Model-updated image guidance: initial clinical experiences with gravity-induced brain deformation

Automatic framework for patient-specific modelling of tumour resection-induced brain shift

Our motivation is to enable non-biomechanical engineering specialists to use sophisticated biomechanical models in the clinic to predict tumour resection-induced brain shift, and subsequently know the location of the residual tumour and its boundary. To achieve this goal, we developed a framework for automatically generating and solving patient-specific biomechanical models of the brain. This framework automatically determines patient-specific brain geometry from MRI data, generates patient-specific computational grid, assigns material properties, defines boundary conditions, applies external loads to the anatomical structures, and solves differential equations of nonlinear elasticity using Meshless Total Lagrangian Explicit Dynamics (MTLED) algorithm. We demonstrated the effectiveness and appropriateness of our framework on real clinical cases of tumour resection-induced brain shift.

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.

Preliminary Results Comparing Thin Plate Splines with Finite Element Methods for Modeling Brain Deformation during Neurosurgery using Intraoperative Ultrasound

Brain shift compensation attempts to model the deformation of the brain which occurs during the surgical removal of brain tumors to enable mapping of presurgical image data into patient coordinates during surgery and thus improve the accuracy and utility of neuro-navigation. We present preliminary results from clinical tumor resections that compare two methods for modeling brain deformation, a simple thin plate spline method that interpolates displacements and a more complex finite element method (FEM) that models physical and geometric constraints of the brain and its material properties. Both methods are driven by the same set of displacements at locations surrounding the tumor. These displacements were derived from sets of corresponding matched features that were automatically detected using the SIFT-Rank algorithm. The deformation accuracy was tested using a set of manually identified landmarks. The FEM method requires significantly more preprocessing than the spline method but both methods can be used to model deformations in the operating room in reasonable time frames. Our preliminary results indicate that the FEM deformation model significantly out-performs the spline-based approach for predicting the deformation of manual landmarks. While both methods compensate for brain shift, this work suggests that models that incorporate biophysics and geometric constraints may be more accurate.

Mechanically Coupled Reaction-Diffusion Model to Predict Glioma Growth: Methodological Details

Biophysical models designed to predict the growth and response of tumors to treatment have the potential to become a valuable tool for clinicians in care of cancer patients. Specifically, individualized tumor forecasts could be used to predict response or resistance early in the course of treatment, thereby providing an opportunity for treatment selection or adaption. This chapter discusses an experimental and modeling framework in which noninvasive imaging data is used to initialize and parameterize a subject-specific model of tumor growth. This modeling approach is applied to an analysis of murine models of glioma growth.

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.

A numerical framework for interstitial fluid pressure imaging in poroelastic MRE

A numerical framework for interstitial fluid pressure imaging (IFPI) in biphasic materials is investigated based on three-dimensional nonlinear finite element poroelastic inversion. The objective is to reconstruct the time-harmonic pore-pressure field from tissue excitation in addition to the elastic parameters commonly associated with magnetic resonance elastography (MRE). The unknown pressure boundary conditions (PBCs) are estimated using the available full-volume displacement data from MRE. A subzone-based nonlinear inversion (NLI) technique is then used to update mechanical and hydrodynamical properties, given the appropriate subzone PBCs, by solving a pressure forward problem (PFP). The algorithm was evaluated on a single-inclusion phantom in which the elastic property and hydraulic conductivity images were recovered. Pressure field and material property estimates had spatial distributions reflecting their true counterparts in the phantom geometry with RMS errors around 20% for cases with 5% noise, but degraded significantly in both spatial distribution and property values for noise levels > 10%. When both shear moduli and hydraulic conductivity were estimated along with the pressure field, property value error rates were as high as 58%, 85% and 32% for the three quantities, respectively, and their spatial distributions were more distorted. Opportunities for improving the algorithm are discussed.

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.

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.

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.

Review of fluorescence guided surgery visualization and overlay techniques

In fluorescence guided surgery, data visualization represents a critical step between signal capture and display needed for clinical decisions informed by that signal. The diversity of methods for displaying surgical images are reviewed, and a particular focus is placed on electronically detected and visualized signals, as required for near-infrared or low concentration tracers. Factors driving the choices such as human perception, the need for rapid decision making in a surgical environment, and biases induced by display choices are outlined. Five practical suggestions are outlined for optimal display orientation, color map, transparency/alpha function, dynamic range compression, and color perception check.

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.

