Intraoperative brain shift compensation: accounting for dural septa

A review on organ deformation modeling approaches for reliable surgical navigation using augmented reality

Augmented Reality (AR) holds the potential to revolutionize surgical procedures by allowing surgeons to visualize critical structures within the patient's body. This is achieved through superimposing preoperative organ models onto the actual anatomy. Challenges arise from dynamic deformations of organs during surgery, making preoperative models inadequate for faithfully representing intraoperative anatomy. To enable reliable navigation in augmented surgery, modeling of intraoperative deformation to obtain an accurate alignment of the preoperative organ model with the intraoperative anatomy is indispensable. Despite the existence of various methods proposed to model intraoperative organ deformation, there are still few literature reviews that systematically categorize and summarize these approaches. This review aims to fill this gap by providing a comprehensive and technical-oriented overview of modeling methods for intraoperative organ deformation in augmented reality in surgery. Through a systematic search and screening process, 112 closely relevant papers were included in this review. By presenting the current status of organ deformation modeling methods and their clinical applications, this review seeks to enhance the understanding of organ deformation modeling in AR-guided surgery, and discuss the potential topics for future advancements.

A position- and time-dependent pressure profile to model viscoelastic mechanical behavior of the brain tissue due to tumor growth

This study proposed a computational framework to calculate the resultant position- and time-dependent pressure profile on the brain tissue due to tumor growth. A finite element (FE) patch of the brain tissue was constructed and an inverse dynamic FE-optimization algorithm was used to calculate its viscoelastic mechanical properties under compressive uniaxial loading. Two patient-specific post-tumor resection FE models were input to the FE-optimization algorithm to calculate the optimized 3rd-order position-dependent and normal distribution time-dependent pressure profile parameters. The optimized viscoelastic material properties, the most suitable simulation time, and the optimized 3rd-order position- and -time-dependent pressure profiles were calculated.

Fabrication of a positional brain shift phantom through the utilization of the frozen intermediate hydrogel state

Synthetic models (phantoms) of the brain-skull system are useful tools for the study of surgical events that are otherwise difficult to study directly in humans. To date, very few studies can be found which replicate the full anatomical brain-skull system. Such models are required to study the more global mechanical events that can occur in neurosurgery, such as positional brain shift. Presented in this work is a novel workflow for the fabrication of a biofidelic brain-skull phantom which features a full hydrogel brain with fluid-filled ventricle/fissure spaces, elastomer dural septa and fluid-filled skull. Central to this workflow is the utilization of the frozen intermediate curing state of an established brain tissue surrogate, which allows for a novel moulding and skull installation approach that permits a much fuller recreation of the anatomy. The mechanical realism of the phantom was validated through indentation testing of the phantom's brain and simulation of the supine to prone brain shift event, while the geometric realism was validated through magnetic resonance imaging. The developed phantom captured a novel measurement of the supine to prone brain shift event with a magnitude that accurately reproduces that seen in the literature.

Inverse dynamic finite element-optimization modeling of the brain tumor mass-effect using a variable pressure boundary

BACKGROUND AND OBJECTIVE

Statistical atlases of brain structure can potentially contribute in the surgical and radiotherapeutic treatment planning for the brain tumor patients. However, the current brain image-registration methods lack of accuracy when it comes to the mass-effect caused by tumor growth. Numerical simulations, such as finite element method (FEM), allow us to calculate the resultant pressure and deformation in the brain tissue due to tumor growth, and to predict the mass-effect. To date, however, the pressure boundary in the brain tissue due to tumor growth has been simply presented as a constant profile throughout the entire tumor outer surface that resulted in discrepancy between the patient imaging data and brain atlases.

METHODS

In this study, we employed a fully-coupled inverse dynamic FE-optimization method to estimate the resultant variable pressure boundary due to tumor resection surgery. To do that, magnetic resonance imaging data of two patients' pre- and post-tumor resection surgery were registered, segmented, volume-meshed, and prepared for fully-coupled inverse dynamic FE-optimization simulations. Two different pressure boundaries were defined on the brain cavity after tumor resection including: a) a constant pressure boundary and b) a variable pressure boundary. The inverse FE-optimization algorithm was used to find the optimum constant and variable pressure boundaries that result in the least distance between the surface-nodes of the post-surgery brain cavity and pre-surgery tumor.

