Stereopsis-guided brain shift compensation

Optical Co-Registration Method of Triaxial OPM-MEG and MRI

The advent of optically pumped magnetometers (OPMs) facilitates the development of on-scalp magnetoencephalography (MEG). In particular, the triaxial OPM emerged recently, making simultaneous measurements of all three orthogonal components of vector fields possible. The detection of triaxial magnetic fields improves the interference suppression capability and achieves higher source localization accuracy using fewer sensors. The source localization accuracy of MEG is based on the accurate co-registration of MEG and MRI. In this study, we proposed a triaxial co-registration method according to combined principal component analysis and iterative closest point algorithms for use of a flexible cap. A reference phantom with known sensor positions and orientations was designed and constructed to evaluate the accuracy of the proposed method. Experiments showed that the average co-registered position errors of all sensors were approximately 1 mm and average orientation errors were less than 2.5° in the X -and Y orientations and less than 1.6° in the Z orientation. Furthermore, we assessed the influence of co-registration errors on the source localization using simulations. The average source localization error of approximately 1 mm reflects the effectiveness of the co-registration method. The proposed co-registration method facilitates future applications of triaxial sensors on flexible caps.

Model-Based Image Updating for Brain Shift in Deep Brain Stimulation Electrode Placement Surgery

OBJECTIVE

The efficacy of deep brain stimulation (DBS) depends on accurate placement of electrodes. Although stereotactic frames enable co-registration of image-based surgical planning and the operative field, the accuracy of electrode placement can be degraded by intra-operative brain shift. In this study, we adapted a biomechanical model to estimate whole brain displacements from which we deformed preoperative CT (preCT) to generate an updated CT (uCT) that compensates for brain shift.

METHODS

We drove the deformation model using displacement data derived from deformation in the frontal cortical surface that occurred during the DBS intervention. We evaluated 15 patients, retrospectively, who underwent bilateral DBS surgery, and assessed the accuracy of uCT in terms of target registration error (TRE) relative to a CT acquired post-placement (postCT). We further divided subjects into large (Group L) and small (Group S) deformation groups based on a TRE threshold of 1.6mm. Anterior commissure (AC), posterior commissure (PC) and pineal gland (PG) were identified on preCT and postCT and used to quantify TREs in preCT and uCT.

RESULTS

In the group of large brain deformation, average TREs for uCT were 1.11 ± 0.13 and 1.07 ± 0.38 mm at AC and PC, respectively, compared to 1.85 ± 0.17 and 0.92 ± 0.52 mm for preCT. The model updating approach improved AC localization but did not alter TREs at PC.

CONCLUSION

This preliminary study suggests that our image updating method may compensate for brain shift around surgical targets of importance during DBS surgery, although further investigation is warranted before conclusive evidence will be available.

SIGNIFICANCE

With further development and evaluation, our model-based image updating method using intraoperative sparse data may compensate for brain shift in DBS surgery efficiently, and have utility in updating targeting coordinates.

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.

Perspective review on applications of optics in spinal surgery

Optical technologies may be applied to multiple facets of spinal surgery from diagnostics to intraoperative image guidance to therapeutics. In diagnostics, the current standard remains cross-sectional static imaging. Optical surface scanning tools may have an important role; however, significant work is required to clearly correlate surface metrics to radiographic and clinically relevant spinal anatomy and alignment. In the realm of intraoperative image guidance, optical tracking is widely developed as the current standard of instrument tracking, however remains compromised by line-of-sight issues and more globally cumbersome registration workflows. Surface scanning registration tools are being refined to address concerns over workflow and learning curves, and allow real-time update of tissue deformation; however, the line-of-sight issues plaguing instrument tracking remain to be addressed. In therapeutics, optical applications exist in both visualization, in the form of endoscopes, and ablation, in the form of lasers. Further work is required to extend the feasibility of laser ablation to multiple tissues, including disc, bone, and tumor, in a safe and time-efficient manner. Finally, we postulate some of the short- and long-term opportunities for future growth of optical techniques in the context of spinal surgery. Particular emphasis is placed on intraoperative image guidance, the area of the authors' primary expertise.