Volumetric Measurements of Brain Shift Using Intraoperative Cone-Beam Computed Tomography: Preliminary Study

BACKGROUND

Cerebrospinal fluid leakage and ventricular compression during open surgery may lead to brain deformation called brain shift. Brain shift may affect intraoperative navigation that is based on image-based preoperative planning. Tools to correct or predict these anatomic modifications can be important to maintain precision during open guided neurosurgery.

OBJECTIVE

To obtain a reliable intraoperative volumetric deformation vector field describing brain shift during intracranial neurosurgical procedures.

METHODS

We acquired preoperative and intraoperative cone-beam computed tomography enhanced with intravenous injection of iodine contrast. These data sets were preprocessed and elastically registered to obtain the volumetric brain shift deformation vector fields.

RESULTS

We obtained the brain shift deformation vector field in 9 cases. The deformation fields proved to be highly nonlinear, particularly around the ventricles. Interpatient variability was considerable, with a maximum deformation ranging from 8.1 to 26.6 mm and a standard deviation ranging from 0.9 to 4.9 mm.

CONCLUSION

Contrast-enhanced cone-beam computed tomography provides a feasible technique for intraoperatively determining brain shift deformation vector fields. This technique can be used perioperatively to adjust preoperative planning and coregistration during neurosurgical procedures.

A nonrigid registration method for correcting brain deformation induced by tumor resection

PURPOSE

This paper presents a nonrigid registration method to align preoperative MRI with intraoperative MRI to compensate for brain deformation during tumor resection. This method extends traditional point-based nonrigid registration in two aspects: (1) allow the input data to be incomplete and (2) simulate the underlying deformation with a heterogeneous biomechanical model.

METHODS

The method formulates the registration as a three-variable (point correspondence, deformation field, and resection region) functional minimization problem, in which point correspondence is represented by a fuzzy assign matrix; Deformation field is represented by a piecewise linear function regularized by the strain energy of a heterogeneous biomechanical model; and resection region is represented by a maximal simply connected tetrahedral mesh. A nested expectation and maximization framework is developed to simultaneously resolve these three variables.

RESULTS

To evaluate this method, the authors conducted experiments on both synthetic data and clinical MRI data. The synthetic experiment confirmed their hypothesis that the removal of additional elements from the biomechanical model can improve the accuracy of the registration. The clinical MRI experiments on 25 patients showed that the proposed method outperforms the ITK implementation of a physics-based nonrigid registration method. The proposed method improves the accuracy by 2.88 mm on average when the error is measured by a robust Hausdorff distance metric on Canny edge points, and improves the accuracy by 1.56 mm on average when the error is measured by six anatomical points.

CONCLUSIONS

The proposed method can effectively correct brain deformation induced by tumor resection.

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.

More accurate neuronavigation data provided by biomechanical modeling instead of rigid registration

It is possible to improve neuronavigation during image-guided surgery by warping the high-quality preoperative brain images so that they correspond with the current intraoperative configuration of the brain. In this paper, the accuracy of registration results obtained using comprehensive biomechanical models is compared with the accuracy of rigid registration, the technology currently available to patients. This comparison allows investigation into whether biomechanical modeling provides good-quality image data for neuronavigation for a larger proportion of patients than rigid registration. Preoperative images for 33 neurosurgery cases were warped onto their respective intraoperative configurations using both the biomechanics-based method and rigid registration. The Hausdorff distance-based evaluation process, which measures the difference between images, was used to quantify the performance of both registration methods. A statistical test for difference in proportions was conducted to evaluate the null hypothesis that the proportion of patients for whom improved neuronavigation can be achieved is the same for rigid and biomechanics-based registration. The null hypothesis was confidently rejected (p < 10(-4)). Even the modified hypothesis that fewer than 25% of patients would benefit from the use of biomechanics-based registration was rejected at a significance level of 5% (p = 0.02). The biomechanics-based method proved particularly effective in cases demonstrating large craniotomy-induced brain deformations. The outcome of this analysis suggests that nonlinear biomechanics-based methods are beneficial to a large proportion of patients and can be considered for use in the operating theater as a possible means of improving neuronavigation and surgical outcomes.

Preliminary study of a novel method for conveying corrected image volumes in surgical navigation

BACKGROUND

Commercial image-guided surgery systems rely on the fundamental assumption that preoperative medical images represent the physical state of the patient in the operating room. The guidance display typically consists of a three-dimensional (3D) model derived from medical images and three orthogonal views of the imaging data. A challenging question in image-guided surgery is: what happens when the images used in the guidance display no longer correspond to the current geometric state of the anatomy and guidance information is still desirable?

