color information (via visible light collection) through a single-lens onto the same charge-coupled device (CCD). The accuracy of the geometric data was evaluated by scanning a high-precision phantom and comparing relative distances between landmarks in the scans with the corresponding ground truth (known) distances. The range-of-motion of the scanner with respect to the optical camera was determined by placing the scanner in common operating room configurations while sampling the visibility of the reflective spheres. The tracking accuracy was then analyzed by fixing the scanner and phantom in place, perturbing the optical camera around the scene, and observing variability in scan locations with respect to a tracked pen probe ground truth as the camera tracked the same scene from different positions.

RESULTS

The geometric accuracy test produced a mean error and standard deviation of 0.25 ± 0.40 mm with an RMS error of 0.47 mm. The tracking tests showed that the scanner could be tracked at virtually all desired orientations required in the OR set up, with an overall tracking error and standard deviation of 2.2 ± 1.0 mm with an RMS error of 2.4 mm. There was no discernible difference between any of the three faces on the lasers range scanner (LRS) with regard to tracking accuracy.

CONCLUSIONS

A single-lens laser range scanner design was successfully developed and implemented with sufficient scanning and tracking accuracy for image-guided surgery.

Virtual reality augmentation in skull base surgery

OBJECTIVE

Skull base anatomy is complex and subject to individual variation. Understanding the complexity of surgical anatomy is faster and easier with virtual models created from primary imaging data of the patient. This study was designed to investigate the usefulness of virtual reality in image guidance for skull base procedures.

DESIGN

Primary volumetric image data from 110 patients was acquired using magnetic resonance, computed tomography (CT), and CT angiography. Pathologies included lesions in the anterior, middle, and posterior skull base. The data were transferred to an infrared-based image-guidance system for creation of a virtual operating field (VOF) with translucent surface modulation and optional "fly-through" video mode. During surgery, the target registration error for anatomical landmarks was assessed and the VOF was compared with the patient's anatomy in the operative field.

RESULTS

Complex structures like the course of the sigmoid sinus, the carotid artery, and the outline of the paranasal sinuses were well visualized in the VOF and were recognized by the surgeon instantly. Perception was greatly facilitated as compared with routine mental reconstruction of triaxial images. Accurate assessment of the depth of field and very small objects was not possible in VOF images.

CONCLUSION

Supported by sound anatomical knowledge, creation of a virtual operating field for a surgical approach in an individual patient offers a déjà vu experience that can enhance the capabilities of a surgical team in skull base approaches. In addition, application of this technique in image-guided procedures assists in targeting or avoiding hidden anatomical structures.

Intraoperative brain shift compensation: accounting for dural septa

Biomechanical models that describe soft tissue deformation provide a relatively inexpensive way to correct registration errors in image-guided neurosurgical systems caused by nonrigid brain shift. Quantifying the factors that cause this deformation to sufficient precision is a challenging task. To circumvent this difficulty, atlas-based methods have been developed recently that allow for uncertainty, yet still capture the first-order effects associated with deformation. The inverse solution is driven by sparse intraoperative surface measurements, which could bias the reconstruction and affect the subsurface accuracy of the model prediction. Studies using intraoperative MR have shown that the deformation in the midline, tentorium, and contralateral hemisphere is relatively small. The dural septa act as rigid membranes supporting the brain parenchyma and compartmentalizing the brain. Accounting for these structures in models may be an important key to improving subsurface shift accuracy. A novel method to segment the tentorium cerebelli will be described, along with the procedure for modeling the dural septa. Results in seven clinical cases show a qualitative improvement in subsurface shift accuracy making the predicted deformation more congruous with previous observations in the literature. The results also suggest a considerably more important role for hyperosmotic drug modeling for the intraoperative shift correction environment.

A fast and efficient method to compensate for brain shift for tumor resection therapies measured between preoperative and postoperative tomograms

In this paper, an efficient paradigm is presented to correct for brain shift during tumor resection therapies. For this study, high resolution preoperative (pre-op) and postoperative (post-op) MR images were acquired for eight in vivo patients, and surface/subsurface shift was identified by manual identification of homologous points between the pre-op and immediate post-op tomograms. Cortical surface deformation data were then used to drive an inverse problem framework. The manually identified subsurface deformations served as a comparison toward validation. The proposed framework recaptured 85% of the mean subsurface shift. This translated to a subsurface shift error of 0.4 +/- 0.4 mm for a measured shift of 3.1 +/- 0.6 mm. The patient's pre-op tomograms were also deformed volumetrically using displacements predicted by the model. Results presented allow a preliminary evaluation of correction both quantitatively and visually. While intraoperative (intra-op) MR imaging data would be optimal, the extent of shift measured from pre- to post-op MR was comparable to clinical conditions. This study demonstrates the accuracy of the proposed framework in predicting full-volume displacements from sparse shift measurements. It also shows that the proposed framework can be extended and used to update pre-op images on a time scale that is compatible with surgery.