RESULTS

The results revealed that a variable pressure boundary causes a considerably lower mean percentage error compared to a constant pressure one; hence, it can more effectively address the realistic boundary in tumor resection surgery and predict the mass-effect.

CONCLUSIONS

The proposed variable pressure boundary can be a robust tool that allows batch processing to register the brains with tumors to statistical atlases of normal brains and construction of brain tumor atlases. This approach is also computationally inexpensive and can be coupled to any FE software to run. The findings of this study have implications for not only predicting the accurate pressure boundary and mass-effect before tumor resection surgery, but also for predicting some clinical symptoms of brain cancers and presenting useful tools for APPLICATIONs in image-guided neurosurgery.

Impact of brain shift on neural pathways in deep brain stimulation: a preliminary analysis via multi-physics finite element models

OBJECTIVE

The effectiveness of deep brain stimulation (DBS) depends on electrode placement accuracy, which can be compromised by brain shift during surgery. While there have been efforts in assessing the impact of electrode misplacement due to brain shift using preop- and postop- imaging data, such analysis using preop- and intraop- imaging data via biophysical modeling has not been conducted. This work presents a preliminary study that applies a multi-physics analysis framework using finite element biomechanical and bioelectric models to examine the impact of realistic intraoperative shift on neural pathways determined by tractography.

APPROACH

The study examined six patients who had undergone interventional magnetic resonance (iMR)-guided DBS surgery. The modeling framework utilized a biomechanical approach to update preoperative MR to reflect shift-induced anatomical changes. Using this anatomically deformed image and its undeformed counterpart, bioelectric effects from shifting electrode leads could be simulated and neural activation differences were approximated. Specifically, for each configuration, volume of tissue activation (VTA) was computed and subsequently used for tractography estimation. Total tract volume and overlapping volume with motor regions as well as connectivity profile were compared. In addition, volumetric overlap between different fiber bundles among configurations was computed and correlated to estimated shift.

MAIN RESULT

The study found deformation-induced differences in tract volume, motor region overlap, and connectivity behavior, suggesting the impact of shift. There is a strong correlation (R=-0.83) between shift from intended target and intended neural pathway recruitment, where at threshold of ~2.94 mm, intended recruitment completely degrades. The determined threshold is consistent with and provides quantitative support to prior observations and literature that deviations of 2-3 mm are detrimental.

SIGNIFICANCE

The findings support and advance prior studies and understanding to illustrate the need to account for shift in DBS and the potentiality of computational modeling for estimating influence of shift on neural activation.

A hybrid, image-based and biomechanics-based registration approach to markerless intraoperative nodule localization during video-assisted thoracoscopic surgery

The resection of small, low-dense or deep lung nodules during video-assisted thoracoscopic surgery (VATS) is surgically challenging. Nodule localization methods in clinical practice typically rely on the preoperative placement of markers, which may lead to clinical complications. We propose a markerless lung nodule localization framework for VATS based on a hybrid method combining intraoperative cone-beam CT (CBCT) imaging, free-form deformation image registration, and a poroelastic lung model with allowance for air evacuation. The difficult problem of estimating intraoperative lung deformations is decomposed into two more tractable sub-problems: (i) estimating the deformation due the change of patient pose from preoperative CT (supine) to intraoperative CBCT (lateral decubitus); and (ii) estimating the pneumothorax deformation, i.e. a collapse of the lung within the thoracic cage. We were able to demonstrate the feasibility of our localization framework with a retrospective validation study on 5 VATS clinical cases. Average initial errors in the range of 22 to 38 mm were reduced to the range of 4 to 14 mm, corresponding to an error correction in the range of 63 to 85%. To our knowledge, this is the first markerless lung deformation compensation method dedicated to VATS and validated on actual clinical data.