METHODS

We modify the conventional display with two techniques for incorporating a displacement field from a finite-element model into the guidance display and present a preliminary study of the effect of our method on performance with a simple surgical task. The topic of this paper is methods for conveying the computational model solution, not the model itself. To address the integration of the computational model solution into the display, a novel method of applying the deformation to the tool tip was developed, which quickly corrects for deformation but also maintains the pristine nature of the preoperative images. We compare the proposed technique to an existing method that applies the deformation field to the image volume.

RESULTS

A pilot study compared mean performance with our method of applying the deformation to the tool tip and the conventional technique. These methods were statistically similar with respect to accuracy of localization (p < 0.05) and amount of time (p < 0.05) required for localization of the target.

CONCLUSIONS

These results suggest that our new technique can be used in place of the computationally expensive task of deforming the image volume, without affecting the time or accuracy of the surgical task. Most notably, our work addresses the problem of incorporating deformation correction into the guidance display and offers a first step toward understanding its effect on surgical performance.

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.

Quantitative imaging methods for the development and validation of brain biomechanics models

Rapid deformation of brain tissue in response to head impact or acceleration can lead to numerous pathological changes, both immediate and delayed. Modeling and simulation hold promise for illuminating the mechanisms of traumatic brain injury (TBI) and for developing preventive devices and strategies. However, mathematical models have predictive value only if they satisfy two conditions. First, they must capture the biomechanics of the brain as both a material and a structure, including the mechanics of brain tissue and its interactions with the skull. Second, they must be validated by direct comparison with experimental data. Emerging imaging technologies and recent imaging studies provide important data for these purposes. This review describes these techniques and data, with an emphasis on magnetic resonance imaging approaches. In combination, these imaging tools promise to extend our understanding of brain biomechanics and improve our ability to study TBI in silico.

TRACING THE DEFORMED MIDLINE ON BRAIN CT

Midline shift (MLS) is the most important quantitative feature clinicians use to evaluate the severity of brain compression by various pathologies. We proposed a model of the deformed midline according to the biomechanical properties of different types of intracranial tissue. The model comprised three segments. The upper and lower straight segments represented parts of the tough meninges separating two hemispheres, and the central curved segment, formed by a quadratic Bezier curve, represented the intervening soft brain tissue. For each point of the model, the intensity difference was calculated over 48 adjacent point pairs at each side. The deformed midline was considered ideal as summed square of the difference across all midline points approaches global minimum, simulating maximal bilateral symmetry. Genetic algorithm was applied to optimize the values of the three control points of the Bezier curve. Our system was tested on images containing various pathologies from 81 consecutive patients treated in a single institute over one-year period. The deformed midlines itself as well as the amount of midline shift were evaluated by human experts, with satisfactory results.

Pre- and intraoperative tractographic evaluation of corticospinal tract shift

BACKGROUND

Magnetic resonance with diffusion tensor image (DTI) may be able to estimate trajectories compatible with subcortical tracts close to brain lesions. A limit of DTI is brain shifting (movement of the brain after dural opening and tumor resection).

OBJECTIVE

To calculate the brain shift of trajectories compatible with the corticospinal tract (CST) in patients undergoing glioma resection and predict the shift directions of CST.

METHODS

DTI was acquired in 20 patients and carried out through 12 noncollinear directions. Dedicated software "merged" all sequences acquired with tractographic processing and the whole dataset was sent to the neuronavigation system. Preoperative, after dural opening (in 11) and tumor resection (in all) DTI acquisitions were performed to evaluate CST shifting. The extent of shifting was considered as the maximum distance between the preoperative and intraoperative contours of the trajectories.

RESULTS

An outward shift of CST was observed in 8 patients and an inward shift in 10 patients during surgery. In the remaining 2 patients, no intraoperative displacement was detected. Only peritumoral edema showed a statistically significant correlation with the amount of shift. In those patients in which DTI was acquired after dural opening as well (11 patients), an outward shifting of CST was evident in that phase.

CONCLUSION

The use of intraoperative DTI demonstrated brain shifting of the CST. DTI evaluation of white matter tracts can be used during surgical procedures only if updated with intraoperative acquisitions.

A reduced order explicit dynamic finite element algorithm for surgical simulation

Reduced order modelling, in which a full system response is projected onto a subspace of lower dimensionality, has been used previously to accelerate finite element solution schemes by reducing the size of the involved linear systems. In the present work we take advantage of a secondary effect of such reduction for explicit analyses, namely that the stable integration time step is increased far beyond that of the full system. This phenomenon alleviates one of the principal drawbacks of explicit methods, compared with implicit schemes. We present an explicit finite element scheme in which time integration is performed in a reduced basis. Futhermore, we present a simple procedure for imposing inhomogeneous essential boundary conditions, thus overcoming one of the principal deficiencies of such approaches. The computational benefits of the procedure within a GPU-based execution framework are examined, and an assessment of the errors introduced is given. It is shown that speedups approaching an order of magnitude are feasible, without introduction of prohibitive errors, and without hardware modifications. The procedure may have applications in interactive simulation and medical image-guidance problems, in which both speed and accuracy are vital.