Coregistered fluorescence-enhanced tumor resection of malignant glioma: relationships between δ-aminolevulinic acid-induced protoporphyrin IX fluorescence, magnetic resonance imaging enhancement, and neuropathological parameters. Clinical article

OBJECT

The aim of this study was to investigate the relationships between intraoperative fluorescence, features on MR imaging, and neuropathological parameters in 11 cases of newly diagnosed glioblastoma multiforme (GBM) treated using protoporphyrin IX (PpIX) fluorescence-guided resection.

METHODS

In 11 patients with a newly diagnosed GBM, δ-aminolevulinic acid (ALA) was administered to enhance endogenous synthesis of the fluorophore PpIX. The patients then underwent fluorescence-guided resection, coregistered with conventional neuronavigational image guidance. Biopsy specimens were collected at different times during surgery and assigned a fluorescence level of 0-3 (0, no fluorescence; 1, low fluorescence; 2, moderate fluorescence; or 3, high fluorescence). Contrast enhancement on MR imaging was quantified using two image metrics: 1) Gd-enhanced signal intensity (GdE) on T1-weighted subtraction MR image volumes, and 2) normalized contrast ratios (nCRs) in T1-weighted, postGd-injection MR image volumes for each biopsy specimen, using the biopsy-specific image-space coordinate transformation provided by the navigation system. Subsequently, each GdE and nCR value was grouped into one of two fluorescence categories, defined by its corresponding biopsy specimen fluorescence assessment as negative fluorescence (fluorescence level 0) or positive fluorescence (fluorescence level 1, 2, or 3). A single neuropathologist analyzed the H & E-stained tissue slides of each biopsy specimen and measured three neuropathological parameters: 1) histopathological score (0-IV); 2) tumor burden score (0-III); and 3) necrotic burden score (0-III).

RESULTS

Mixed-model analyses with random effects for individuals show a highly statistically significant difference between fluorescing and nonfluorescing tissue in GdE (mean difference 8.33, p = 0.018) and nCRs (mean difference 5.15, p < 0.001). An analysis of association demonstrated a significant relationship between the levels of intraoperative fluorescence and histopathological score (χ(2) = 58.8, p < 0.001), between fluorescence levels and tumor burden (χ(2) = 42.7, p < 0.001), and between fluorescence levels and necrotic burden (χ(2) = 30.9, p < 0.001). The corresponding Spearman rank correlation coefficients were 0.51 (p < 0.001) for fluorescence and histopathological score, and 0.49 (p < 0.001) for fluorescence and tumor burden, suggesting a strongly positive relationship for each of these variables.

CONCLUSIONS

These results demonstrate a significant relationship between contrast enhancement on preoperative MR imaging and observable intraoperative PpIX fluorescence. The finding that preoperative MR image signatures are predictive of intraoperative PpIX fluorescence is of practical importance for identifying candidates for the procedure. Furthermore, this study provides evidence that a strong relationship exists between tumor aggressiveness and the degree of tissue fluorescence that is observable intraoperatively, and that observable fluorescence has an excellent positive predictive value but a low negative predictive value.

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.

Estimation of brain deformation for volumetric image updating in protoporphyrin IX fluorescence-guided resection

INTRODUCTION

Fluorescence-guided resection (FGR) of brain tumors is an intuitive, practical and emerging technology for visually delineating neoplastic tissue exposed intraoperatively. Image guidance is the standard technique for producing 3-dimensional spatially coregistered information for surgical decision making. Both technologies together are synergistic: the former detects surface fluorescence as a biomarker of the current surgical margin while the latter shows coregistered volumetric neuroanatomy but can be degraded by intraoperative brain shift. We present the implementation of deformation modeling for brain shift compensation in protoporphyrin IX FGR, integrating these two sources of information for maximum surgical benefit.

METHODS

Two patients underwent FGR coregistered with conventional image guidance. Histopathological analysis, intraoperative fluorescence and image space coordinates were recorded for biopsy specimens acquired during surgery. A biomechanical brain deformation model driven by intraoperative ultrasound data was used to generate updated MR images.

RESULTS

Combined use of fluorescence signatures and updated MR image information showed substantially improved accuracy compared to fluorescence or the original (i.e., nonupdated) MR images, detecting only true positives and true negatives, and no instances of false positives or false negatives.

CONCLUSION

Implementation of brain deformation modeling in FGR shows promise for increasing the accuracy of neurosurgical guidance in the delineation and resection of brain tumors.