Red-light excitation of protoporphyrin IX fluorescence for subsurface tumor detection

OBJECTIVE The objective of this study was to detect 5-aminolevulinic acid (ALA)-induced tumor fluorescence from glioma below the surface of the surgical field by using red-light illumination. METHODS To overcome the shallow tissue penetration of blue light, which maximally excites the ALA-induced fluorophore protoporphyrin IX (PpIX) but is also strongly absorbed by hemoglobin and oxyhemoglobin, a system was developed to illuminate the surgical field with red light (620-640 nm) matching a secondary, smaller absorption peak of PpIX and detecting the fluorescence emission through a 650-nm longpass filter. This wide-field spectroscopic imaging system was used in conjunction with conventional blue-light fluorescence for comparison in 29 patients undergoing craniotomy for resection of high-grade glioma, low-grade glioma, meningioma, or metastasis. RESULTS Although, as expected, red-light excitation is less sensitive to PpIX in exposed tumor, it did reveal tumor at a depth up to 5 mm below the resection bed in 22 of 24 patients who also exhibited PpIX fluorescence under blue-light excitation during the course of surgery. CONCLUSIONS Red-light excitation of tumor-associated PpIX fluorescence below the surface of the surgical field can be achieved intraoperatively and enables detection of subsurface tumor that is not visualized under conventional blue-light excitation. Clinical trial registration no.: NCT02191488 (clinicaltrials.gov).

Retrospective study comparing model-based deformation correction to intraoperative magnetic resonance imaging for image-guided neurosurgery

Brain shift during tumor resection compromises the spatial validity of registered preoperative imaging data that is critical to image-guided procedures. One current clinical solution to mitigate the effects is to reimage using intraoperative magnetic resonance (iMR) imaging. Although iMR has demonstrated benefits in accounting for preoperative-to-intraoperative tissue changes, its cost and encumbrance have limited its widespread adoption. While iMR will likely continue to be employed for challenging cases, a cost-effective model-based brain shift compensation strategy is desirable as a complementary technology for standard resections. We performed a retrospective study of [Formula: see text] tumor resection cases, comparing iMR measurements with intraoperative brain shift compensation predicted by our model-based strategy, driven by sparse intraoperative cortical surface data. For quantitative assessment, homologous subsurface targets near the tumors were selected on preoperative MR and iMR images. Once rigidly registered, intraoperative shift measurements were determined and subsequently compared to model-predicted counterparts as estimated by the brain shift correction framework. When considering moderate and high shift ([Formula: see text], [Formula: see text] measurements per case), the alignment error due to brain shift reduced from [Formula: see text] to [Formula: see text], representing [Formula: see text] correction. These first steps toward validation are promising for model-based strategies.

White matter tractography for neurosurgical planning: A topography-based review of the current state of the art

We perform a review of the literature in the field of white matter tractography for neurosurgical planning, focusing on those works where tractography was correlated with clinical information such as patient outcome, clinical functional testing, or electro-cortical stimulation. We organize the review by anatomical location in the brain and by surgical procedure, including both supratentorial and infratentorial pathologies, and excluding spinal cord applications. Where possible, we discuss implications of tractography for clinical care, as well as clinically relevant technical considerations regarding the tractography methods. We find that tractography is a valuable tool in variable situations in modern neurosurgery. Our survey of recent reports demonstrates multiple potentially successful applications of white matter tractography in neurosurgery, with progress towards overcoming clinical challenges of standardization and interpretation.

Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery

Intraoperative brain shift during neurosurgical procedures is a well-known phenomenon caused by gravity, tissue manipulation, tumor size, loss of cerebrospinal fluid (CSF), and use of medication. For the use of image-guided systems, this phenomenon greatly affects the accuracy of the guidance. During the last several decades, researchers have investigated how to overcome this problem. The purpose of this paper is to present a review of publications concerning different aspects of intraoperative brain shift especially in a tumor resection surgery such as intraoperative imaging systems, quantification, measurement, modeling, and registration techniques. Clinical experience of using intraoperative imaging modalities, details about registration, and modeling methods in connection with brain shift in tumor resection surgery are the focuses of this review. In total, 126 papers regarding this topic are analyzed in a comprehensive summary and are categorized according to fourteen criteria. The result of the categorization is presented in an interactive web tool. The consequences from the categorization and trends in the future are discussed at the end of this work.