Classification and Analysis of the Errors in Neuronavigation

There are many different types of errors in neuronavigation, and the reasons and results of these errors are complex. For a neurosurgeon using the neuronavigation system, it is important to have a clear understanding of when an error may occur, what the magnitude of it is, and how to avoid it or reduce its influence on the final application accuracy. In this article, we classify all the errors into 2 groups according to the working principle of neuronavigation systems. The first group contains the errors caused by the differences between the anatomic structures in the images and that of the real patient, and the second group contains the errors occurring in transforming the position of surgical tools from the patient space to the image space. Each group is further divided into 2 subgroups. We discuss 16 types of errors and classify each of them into one of the subgroups. The classification and analysis of these errors should help neurosurgeons understand the power and limits of neuronavigation systems and use them more properly.

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).

Model-updated image-guided liver surgery: preliminary results using surface characterization

The current protocol for image guidance in open abdominal liver tumor removal surgeries involves a rigid registration between the patient's operating room space and the pre-operative diagnostic image-space. Systematic studies have shown that the liver can deform up to 2 cm during surgeries in a non-rigid fashion thereby compromising the accuracy of these surgical navigation systems. Compensating for intra-operative deformations using mathematical models has shown promising results. In this work, we follow up the initial rigid registration with a computational approach that is geared towards minimizing the residual closest point distances between the un-deformed pre-operative surface and the rigidly registered intra-operative surface. We also use a surface Laplacian equation based filter that generates a realistic deformation field. Preliminary validation of the proposed computational framework was performed using phantom experiments and clinical trials. The proposed framework improved the rigid registration errors for the phantom experiments on average by 43%, and 74% using partial and full surface data, respectively. With respect to clinical data, it improved the closest point residual error associated with rigid registration by 54% on average for the clinical cases. These results are highly encouraging and suggest that computational models can be used to increase the accuracy of image-guided open abdominal liver tumor removal surgeries.

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.

Real-time surgical simulation using reduced order finite element analysis

Reduced order modelling, in which a full system response is projected onto a subspace of lower dimensionality, has been used previously to accelerate finite element solution schemes by reducing the size of the involved linear systems. In the present work we take advantage of a secondary effect of such reduction for explicit analyses, namely that the stable integration time step is increased far beyond that of the full system. This phenomenon alleviates one of the principal drawbacks of explicit methods, compared with implicit schemes. We present an explicit finite element scheme in which time integration is performed in a reduced basis. The computational benefits of the procedure within a GPU-based execution framework are examined, and an assessment of the errors introduced is given. Speedups approaching an order of magnitude are feasible, without introduction of prohibitive errors, and without hardware modifications. The procedure may have applications in medical image-guidance problems in which both speed and accuracy are vital.

Contrast detection in fluid-saturated media with magnetic resonance poroelastography

PURPOSE

Recent interest in the poroelastic behavior of tissues has led to the development of magnetic resonance poroelastography (MRPE) as an alternative to single-phase MR elastographic image reconstruction. In addition to the elastic parameters (i.e., Lamé's constants) commonly associated with magnetic resonance elastography (MRE), MRPE enables estimation of the time-harmonic pore-pressure field induced by external mechanical vibration.

METHODS

This study presents numerical simulations that demonstrate the sensitivity of the computed displacement and pore-pressure fields to a priori estimates of the experimentally derived model parameters. In addition, experimental data collected in three poroelastic phantoms are used to assess the quantitative accuracy of MR poroelastographic imaging through comparisons with both quasistatic and dynamic mechanical tests.

RESULTS

The results indicate hydraulic conductivity to be the dominant parameter influencing the deformation behavior of poroelastic media under conditions applied during MRE. MRPE estimation of the matrix shear modulus was bracketed by the values determined from independent quasistatic and dynamic mechanical measurements as expected, whereas the contrast ratios for embedded inclusions were quantitatively similar (10%-15% difference between the reconstructed images and the mechanical tests).

CONCLUSIONS

The findings suggest that the addition of hydraulic conductivity and a viscoelastic solid component as parameters in the reconstruction may be warranted.