Accounting for intraoperative brain shift ascribable to cavity collapse during intracranial tumor resection

Purpose: For many patients with intracranial tumors, accurate surgical resection is a mainstay of their treatment paradigm. During surgical resection, image guidance is used to aid in localization and resection. Intraoperative brain shift can invalidate these guidance systems. One cause of intraoperative brain shift is cavity collapse due to tumor resection, which will be referred to as "debulking." We developed an imaging-driven finite element model of debulking to create a comprehensive simulation data set to reflect possible intraoperative changes. The objective was to create a method to account for brain shift due to debulking for applications in image-guided neurosurgery. We hypothesized that accounting for tumor debulking in a deformation atlas data framework would improve brain shift predictions, which would enhance image-based surgical guidance. Approach: This was evaluated in a six-patient intracranial tumor resection intraoperative data set. The brain shift deformation atlas data framework consisted of n = 756 simulated deformations to account for effects due to gravity-induced and hyperosmotic drug-induced brain shift, which reflects previous developments. An additional complement of n = 84 deformations involving simulated tumor growth followed by debulking was created to capture observed intraoperative effects not previously included. Results: In five of six patient cases evaluated, inclusion of debulking mechanics improved brain shift correction by capturing global mass effects resulting from the resected tumor. Conclusions: These findings suggest imaging-driven brain shift models used to create a deformation simulation data framework of observed intraoperative events can be used to assist in more accurate image-guided surgical navigation in the brain.

Accounting for Deformation in Deep Brain Stimulation Surgery With Models: Comparison to Interventional Magnetic Resonance Imaging

The efficacy of deep brain stimulation (DBS) depends on electrode placement accuracy, which can be jeopardized by brain shift due to burr hole and dura opening during surgery. Brain shift violates assumed rigid alignment between preoperative image and intraoperative anatomy, negatively impacting therapy.

OBJECTIVE

This study presents a deformation-atlas biomechanical model-based approach to address shift.

METHODS

Six patients, who underwent interventional magnetic resonance (iMR) image-guided DBS burr hole surgery, were studied. A patient-specific model was employed under varying surgical conditions, generating a collection of possible intraoperative shift estimations or a 'deformation atlas.' An inverse problem was driven by sparse measurements derived from iMR to determine an optimal fit of solutions of the atlas. This fit was then used to obtain a volumetric deformation field, which was utilized to update preoperative MR and estimate shift at surgical target region localized on iMR. Model performance was examined by quantitatively comparing intraoperative subsurface measurements to their model-predicted counterparts, and qualitatively comparing iMR, preoperative MR, and model updated MR. A nonrigid image registration was introduced as a comparator.

RESULTS

Model-based approach reduced general parenchyma shift from 8.2 ± 2.2 to 2.7 ± 1.1 mm (∼66.8% correction), and produced updated MR with better agreement to iMR than that of preoperative MR. The average model estimated shift at target region was 1.2 mm.

CONCLUSIONS

This study demonstrates the feasibility of a model-based shift correction strategy in DBS surgery with only sparse data.

SIGNIFICANCE

The developed strategy has the potential to complement and/or enhance current clinical approaches in addressing shift.

Image Updating for Brain Shift Compensation During Resection

BACKGROUND

In open-cranial neurosurgery, preoperative magnetic resonance (pMR) images are typically coregistered for intraoperative guidance. Their accuracy can be significantly degraded by intraoperative brain deformation, especially when resection is involved.

OBJECTIVE

To produce model updated MR (uMR) images to compensate for brain shift that occurred during resection, and evaluate the performance of the image-updating process in terms of accuracy and computational efficiency.

METHODS

In 14 resection cases, intraoperative stereovision image pairs were acquired after dural opening and during resection to generate displacement maps of the surgical field. These data were assimilated by a biomechanical model to create uMR volumes of the evolving surgical field. A tracked stylus provided independent measurements of feature locations to quantify target registration errors (TREs) in the original coregistered pMR and uMR as surgery progressed.