Intraoperative image updating for brain shift following dural opening

OBJECTIVE Preoperative magnetic resonance images (pMR) are typically coregistered to provide intraoperative navigation, the accuracy of which can be significantly compromised by brain deformation. In this study, the authors generated updated MR images (uMR) in the operating room (OR) to compensate for brain shift due to dural opening, and evaluated the accuracy and computational efficiency of the process. METHODS In 20 open cranial neurosurgical cases, a pair of intraoperative stereovision (iSV) images was acquired after dural opening to reconstruct a 3D profile of the exposed cortical surface. The iSV surface was registered with pMR to detect cortical displacements that were assimilated by a biomechanical model to estimate whole-brain nonrigid deformation and produce uMR in the OR. The uMR views were displayed on a commercial navigation system and compared side by side with the corresponding coregistered pMR. A tracked stylus was used to acquire coordinate locations of features on the cortical surface that served as independent positions for calculating target registration errors (TREs) for the coregistered uMR and pMR image volumes. RESULTS The uMR views were visually more accurate and well aligned with the iSV surface in terms of both geometry and texture compared with pMR where misalignment was evident. The average misfit between model estimates and measured displacements was 1.80 ± 0.35 mm, compared with the average initial misfit of 7.10 ± 2.78 mm between iSV and pMR, and the average TRE was 1.60 ± 0.43 mm across the 20 patients in the uMR image volume, compared with 7.31 ± 2.82 mm on average in the pMR cases. The iSV also proved to be accurate with an average error of 1.20 ± 0.37 mm. The overall computational time required to generate the uMR views was 7-8 minutes. CONCLUSIONS This study compensated for brain deformation caused by intraoperative dural opening using computational model-based assimilation of iSV cortical surface displacements. The uMR proved to be more accurate in terms of model-data misfit and TRE in the 20 patient cases evaluated relative to pMR. The computational time was acceptable (7-8 minutes) and the process caused minimal interruption of surgical workflow.

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.

In Vivo Investigation of the Effectiveness of a Hyper-viscoelastic Model in Simulating Brain Retraction

Intraoperative brain retraction leads to a misalignment between the intraoperative positions of the brain structures and their previous positions, as determined from preoperative images. In vitro swine brain sample uniaxial tests showed that the mechanical response of brain tissue to compression and extension could be described by the hyper-viscoelasticity theory. The brain retraction caused by the mechanical process is a combination of brain tissue compression and extension. In this paper, we first constructed a hyper-viscoelastic framework based on the extended finite element method (XFEM) to simulate intraoperative brain retraction. To explore its effectiveness, we then applied this framework to an in vivo brain retraction simulation. The simulation strictly followed the clinical scenario, in which seven swine were subjected to brain retraction. Our experimental results showed that the hyper-viscoelastic XFEM framework is capable of simulating intraoperative brain retraction and improving the navigation accuracy of an image-guided neurosurgery system (IGNS).

Automatic tracking of intraoperative brain surface displacements in brain tumor surgery

In brain tumor surgery, soft-tissue deformation, known as brain shift, introduces inaccuracies in the application of the preoperative surgical plan and impedes the advancement of image-guided surgical (IGS) systems. Considerable progress in using patient-specific biomechanical models to update the preoperative images intraoperatively has been made. These model-update methods rely on accurate intraoperative 3D brain surface displacements. In this work, we investigate and develop a fully automatic method to compute these 3D displacements for lengthy (~15 minutes) stereo-pair video sequences acquired during neurosurgery. The first part of the method finds homologous points temporally in the video and the second part computes the nonrigid transformation between these homologous points. Our results, based on parts of 2 clinical cases, show that this speedy and promising method can robustly provide 3D brain surface measurements for use with model-based updating frameworks.

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.

Superficial vessel reconstruction with a multiview camera system

We aim at reconstructing superficial vessels of the brain. Ultimately, they will serve to guide the deformation methods to compensate for the brain shift. A pipeline for three-dimensional (3-D) vessel reconstruction using three mono-complementary metal-oxide semiconductor cameras has been developed. Vessel centerlines are manually selected in the images. Using the properties of the Hessian matrix, the centerline points are assigned direction information. For correspondence matching, a combination of methods was used. The process starts with epipolar and spatial coherence constraints (geometrical constraints), followed by relaxation labeling and an iterative filtering where the 3-D points are compared to surfaces obtained using the thin-plate spline with decreasing relaxation parameter. Finally, the points are shifted to their local centroid position. Evaluation in virtual, phantom, and experimental images, including intraoperative data from patient experiments, shows that, with appropriate camera positions, the error estimates (root-mean square error and mean error) are [Formula: see text].