Patient-specific non-linear finite element modelling for predicting soft organ deformation in real-time: application to non-rigid neuroimage registration

Long computation times of non-linear (i.e. accounting for geometric and material non-linearity) biomechanical models have been regarded as one of the key factors preventing application of such models in predicting organ deformation for image-guided surgery. This contribution presents real-time patient-specific computation of the deformation field within the brain for six cases of brain shift induced by craniotomy (i.e. surgical opening of the skull) using specialised non-linear finite element procedures implemented on a graphics processing unit (GPU). In contrast to commercial finite element codes that rely on an updated Lagrangian formulation and implicit integration in time domain for steady state solutions, our procedures utilise the total Lagrangian formulation with explicit time stepping and dynamic relaxation. We used patient-specific finite element meshes consisting of hexahedral and non-locking tetrahedral elements, together with realistic material properties for the brain tissue and appropriate contact conditions at the boundaries. The loading was defined by prescribing deformations on the brain surface under the craniotomy. Application of the computed deformation fields to register (i.e. align) the preoperative and intraoperative images indicated that the models very accurately predict the intraoperative deformations within the brain. For each case, computing the brain deformation field took less than 4 s using an NVIDIA Tesla C870 GPU, which is two orders of magnitude reduction in computation time in comparison to our previous study in which the brain deformation was predicted using a commercial finite element solver executed on a personal computer.

Data-guide for brain deformation in surgery: comparison of linear and nonlinear models

Background

Pre-operative imaging devices generate high-resolution images but intra-operative imaging devices generate low-resolution images. To use high-resolution pre-operative images during surgery, they must be deformed to reflect intra-operative geometry of brain.

Methods

We employ biomechanical models, guided by low resolution intra-operative images, to determine location of normal and abnormal regions of brain after craniotomy. We also employ finite element methods to discretize and solve the related differential equations. In the process, pre- and intra-operative images are utilized and corresponding points are determined and used to optimize parameters of the models. This paper develops a nonlinear model and compares it with linear models while our previous work developed and compared linear models (mechanical and elastic).

Results

Nonlinear model is evaluated and compared with linear models using simulated and real data. Partial validation using intra-operative images indicates that the proposed models reduce the localization error caused by brain deformation after craniotomy.

Conclusions

The proposed nonlinear model generates more accurate results than the linear models. When guided by limited intra-operative surface data, it predicts deformation of entire brain. Its execution time is however considerably more than those of linear models.

A brain-deformation framework based on a linear elastic model and evaluation using clinical data

In image-guided neurosurgery, brain tissue displacement and deformation during neurosurgical procedures are a major source of error. In this paper, we implement and evaluate a linear-elastic-model-based framework for correction of brain shift using clinical data from five brain tumor patients. The framework uses a linear elastic model to simulate brain-shift behavior. The model is driven by cortical surface deformations, which are tracked using a surface-tracking algorithm combined with a laser-range scanner. The framework performance was evaluated using displacements of anatomical landmarks, tumor contours and self-defined evaluation parameters. The results show that tumor deformations predicted by the present framework agreed well with the ones observed intraoperatively, especially in the parts of the larger deformations. On average, a brain shift of 3.9 mm and a tumor margin shift of 4.2 mm were corrected to 1.2 and 1.3 mm, respectively. The entire correction process was performed in less than 5 min. The data from this study suggest that the technique is a suitable candidate for intraoperative brain-deformation correction.

Adaptive spatial calibration of a 3D ultrasound system

PURPOSE

The authors present a method devised to calibrate the spatial relationship between a 3D ultrasound scanhead and its tracker completely automatically and reliably. The user interaction is limited to collecting ultrasound data on which the calibration is based.

METHODS

The method of calibration is based on images of a fixed plane of unknown location with respect to the 3D tracking system. This approach has, for advantage, to eliminate the measurement of the plane location as a source of error. The devised method is sufficiently general and adaptable to calibrate scanheads for 2D images and 3D volume sets using the same approach. The basic algorithm for both types of scanheads is the same and can be run unattended fully automatically once the data are collected. The approach was devised by seeking the simplest and most robust solutions for each of the steps required. These are the identification of the plane intersection within the images or volumes and the optimization method used to compute a calibration transformation matrix. The authors use adaptive algorithms in these two steps to eliminate data that would otherwise prevent the convergence of the procedure, which contributes to the robustness of the method.

RESULTS

The authors have run tests amounting to 57 runs of the calibration on two a scanhead that produce 3D imaging volumes, at all the available scales. The authors evaluated the system on two criteria: Robustness and accuracy. The program converged to useful values unattended for every one of the tests (100%). Its accuracy, based on the measured location of a reference plane, was estimated to be 0.7 +/- 0.6 mm for all tests combined.