RESULTS

Updated MR TREs were 1.66 ± 0.27 and 1.92 ± 0.49 mm in the 14 cases after dural opening and after partial resection, respectively, compared to 8.48 ± 3.74 and 8.77 ± 4.61 mm for pMR, respectively. The overall computational time for generating uMRs after partial resection was less than 10 min.

CONCLUSION

We have developed an image-updating system to compensate for brain deformation during resection using a computational model with data assimilation of displacements measured with intraoperative stereovision imaging that maintains TREs less than 2 mm on average.

In vivo modeling of interstitial pressure in a porcine model: approximation of poroelastic properties and effects of enhanced anatomical structure modeling

The purpose of this investigation is to test whether a poroelastic model with enhanced structure can capture in vivo interstitial pressure dynamics in a brain undergoing mock surgical loads. Using interstitial pressure data from a porcine study, we use an inverse model to reconstruct material properties in an effort to capture these in vivo brain tissue dynamics. Four distinct models for the reconstruction of parameters are investigated (full anatomical condition description, condition without dural septa description, condition without ventricle boundary description, and the conventional fully saturated model). These models are systematic in their development to isolate the influence of three model characteristics: the dural septa, the treatment of the ventricles, and the treatment of the brain as a saturated media. This study demonstrates that to capture appropriate pressure compartmentalization, interstitial pressure gradients, pressure transient effects, and deformations within the brain, the proposed boundary conditions and structural enhancement coupled with a heterogeneous description invoking partial saturation are needed in a biphasic poroelastic model. These findings suggest that with enhanced anatomical modeling and appropriate model assumptions, poroelastic models can be used to capture quite complex brain deformations and interstitial pressure dynamics.

Falx Cerebri Segmentation via Multi-atlas Boundary Fusion

The falx cerebri is a meningeal projection of dura in the brain, separating the cerebral hemispheres. It has stiffer mechanical properties than surrounding tissue and must be accurately segmented for building computational models of traumatic brain injury. In this work, we propose a method to segment the falx using T1-weighted magnetic resonance images (MRI) and susceptibility-weighted MRI (SWI). Multi-atlas whole brain segmentation is performed using the T1-weighted MRI and the gray matter cerebrum labels are extended into the longitudinal fissure using fast marching to find an initial estimate of the falx. To correct the falx boundaries, we register and then deform a set of SWI with manually delineated falx boundaries into the subject space. The continuous-STAPLE algorithm fuses sets of corresponding points to produce an estimate of the corrected falx boundary. Correspondence between points on the deformed falx boundaries is obtained using coherent point drift. We compare our method to manual ground truth, a multi-atlas approach without correction, and single-atlas approaches.

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.

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.

Android application for determining surgical variables in brain-tumor resection procedures

The fidelity of image-guided neurosurgical procedures is often compromised due to the mechanical deformations that occur during surgery. In recent work, a framework was developed to predict the extent of this brain shift in brain-tumor resection procedures. The approach uses preoperatively determined surgical variables to predict brain shift and then subsequently corrects the patient's preoperative image volume to more closely match the intraoperative state of the patient's brain. However, a clinical workflow difficulty with the execution of this framework is the preoperative acquisition of surgical variables. To simplify and expedite this process, an Android, Java-based application was developed for tablets to provide neurosurgeons with the ability to manipulate three-dimensional models of the patient's neuroanatomy and determine an expected head orientation, craniotomy size and location, and trajectory to be taken into the tumor. These variables can then be exported for use as inputs to the biomechanical model associated with the correction framework. A multisurgeon, multicase mock trial was conducted to compare the accuracy of the virtual plan to that of a mock physical surgery. It was concluded that the Android application was an accurate, efficient, and timely method for planning surgical variables.