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

PURPOSE

Brain shift during neurosurgical procedures must be corrected for in order to reestablish accurate alignment for successful image-guided tumor resection. Sparse-data-driven biomechanical models that predict physiological brain shift by accounting for typical deformation-inducing events such as cerebrospinal fluid drainage, hyperosmotic drugs, swelling, retraction, resection, and tumor cavity collapse are an inexpensive solution. This study evaluated the robustness and accuracy of a biomechanical model-based brain shift correction system to assist with tumor resection surgery in 16 clinical cases.

METHODS

Preoperative computation involved the generation of a patient-specific finite element model of the brain and creation of an atlas of brain deformation solutions calculated using a distribution of boundary and deformation-inducing forcing conditions (e.g., sag, tissue contraction, and tissue swelling). The optimum brain shift solution was determined using an inverse problem approach which linearly combines solutions from the atlas to match the cortical surface deformation data collected intraoperatively. The computed deformations were then used to update the preoperative images for all 16 patients.

RESULTS

The mean brain shift measured ranged on average from 2.5 to 21.3 mm, and the biomechanical model-based correction system managed to account for the bulk of the brain shift, producing a mean corrected error ranging on average from 0.7 to 4.0 mm.

CONCLUSIONS

Biomechanical models are an inexpensive means to assist intervention via correction for brain deformations that can compromise surgical navigation systems. To our knowledge, this study represents the most comprehensive clinical evaluation of a deformation correction pipeline for image-guided neurosurgery.

Computational Modeling for Enhancing Soft Tissue Image Guided Surgery: An Application in Neurosurgery

With the recent advances in computing, the opportunities to translate computational models to more integrated roles in patient treatment are expanding at an exciting rate. One area of considerable development has been directed towards correcting soft tissue deformation within image guided neurosurgery applications. This review captures the efforts that have been undertaken towards enhancing neuronavigation by the integration of soft tissue biomechanical models, imaging and sensing technologies, and algorithmic developments. In addition, the review speaks to the evolving role of modeling frameworks within surgery and concludes with some future directions beyond neurosurgical applications.

Patient Registration Using Intraoperative Stereovision in Image-guided Open Spinal Surgery

Despite its widespread availability and success in open cranial neurosurgery, image-guidance technology remains more limited in use in open spinal procedures, in large part, because of patient registration challenges. In this study, we evaluated the feasibility of using intraoperative stereovision (iSV) for accurate, efficient, and robust patient registration in an open spinal fusion surgery. Geometrical surfaces of exposed vertebrae were first reconstructed from iSV. A classical multistart registration was then executed between point clouds generated from iSV and preoperative computed tomography images of the spine. With two pairs of feature points manually identified to facilitate the registration, an average registration accuracy of 1.43 mm in terms of surface-to-surface distance error was achieved in eight patient cases using a single iSV image pair sampling 2-3 vertebral segments. The iSV registration error was consistently smaller than the conventional landmark approach for every case (average of 2.02 mm with the same error metric). The large capture ranges (average of 23.8 mm in translation and 46.0° in rotation) found in the iSV patient registration suggest the technique may offer sufficient robustness for practical application in the operating room. Although some manual effort was still necessary, the manually-derived inputs for iSV registration only needed to be approximate as opposed to be precise and accurate for the manual efforts required in landmark registration. The total computational cost of the iSV registration was 1.5 min on average, significantly less than the typical ∼30 min required for the landmark approach. These findings support the clinical feasibility of iSV to offer accurate, efficient, and robust patient registration in open spinal surgery, and therefore, its potential to further increase the adoption of image guidance in this surgical specialty.