CONCLUSIONS

The system presented is robust and allows unattended computations of the calibration parameters required for freehand tracked ultrasound based on either 2D or 3D imaging systems.

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

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

Positional brain deformation visualized with magnetic resonance morphometry

OBJECTIVE

To assess and visualize gravitational effects on brain morphology and the position of the brain within the skull by magnetic resonance (MR) morphometry in order to identify confounding effects and possible sources of error for accurate planning of neurosurgical interventions.

METHODS

Three-dimensional MR imaging data sets of 13 healthy adults were acquired in different positions in the scanner. With a morphometric approach, data sets were evaluated by deformation field analysis and the brain boundary shift integral. Distortions of the brain were assessed comparing right versus left and prone versus supine positioning, respectively.

RESULTS

Two effects could be differentiated: 1) greatest brain deformation of up to 1.7 mm predominantly located around central brain structures in the lateral direction and a less pronounced change after position changes in posterior-anterior direction, and 2) the brain boundary shift integral depicted position-dependent brain shift relative to the inner skull.

CONCLUSION

Position-dependent effects on brain structure may undermine the accuracy of neuronavigational and other neurosurgical procedures. Furthermore, in longitudinal MR volumetric studies, gravitational effects should be kept in mind and the scanning position should be rigidly controlled for.

Automatic non-linear MRI-ultrasound registration for the correction of intra-operative brain deformations

OBJECTIVE

Movements of brain tissue during neurosurgical procedures reduce the effectiveness of using pre-operative images for intra-operative surgical guidance. In this paper, we explore the use of acquiring intra-operative ultrasound (US) images for the quantification of and correction for non-linear brain deformations.

MATERIALS AND METHODS

We will present a multi-modal registration strategy that automatically matches pre-operative images (e.g., MRI) to intra-operative US to correct for these deformations. The strategy involves using the predicted appearance of neuroanatomical structures in US images to build "pseudo ultrasound" images based on pre-operative segmented MRI. These images can then be non-linearly registered to intra-operative US using cross-correlation measurements within the ANIMAL package. The feasibility of the theory is demonstrated through its application to clinical patient data acquired during 12 neurosurgical procedures.

RESULTS

Results of applying the method to 12 surgical cases, including those with brain tumors and selective amygdalo-hippocampectomies, indicate that our strategy significantly recovers from non-linear brain deformations occurring during surgery. Quantitative results at tumor boundaries indicate up to 87% correction for brain shift.

CONCLUSIONS

Qualitative and quantitative examination of the results indicate that the system is able to correct for non-linear brain deformations in clinical patient data.

Simulation of brain tumors in MR images for evaluation of segmentation efficacy

Obtaining validation data and comparison metrics for segmentation of magnetic resonance images (MRI) are difficult tasks due to the lack of reliable ground truth. This problem is even more evident for images presenting pathology, which can both alter tissue appearance through infiltration and cause geometric distortions. Systems for generating synthetic images with user-defined degradation by noise and intensity inhomogeneity offer the possibility for testing and comparison of segmentation methods. Such systems do not yet offer simulation of sufficiently realistic looking pathology. This paper presents a system that combines physical and statistical modeling to generate synthetic multi-modal 3D brain MRI with tumor and edema, along with the underlying anatomical ground truth, Main emphasis is placed on simulation of the major effects known for tumor MRI, such as contrast enhancement, local distortion of healthy tissue, infiltrating edema adjacent to tumors, destruction and deformation of fiber tracts, and multi-modal MRI contrast of healthy tissue and pathology. The new method synthesizes pathology in multi-modal MRI and diffusion tensor imaging (DTI) by simulating mass effect, warping and destruction of white matter fibers, and infiltration of brain tissues by tumor cells. We generate synthetic contrast enhanced MR images by simulating the accumulation of contrast agent within the brain. The appearance of the the brain tissue and tumor in MRI is simulated by synthesizing texture images from real MR images. The proposed method is able to generate synthetic ground truth and synthesized MR images with tumor and edema that exhibit comparable segmentation challenges to real tumor MRI. Such image data sets will find use in segmentation reliability studies, comparison and validation of different segmentation methods, training and teaching, or even in evaluating standards for tumor size like the RECIST criteria (response evaluation criteria in solid tumors).