Automatic falx cerebri and tentorium cerebelli segmentation from Magnetic Resonance Images

The falx cerebri and tentorium cerebelli are dural structures found in the brain. Due to the roles both structures play in constraining brain motion, the falx and tentorium must be identified and included in finite element models of the head to accurately predict brain dynamics during injury events. To date there has been very little research work on automatically segmenting these two structures, which is understandable given that their 1) thin structure challenges the resolution limits of in vivo 3D imaging, and 2) contrast with respect to surrounding tissue is low in standard magnetic resonance imaging. An automatic segmentation algorithm to find the falx and tentorium which uses the results of a multi-atlas segmentation and cortical reconstruction algorithm is proposed. Gray matter labels are used to find the location of the falx and tentorium. The proposed algorithm is applied to five datasets with manual delineations. 3D visualizations of the final results are provided, and Hausdorff distance (HD) and mean surface distance (MSD) is calculated to quantify the accuracy of the proposed method. For the falx, the mean HD is 43.84 voxels and the mean MSD is 2.78 voxels, with the largest errors occurring at the frontal inferior falx boundary. For the tentorium, the mean HD is 14.50 voxels and mean MSD is 1.38 voxels.

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.

Medical image registration using sparse coding of image patches

Image registration is a basic task in medical image processing applications like group analysis and atlas construction. Similarity measure is a critical ingredient of image registration. Intensity distortion of medical images is not considered in most previous similarity measures. Therefore, in the presence of bias field distortions, they do not generate an acceptable registration. In this paper, we propose a sparse based similarity measure for mono-modal images that considers non-stationary intensity and spatially-varying distortions. The main idea behind this measure is that the aligned image is constructed by an analysis dictionary trained using the image patches. For this purpose, we use "Analysis K-SVD" to train the dictionary and find the sparse coefficients. We utilize image patches to construct the analysis dictionary and then we employ the proposed sparse similarity measure to find a non-rigid transformation using free form deformation (FFD). Experimental results show that the proposed approach is able to robustly register 2D and 3D images in both simulated and real cases. The proposed method outperforms other state-of-the-art similarity measures and decreases the transformation error compared to the previous methods. Even in the presence of bias field distortion, the proposed method aligns images without any preprocessing.

Augmenting Surgery via Multi-scale Modeling and Translational Systems Biology in the Era of Precision Medicine: A Multidisciplinary Perspective

In this era of tremendous technological capabilities and increased focus on improving clinical outcomes, decreasing costs, and increasing precision, there is a need for a more quantitative approach to the field of surgery. Multiscale computational modeling has the potential to bridge the gap to the emerging paradigms of Precision Medicine and Translational Systems Biology, in which quantitative metrics and data guide patient care through improved stratification, diagnosis, and therapy. Achievements by multiple groups have demonstrated the potential for (1) multiscale computational modeling, at a biological level, of diseases treated with surgery and the surgical procedure process at the level of the individual and the population; along with (2) patient-specific, computationally-enabled surgical planning, delivery, and guidance and robotically-augmented manipulation. In this perspective article, we discuss these concepts, and cite emerging examples from the fields of trauma, wound healing, and cardiac surgery.

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.

Anticipation of brain shift in Deep Brain Stimulation automatic planning

Deep Brain Stimulation is a neurosurgery procedure consisting in implanting an electrode in a deep structure of the brain. This intervention requires a preoperative planning phase, with a millimetric accuracy, in which surgeons decide the best placement of the electrode depending on a set of surgical rules. However, brain tissues may deform during the surgery because of the brain shift phenomenon, leading the electrode to mistake the target, or moreover to damage a vital anatomical structure. In this paper, we present a patient-specific automatic planning approach for DBS procedures which accounts for brain deformation. Our approach couples an optimization algorithm with FEM based brain shift simulation. The system was tested successfully on a patient-specific 3D model, and was compared to a planning without considering brain shift. The obtained results point out the importance of performing planning in dynamic conditions.

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.

Estimation of intra-operative brain shift based on constrained Kalman filter

In this study, the problem of estimation of brain shift is addressed by which the accuracy of neuronavigation systems can be improved. To this end, the actual brain shift is considered as a Gaussian random vector with a known mean and an unknown covariance. Then, brain surface imaging is employed together with solutions of linear elastic model and the best estimation is found using constrained Kalman filter (CKF). Moreover, a recursive method (RCKF) is presented, the computational cost of which in the operating room is significantly lower than CKF, because it is not required to compute inverse of any large matrix. Finally, the theory is verified by the simulation results, which show the superiority of the proposed method as compared to one existing method.