Multi-sensor super-resolution for hybrid range imaging with application to 3-D endoscopy and open surgery

In this paper, we propose a multi-sensor super-resolution framework for hybrid imaging to super-resolve data from one modality by taking advantage of additional guidance images of a complementary modality. This concept is applied to hybrid 3-D range imaging in image-guided surgery, where high-quality photometric data is exploited to enhance range images of low spatial resolution. We formulate super-resolution based on the maximum a-posteriori (MAP) principle and reconstruct high-resolution range data from multiple low-resolution frames and complementary photometric information. Robust motion estimation as required for super-resolution is performed on photometric data to derive displacement fields of subpixel accuracy for the associated range images. For improved reconstruction of depth discontinuities, a novel adaptive regularizer exploiting correlations between both modalities is embedded to MAP estimation. We evaluated our method on synthetic data as well as ex-vivo images in open surgery and endoscopy. The proposed multi-sensor framework improves the peak signal-to-noise ratio by 2 dB and structural similarity by 0.03 on average compared to conventional single-sensor approaches. In ex-vivo experiments on porcine organs, our method achieves substantial improvements in terms of depth discontinuity reconstruction.

Stereovision to MR image registration for cortical surface displacement mapping to enhance image-guided neurosurgery

PURPOSE

A surface registration method is presented to align intraoperative stereovision (iSV) with preoperative magnetic resonance (pMR) images, which utilizes both geometry and texture information to extract tissue displacements as part of the overall process of compensating for intraoperative brain deformation in order to maintain accurate neuronavigational image guidance during surgery.

METHODS

A sum-of-squared-difference rigid image registration was first executed to detect lateral shift of the cortical surface and was followed by a mutual-information-based block matching method to detect local nonrigid deformation caused by distention or collapse of the cortical surface. Ten (N = 10) surgical cases were evaluated in which an independent point measurement of a dominant cortical surface feature location was recorded with a tracked stylus in each case and compared to its surface-registered counterpart. The full three-dimensional (3D) displacement field was also extracted to drive a biomechanical brain deformation model, the results of which were reconciled with the reconstructed iSV surface as another form of evaluation.

RESULTS

Differences between the tracked stylus coordinates of cortical surface features and their surface-registered locations were 1.94 ± 0.59 mm on average across the ten cases. When the complete displacement map derived from surface registration was utilized, the resulting images generated from mechanical model updates were consistent in terms of both geometry (1-2 mm of model misfit) and texture, and were generated with less than 10 min of computational time. Analysis of the surface-registered 3D displacements indicate that the magnitude of motion ranged from 4.03 to 9.79 mm in the ten patient cases, and the amount of lateral shift was not related statistically to the direction of gravity (p = 0.73 ≫ 0.05) or the craniotomy size (p = 0.48 ≫ 0.05) at the beginning of surgery.

CONCLUSIONS

The iSV-pMR surface registration method utilizes texture and geometry information to extract both global lateral shift and local nonrigid movement of the cortical surface in 3D. The results suggest small differences exist in surface-registered locations when compared to positions measured independently with a coregistered stylus and when the full iSV surface was aligned with model-updated MR. The effectiveness and efficiency of the registration method is also minimally disruptive to surgical workflow.

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.

Cortical surface shift estimation using stereovision and optical flow motion tracking via projection image registration

Stereovision is an important intraoperative imaging technique that captures the exposed parenchymal surface noninvasively during open cranial surgery. Estimating cortical surface shift efficiently and accurately is critical to compensate for brain deformation in the operating room (OR). In this study, we present an automatic and robust registration technique based on optical flow (OF) motion tracking to compensate for cortical surface displacement throughout surgery. Stereo images of the cortical surface were acquired at multiple time points after dural opening to reconstruct three-dimensional (3D) texture intensity-encoded cortical surfaces. A local coordinate system was established with its z-axis parallel to the average surface normal direction of the reconstructed cortical surface immediately after dural opening in order to produce two-dimensional (2D) projection images. A dense displacement field between the two projection images was determined directly from OF motion tracking without the need for feature identification or tracking. The starting and end points of the displacement vectors on the two cortical surfaces were then obtained following spatial mapping inversion to produce the full 3D displacement of the exposed cortical surface. We evaluated the technique with images obtained from digital phantoms and 18 surgical cases - 10 of which involved independent measurements of feature locations acquired with a tracked stylus for accuracy comparisons, and 8 others of which 4 involved stereo image acquisitions at three or more time points during surgery to illustrate utility throughout a procedure. Results from the digital phantom images were very accurate (0.05 pixels). In the 10 surgical cases with independently digitized point locations, the average agreement between feature coordinates derived from the cortical surface reconstructions was 1.7-2.1mm relative to those determined with the tracked stylus probe. The agreement in feature displacement tracking was also comparable to tracked probe data (difference in displacement magnitude was <1mm on average). The average magnitude of cortical surface displacement was 7.9 ± 5.7 mm (range 0.3-24.4 mm) in all patient cases with the displacement components along gravity being 5.2 ± 6.0 mm relative to the lateral movement of 2.4 ± 1.6 mm. Thus, our technique appears to be sufficiently accurate and computationally efficiency (typically ∼15 s), for applications in the OR.