Data assimilation using a gradient descent method for estimation of intraoperative brain deformation

Biomechanical models that simulate brain deformation are gaining attention as alternatives for brain shift compensation. One approach, known as the "forced-displacement method", constrains the model to exactly match the measured data through boundary condition (BC) assignment. Although it improves model estimates and is computationally attractive, the method generates fictitious forces and may be ill-advised due to measurement uncertainty. Previously, we have shown that by assimilating intraoperatively acquired brain displacements in an inversion scheme, the Representer algorithm (REP) is able to maintain stress-free BCs and improve model estimates by 33% over those without data guidance in a controlled environment. However, REP is computationally efficient only when a few data points are used for model guidance because its costs scale linearly in the number of data points assimilated, thereby limiting its utility (and accuracy) in clinical settings. In this paper, we present a steepest gradient descent algorithm (SGD) whose computational complexity scales nearly invariantly with the number of measurements assimilated by iteratively adjusting the forcing conditions to minimize the difference between measured and model-estimated displacements (model-data misfit). Solutions of full linear systems of equations are achieved with a parallelized direct solver on a shared-memory, eight-processor Linux cluster. We summarize the error contributions from the entire process of model-updated image registration compensation and we show that SGD is able to attain model estimates comparable to or better than those obtained with REP, capturing about 74-82% of tumor displacement, but with a computational effort that is significantly less (a factor of 4-fold or more reduction relative to REP) and nearly invariant to the amount of sparse data involved when the number of points assimilated is large. Based on five patient cases, an average computational cost of approximately 2 min for estimating whole-brain deformation has been achieved with SGD using 100 sparse data points, suggesting the new algorithm is sufficiently fast with adequate accuracy for routine use in the operating room (OR).

Brain-skull contact boundary conditions in an inverse computational deformation model

Biomechanical models simulating brain motion under loading and boundary conditions in the operating room (OR) are gaining attention as alternatives for brain shift compensation during open cranial neurosurgeries. Although the significance of brain-skull boundary conditions (BCs) in these models has been explored in dynamic simulations, it has not been fully investigated in models representing the quasi-static brain motion that prevails during neurosurgery. In this study, we extend the application of a brain-skull contact BC by incorporating it into an inversion estimation scheme for the deformation field using the steepest gradient descent (SGD) framework. The technique allows parenchymal surface motion normal to the skull while maintaining stress-free BCs at the craniotomy and minimizing the effect of measurement noise. Application of the algorithm in five clinical cases using sparse data generated at the tumor boundary confirms the significance of brain-skull BCs in the model response. Specifically, the results demonstrate that the contact BC enhances model flexibility and achieves improved or comparable performance at the tumor boundary (recovering about 85% of the deformation) relative to that obtained when normal motion of the parenchymal surface is not allowed. It also significantly improves model estimation accuracy at the craniotomy (1.6mm on average), especially when the normal motion is large. The importance of the method is that model performance significantly improves when brain-skull contact influences the deformation field but does not degrade when the contact is less critical and simpler BCs would suffice. The computational cost of the technique is currently 3.9 min on average, but may be further reduced by applying an iterative solver to the linear systems of equations involved and/or by local refinement of the mesh in regions of interest.

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.

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.

An atlas-based method to compensate for brain shift: preliminary results

Compensating for intraoperative brain shift using computational models has shown promising results. Since computational time is an important factor during neurosurgery, a priori knowledge of the possible sources of deformation can increase the accuracy of model-updated image-guided systems. In this paper, a strategy to compensate for distributed loading conditions in the brain such as brain sag, volume changes due to drug reactions, and brain swelling due to edema is presented. An atlas of model deformations based on these complex loading conditions is computed preoperatively and used with a constrained linear inverse model to predict the intraoperative distributed brain shift. This relatively simple inverse finite-element approach is investigated within the context of a series of phantom experiments, two in vivo cases, and a simulation study. Preliminary results indicate that the approach recaptured on average 93% of surface shift for the simulation, phantom, and in vivo experiments. With respect to subsurface shift, comparisons were only made with simulation and phantom experiments and demonstrated an ability to recapture 85% of the shift. This translates to a remaining surface and subsurface shift error of 0.7+/-0.3 mm, and 1.0+/-0.4 mm, respectively, for deformations on the order of 1cm.

Stereopsis-guided brain shift compensation

Brain deformation models have proven to be a powerful tool in compensating for soft tissue deformation during image-guided neurosurgery. The accuracy of these models can be improved by incorporating intraoperative measurements of brain motion. We have designed and implemented a passive intraoperative stereo vision system capable of estimating the three-dimensional shape of the surgical scene in near real-time. This intraoperative shape is compared with the cortical surface in the co-registered preoperative magnetic resonance (MR) volume for the estimation of the cortical motion resulting from the open cranial surgery. The estimated cortical motion is then used to guide a full brain model, which updates a preoperative MR volume. We have found that the stereo vision system is accurate to within approximately 1 mm. Based on data from two representative clinical cases, we show that stereopsis guidance improves the accuracy of brain shift compensation both at and below the cortical surface.