Modeling of soft poroelastic tissue in time-harmonic MR elastography

Elastography is an emerging imaging technique that focuses on assessing the resistance to deformation of soft biological tissues in vivo. Magnetic resonance elastography (MRE) uses measured displacement fields resulting from low-amplitude, low-frequency (10 Hz-1 kHz) time-harmonic vibration to recover images of the elastic property distribution of tissues including breast, liver, muscle, prostate, and brain. While many soft tissues display complex time-dependent behavior not described by linear elasticity, the models most commonly employed in MRE parameter reconstructions are based on elastic assumptions. Further, elasticity models fail to include the interstitial fluid phase present in vivo. Alternative continuum models, such as consolidation theory, are able to represent tissue and other materials comprising two distinct phases, generally consisting of a porous elastic solid and penetrating fluid. MRE reconstructions of simulated elastic and poroelastic phantoms were performed to investigate the limitations of current-elasticity-based methods in producing accurate elastic parameter estimates in poroelastic media. The results indicate that linearly elastic reconstructions of fluid-saturated porous media at amplitudes and frequencies relevant to steady-state MRE can yield misleading effective property distributions resulting from the complex interaction between their solid and fluid phases.

On the unimportance of constitutive models in computing brain deformation for image-guided surgery

Imaging modalities that can be used intra-operatively do not provide sufficient details to confidently locate the abnormalities and critical healthy areas that have been identified from high-resolution pre-operative scans. However, as we have shown in our previous work, high quality pre-operative images can be warped to the intra-operative position of the brain. This can be achieved by computing deformations within the brain using a biomechanical model. In this paper, using a previously developed patient-specific model of brain undergoing craniotomy-induced shift, we conduct a parametric analysis to investigate in detail the influences of constitutive models of the brain tissue. We conclude that the choice of the brain tissue constitutive model, when used with an appropriate finite deformation solution, does not affect the accuracy of computed displacements, and therefore a simple linear elastic model for the brain tissue is sufficient.

The steady-state response of a Maxwell viscoelastic cylinder to sinusoidal oscillation of its boundary

The steady-state response of a Maxwell viscoelastic cylinder to periodic sinusoidal oscillation of its boundary was studied as a simplified model of the brain responding to low-amplitude angular vibration of an idealized skull. The objectives were to identify conditions in which peak strain occurred on the interior of the cylinder, and to identify ways to scale strains from differently sized cylinders. This latter objective is motivated by the work of Holbourn to inform scaling of intracranial strains experienced under similar acceleration of skulls of different animals. The mechanical response was dictated by two dimensionless parameters that incorporate material properties and external loading frequency. The location and magnitude of maximum strain were examined with respect to these governing parameters in steady state. A frequency-dependent mapping of brain constitutive data to idealized Maxwell models was applied to predict the location and magnitude of peak strains inside a cylinder with mechanical properties representing the adult human brain. Results suggest that peak strains occur on the interior of such a cylinder for skull oscillation within a specific frequency band.

Development of a mechanical testing assay for fibrotic murine liver

In this article, a novel protocol for mechanical testing, combined with finite element modeling, is presented that allows the determination of the elastic modulus of normal and fibrotic murine livers and is compared to an independent mechanical testing method. The novel protocol employs suspending a portion of murine liver tissue in a cylindrical polyacrylamide gel, imaging with a microCT, conducting mechanical testing, and concluding with a mechanical property determination via a finite element method analysis. More specifically, the finite element model is built from the computerized tomography (CT) images, and boundary conditions are imposed in order to simulate the mechanical testing conditions. The resulting model surface stress is compared to that obtained during mechanical testing, which subsequently allows for direct evaluation of the liver modulus. The second comparison method involves a mechanical indentation test performed on a remaining liver lobe for comparison. In addition, this lobe is used for histological analysis to determine relationships between elasticity measurements and tissue health. This complete system was used to study 14 fibrotic livers displaying advanced fibrosis (injections with irritant), three control livers (injections without irritant), and three normal livers (no injections). The moduli evaluations for nondiseased livers were estimated as 0.62 +/- 0.09 kPa and 0.59 +/- 0.1 kPa for indenter and model-gel-tissue (MGT) assay tests, respectively. Moduli estimates for diseased liver ranged from 0.6-1.64 kPa and 0.96-1.88 kPa for indenter and MGT assay tests, respectively. The MGT modulus, though not equivalent to the modulus determined by indentation, demonstrates a high correlation, thus indicating a relationship between the two testing methods. The results also showed a clear difference between nondiseased and diseased livers. The developed MGT assay system is quite compact and could easily be utilized for controlled evaluation of soft-tissue moduli as shown here. In addition, future work will add the correlative method of elastography such that direct controlled validation of measurement on tissue can be determined.