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.

A framework for correcting brain retraction based on an eXtended Finite Element Method using a laser range scanner

BACKGROUND

Brain retraction causes great distortion that limits the accuracy of an image-guided neurosurgery system that uses preoperative images. Therefore, brain retraction correction is an important intraoperative clinical application.

METHODS

We used a linear elastic biomechanical model, which deforms based on the eXtended Finite Element Method (XFEM) within a framework for brain retraction correction. In particular, a laser range scanner was introduced to obtain a surface point cloud of the exposed surgical field including retractors inserted into the brain. A brain retraction surface tracking algorithm converted these point clouds into boundary conditions applied to XFEM modeling that drive brain deformation. To test the framework, we performed a brain phantom experiment involving the retraction of tissue. Pairs of the modified Hausdorff distance between Canny edges extracted from model-updated images, pre-retraction, and post-retraction CT images were compared to evaluate the morphological alignment of our framework. Furthermore, the measured displacements of beads embedded in the brain phantom and the predicted ones were compared to evaluate numerical performance.

RESULTS

The modified Hausdorff distance of 19 pairs of images decreased from 1.10 to 0.76 mm. The forecast error of 23 stainless steel beads in the phantom was between 0 and 1.73 mm (mean 1.19 mm). The correction accuracy varied between 52.8 and 100 % (mean 81.4 %).

CONCLUSIONS

The results demonstrate that the brain retraction compensation can be incorporated intraoperatively into the model-updating process in image-guided neurosurgery systems.

Gadolinium- and 5-aminolevulinic acid-induced protoporphyrin IX levels in human gliomas: an ex vivo quantitative study to correlate protoporphyrin IX levels and blood-brain barrier breakdown

In recent years, 5-aminolevulinic acid (ALA)-induced protoporphyrin IX (PpIX) fluorescence guidance has been used as a surgical adjunct to improve the extent of resection of gliomas. Exogenous administration of ALA before surgery leads to the accumulation of red fluorescent PpIX in tumor tissue that the surgeon can visualize and thereby discriminate between normal and tumor tissue. Selective accumulation of PpIX has been linked to numerous factors, of which blood-brain barrier breakdown has been suggested to be a key factor. To test the hypothesis that PpIX concentration positively correlates with gadolinium (Gd) concentrations, we performed ex vivo measurements of PpIX and of Gd using inductively coupled plasma mass spectrometry, the latter as a quantitative biomarker of blood-brain barrier breakdown; this was corroborated with immunohistochemistry of microvascular density in surgical biopsies of patients undergoing fluorescence-guided surgery for glioma. We found positive correlations between PpIX concentration and Gd concentration (r = 0.58, p < 0.0001) and between PpIX concentration and microvascular density (r = 0.55, p < 0.0001), suggesting a significant, yet limited, association between blood-brain barrier breakdown and ALA-induced PpIX fluorescence. To our knowledge, this is the first time that Gd measurements by inductively coupled plasma mass spectrometry have been used in human gliomas.

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.

Serial FEM/XFEM-Based Update of Preoperative Brain Images Using Intraoperative MRI

Current neuronavigation systems cannot adapt to changing intraoperative conditions over time. To overcome this limitation, we present an experimental end-to-end system capable of updating 3D preoperative images in the presence of brain shift and successive resections. The heart of our system is a nonrigid registration technique using a biomechanical model, driven by the deformations of key surfaces tracked in successive intraoperative images. The biomechanical model is deformed using FEM or XFEM, depending on the type of deformation under consideration, namely, brain shift or resection. We describe the operation of our system on two patient cases, each comprising five intraoperative MR images, and we demonstrate that our approach significantly improves the alignment of nonrigidly registered images.