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.

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.

Model-based correction of tissue compression for tracked ultrasound in soft tissue image-guided surgery

Acquisition of ultrasound data negatively affects image registration accuracy during image-guided therapy because of tissue compression by the probe. We present a novel compression correction method that models sub-surface tissue displacement resulting from application of a tracked probe to the tissue surface. Patient landmarks are first used to register the probe pose to pre-operative imaging. The ultrasound probe geometry is used to provide boundary conditions to a biomechanical model of the tissue. The deformation field solution of the model is inverted to non-rigidly transform the ultrasound images to an estimation of the tissue geometry before compression. Experimental results with gel phantoms indicated that the proposed method reduced the tumor margin modified Hausdorff distance (MHD) from 5.0 ± 1.6 to 1.9 ± 0.6 mm, and reduced tumor centroid alignment error from 7.6 ± 2.6 to 2.0 ± 0.9 mm. The method was applied to a clinical case and reduced the tumor margin MHD error from 5.4 ± 0.1 to 2.6 ± 0.1 mm and the centroid alignment error from 7.2 ± 0.2 to 3.5 ± 0.4 mm.

Analysis of electrode deformations in deep brain stimulation surgery

PURPOSE

Deep brain stimulation (DBS) surgery is used to reduce motor symptoms when movement disorders are refractory to medical treatment. Post-operative brain morphology can induce electrode deformations as the brain recovers from an intervention. The inverse brain shift has a direct impact on accuracy of the targeting stage, so analysis of electrode deformations is needed to predict final positions.

METHODS

DBS electrode curvature was evaluated in 76 adults with movement disorders who underwent bilateral stimulation, and the key variables that affect electrode deformations were identified. Non-linear modelling of the electrode axis was performed using post-operative computed tomography (CT) images. A mean curvature index was estimated for each patient electrode. Multivariate analysis was performed using a regression decision tree to create a hierarchy of predictive variables. The identification and classification of key variables that determine electrode curvature were validated with statistical analysis.

RESULTS

The principal variables affecting electrode deformations were found to be the date of the post-operative CT scan and the stimulation target location. The main pathology, patient's gender, and disease duration had a smaller although important impact on brain shift.

CONCLUSIONS

The principal determinants of electrode location accuracy during DBS procedures were identified and validated. These results may be useful for improved electrode targeting with the help of mathematical models.

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.

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.

Toward a preoperative planning tool for brain tumor resection therapies

BACKGROUND

Neurosurgical procedures involving tumor resection require surgical planning such that the surgical path to the tumor is determined to minimize the impact on healthy tissue and brain function. This work demonstrates a predictive tool to aid neurosurgeons in planning tumor resection therapies by finding an optimal model-selected patient orientation that minimizes lateral brain shift in the field of view. Such orientations may facilitate tumor access and removal, possibly reduce the need for retraction, and could minimize the impact of brain shift on image-guided procedures.

METHODS

In this study, preoperative magnetic resonance images were utilized in conjunction with pre- and post-resection laser range scans of the craniotomy and cortical surface to produce patient-specific finite element models of intraoperative shift for 6 cases. These cases were used to calibrate a model (i.e., provide general rules for the application of patient positioning parameters) as well as determine the current model-based framework predictive capabilities. Finally, an objective function is proposed that minimizes shift subject to patient position parameters. Patient positioning parameters were then optimized and compared to our neurosurgeon as a preliminary study.

RESULTS

The proposed model-driven brain shift minimization objective function suggests an overall reduction of brain shift by 23 % over experiential methods.

CONCLUSIONS

This work recasts surgical simulation from a trial-and-error process to one where options are presented to the surgeon arising from an optimization of surgical goals. To our knowledge, this is the first realization of an evaluative tool for surgical planning that attempts to optimize surgical approach by means of shift minimization in this manner.