Biomechanical modeling of decompressive craniectomy in traumatic brain injury

BACKGROUND

Decompressive craniectomy is the final phase in the graded scheme of critical care management of refractory raised intracranial pressure following severe traumatic brain injury. We aim to define the optimal size for decompressive craniectomy so that a good balance is achieved between reduction of raised ICP and the extent of trans-calvarial herniation. Provision of such quantitative data will also allow for improved data comparison in clinical trials addressing the surgical management of severe head injury.

METHODS

In this study, we utilize a finite element mesh model and focus on the effect of size of both unilateral and bifrontal decompressive craniectomy on intracranial pressure and brain herniation.

FINDINGS

The finite element mesh model is able to effect modeling of brain deformation and intracranial pressure changes following both unilateral fronto-parietal-temporal and bifrontal decompressive craniectomy.

CONCLUSIONS

Finite element mesh modeling in the scenario of reafractory raised intracranial pressure following severe head injury may be able to guide the optimal conduct of decompressive surgery so as to effect a reduction in intracranial pressure whilst minimizing trans-calvarial brain herniation.

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

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

CLINICAL EVALUATION AND FOLLOW-UP OUTCOME OF DIFFUSION TENSOR IMAGING-BASED FUNCTIONAL NEURONAVIGATION

OBJECTIVE

To evaluate diffusion tensor imaging (DTI)-based functional neuronavigation in surgery of cerebral gliomas with pyramidal tract (PT) involvement with respect to both perioperative assessment and follow-up outcome.

METHODS

A prospective, randomized controlled study was conducted between 2001 and 2005. A consecutive series of 238 eligible patients with initial imaging diagnosis of cerebral gliomas involving PTs were randomized into study (n = 118) and control (n = 120) groups. The study cases underwent DTI and three-dimensional magnetic resonance imaging scans. The maps of fractional anisotropy were calculated for PT mapping. Both three-dimensional magnetic resonance imaging data sets and fractional anisotropy maps were integrated by rigid registration, after which the tumor and adjacent PT were segmented and reconstructed for presurgical planning and intraoperative guidance. The control cases were operated on using routine neuronavigation.

RESULTS

There was a trend for high-grade gliomas (HGGs) in the study group to be more likely to achieve gross total resection (74.4 versus 33.3%, P &lt; 0.001). There was no significant difference of low-grade gliomas resection between the two groups. Postoperative motor deterioration occurred in 32.8% of control cases, whereas it occurred in only 15.3% of the study cases (P &lt; 0.001). The 6-month Karnofsky Performance Scale score of study cases was significantly higher than that of control cases (86 ± 20 versus 74 ± 28 overall, P &lt; 0.001; 93 ± 10 versus 86 ± 17 for low-grade gliomas, P = 0.013; and 77 ± 27 versus 53 ± 32 for HGGs, P = 0.001). For 81 HGGs, the median survival of study cases was 21.2 months (95% confidence interval, 14.1–28.3 mo) compared with 14.0 months (95% confidence interval, 10.2–17.8 mo) of control cases (P = 0.048). The estimated hazard ratio for the effect of DTI-based functional neuronavigation was 0.570, representing a 43.0% reduction in the risk of death.

CONCLUSION

DTI-based functional neuronavigation contributes to maximal safe resection of cerebral gliomas with PT involvement, thereby decreasing postoperative motor deficits for both HGGs and low-grade gliomas while increasing high-quality survival for HGGs.

Validation of vessel-based registration for correction of brain shift

The displacement and deformation of brain tissue is a major source of error in image-guided neurosurgery systems. We have designed and implemented a method to detect and correct brain shift using pre-operative MR images and intraoperative Doppler ultrasound data and present its validation with both real and simulated data. The algorithm uses segmented vessels from both modalities, and estimates the deformation using a modified version of the iterative closest point (ICP) algorithm. We use the least trimmed squares (LTS) to reduce the number of outliers in the point matching procedure. These points are used to drive a thin-plate spline transform to achieve non-linear registration. Validation was completed in two parts. First, the technique was tested and validated using realistic simulations where the results were compared to the known deformation. The registration technique recovered 75% of the deformation in the region of interest accounting for deformations as large as 20 mm. Second, we performed a PVA-cryogel phantom study where both MR and ultrasound images of the phantom were obtained for three different deformations. The registration results based on MR data were used as a gold standard to evaluate the performance of the ultrasound based registration. On average, deformations of 7.5 mm magnitude were corrected to within 1.6 mm for the ultrasound based registration and 1.07 mm for the MR based registration.