δ-aminolevulinic acid-induced protoporphyrin IX concentration correlates with histopathologic markers of malignancy in human gliomas: the need for quantitative fluorescence-guided resection to identify regions of increasing malignancy

Extent of resection is a major goal and prognostic factor in the treatment of gliomas. In this study we evaluate whether quantitative ex vivo tissue measurements of δ-aminolevulinic acid-induced protoporphyrin IX (PpIX) identify regions of increasing malignancy in low- and high-grade gliomas beyond the capabilities of current fluorescence imaging in patients undergoing fluorescence-guided resection (FGR). Surgical specimens were collected from 133 biopsies in 23 patients and processed for ex vivo neuropathological analysis: PpIX fluorimetry to measure PpIX concentrations (C(PpIX)) and Ki-67 immunohistochemistry to assess tissue proliferation. Samples displaying visible levels of fluorescence showed significantly higher levels of C(PpIX) and tissue proliferation. C(PpIX) was strongly correlated with histopathological score (nonparametric) and tissue proliferation (parametric), such that increasing levels of C(PpIX) were identified with regions of increasing malignancy. Furthermore, a large percentage of tumor-positive biopsy sites (∼40%) that were not visibly fluorescent under the operating microscope had levels of C(PpIX) greater than 0.1 µg/mL, which indicates that significant PpIX accumulation exists below the detection threshold of current fluorescence imaging. Although PpIX fluorescence is recognized as a visual biomarker for neurosurgical resection guidance, these data show that it is quantitatively related at the microscopic level to increasing malignancy in both low- and high-grade gliomas. This work suggests a need for improved PpIX fluorescence detection technologies to achieve better sensitivity and quantification of PpIX in tissue during surgery.

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).

Cortical surface strain estimation using stereovision

We present a completely noninvasive technique to estimate soft tissue surface strain by differentiating three-dimensional displacements obtained from optical flow motion tracking using stereo images. The implementation of the strain estimation algorithm was verified with simulated data and its application was illustrated in three open cranial neurosurgical cases, where cortical surface strain induced by arterial blood pressure pulsation was evaluated. Local least squares smoothing was applied to the displacement field prior to strain estimation to reduce the effect of noise during differentiation. Maximum principal strains (epsilon1) of up to 7% were found in the exposed cortical area on average, and the largest strains (up to -18%) occurred near the craniotomy rim with the majority of epsilon1 perpendicular to the boundary, indicating relative stretching along this direction. The technique offers a new approach for soft tissue strain estimation for the purpose of biomechanical characterization.

A sparse intraoperative data-driven biomechanical model to compensate for brain shift during neuronavigation

BACKGROUND AND PURPOSE

Intraoperative brain deformation is an important factor compromising the accuracy of image-guided neurosurgery. The purpose of this study was to elucidate the role of a model-updated image in the compensation of intraoperative brain shift.

MATERIALS AND METHODS

An FE linear elastic model was built and evaluated in 11 patients with craniotomies. To build this model, we provided a novel model-guided segmentation algorithm. After craniotomy, the sparse intraoperative data (the deformed cortical surface) were tracked by a 3D LRS. The surface deformation, calculated by an extended RPM algorithm, was applied on the FE model as a boundary condition to estimate the entire brain shift. The compensation accuracy of this model was validated by the real-time image data of brain deformation acquired by intraoperative MR imaging.

RESULTS

The prediction error of this model ranged from 1.29 to 1.91 mm (mean, 1.62 ± 0.22 mm), and the compensation accuracy ranged from 62.8% to 81.4% (mean, 69.2 ± 5.3%). The compensation accuracy on the displacement of subcortical structures was higher than that of deep structures (71.3 ± 6.1%:66.8 ± 5.0%, P < .01). In addition, the compensation accuracy in the group with a horizontal bone window was higher than that in the group with a nonhorizontal bone window (72.0 ± 5.3%:65.7 ± 2.9%, P < .05).

CONCLUSIONS

Combined with our novel model-guided segmentation and extended RPM algorithms, this sparse data-driven biomechanical model is expected to be a reliable, efficient, and convenient approach for compensation of intraoperative brain shift in image-guided surgery.

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.

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.