Karen E. Lunn

Modeling of retraction and resection for intraoperative updating of images.

OBJECTIVEIntraoperative tissue deformation that occurs during the course of neurosurgical procedures may compromise patient-to-image registration, which is essential for image guidance. A new approach to account for brain shift, using computational methods driven by sparsely available operating room (OR) data, has been augmented with techniques for modeling tissue retraction and resection. METHODSModeling strategies to arbitrarily place and move an intracranial retractor and to excise designated tissue volumes have been implemented within a computationally tractable framework. To illustrate these developments, a surgical case example, which uses OR data and the preoperative neuroanatomic image volume of the patient to generate a highly resolved, heterogeneous, finite-element model, is presented. Surgical procedures involving the retraction of tissue and the resection of a left frontoparietal tumor were simulated computationally, and the simulations were used to update the preoperative image volume to represent the dynamic OR environment. RESULTSRetraction and resection techniques are demonstrated to accurately reflect intraoperative events, thus providing an approach for near-real-time image-updating in the OR. Information regarding subsurface deformation and, in particular, changing tumor margins is presented. Some of the current limitations of the model, with respect to specific tissue mechanical responses, are highlighted. CONCLUSIONThe results presented demonstrate that complex surgical events such as tissue retraction and resection can be incorporated intraoperatively into the model-updating process for brain shift compensation in high-resolution preoperative images.

Stereopsis-guided brain shift compensation

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

In vivo quantification of retraction deformation modeling for updated image-guidance during neurosurgery

The use of coregistered preoperative anatomical scans to provide navigational information in the operating room has greatly benefited the field of neurosurgery. Nonetheless, it has been widely acknowledged that significant errors between the operating field and the preoperative images are generated as surgery progresses. Quantification of tissue shift can be accomplished with volumetric intraoperative imaging; however, more functional, lower cost alternative solutions to this challenge are desirable. We are developing the strategy of exploiting a computational model driven by sparse data obtained from intraoperative ultrasound and cortical surface tracking to warp preoperative images to reflect the current state of the operating field. This paper presents an initial quantification of the predictive capability of the current model to computationally capture tissue deformation during retraction in the porcine brain. Performance validation is achieved through comparisons of displacement and pressure predictions to experimental measurements obtained from computed tomographic images and pressure sensor recordings. Group results are based upon a generalized set of boundary conditions for four subjects that, on average, account for at least 75% of tissue motion generated during interhemispheric retraction. Individualized boundary conditions can improve the degree of data-model match by 10% or more but warrant further study. Overall, the level of quantitative agreement achieved in these experiments is encouraging for updating preoperative images to reflect tissue deformation resulting from retraction, especially since model improvements are likely as a result of the intraoperative constraints that can be applied through sparse data collection.

Assimilating intraoperative data with brain shift modeling using the adjoint equations.

Biomechanical models of brain deformation are increasingly being used to nonrigidly register preoperative MR (pMR) images of the brain to the surgical scene. These model estimates can potentially be improved by incorporating sparse displacement data available in the operating room (OR), but integrating the intraoperative information with model calculations is a nontrivial problem. We present an inverse method to estimate the unknown boundary and volumetric forces necessary to achieve a least-squares fit between the model and the data that is formulated in terms of the adjoint equations, which are solved directly by the method of representers. The scheme is illustrated in a 2D simulation and in a 2D approximation based on a patient case using actual OR data.

Displacement estimation with co-registered ultrasound for image guided neurosurgery: a quantitative in vivo porcine study

Brain shift during open cranial surgery presents a challenge for maintaining registration with image-guidance systems. Ultrasound (US) is a convenient intraoperative imaging modality that may be a useful tool in detecting tissue shift and updating preoperative images based on intraoperative measurements of brain deformation. We have quantitatively evaluated the ability of spatially tracked freehand US to detect displacement of implanted markers in a series of three in vivo porcine experiments, where both US and computed tomography (CT) image acquisitions were obtained before and after deforming the brain. Marker displacements ranged from 0.5 to 8.5 mm. Comparisons between CT and US measurements showed a mean target localization error of 1.5 mm, and a mean vector error for displacement of 1.1 mm. Mean error in the magnitude of displacement was 0.6 mm. For one of the animals studied, the US data was used in conjunction with a biomechanical model to nonrigidly re-register a baseline CT to the deformed brain. The mean error between the actual and deformed CTs was found to be on average 1.2 and 1.9 mm at the marker locations depending on the extent of the deformation induced. These findings indicate the potential accuracy in coregistered freehand US displacement tracking in brain tissue and suggest that the resulting information can be used to drive a modeling re-registration strategy to comparable levels of agreement.

Data-Guided Brain Deformation Modeling: Evaluation of a 3-D Adjoint Inversion Method in Porcine Studies

Biomechanical models of brain deformation are useful tools for estimating parenchymal shift that results during open cranial procedures. Intraoperative data is likely to improve model estimates, but incorporation of such data into the model is not trivial. This study tests the adjoint equations method (AEM) for data assimilation as a viable approach for integrating displacement data into a brain deformation model. AEM was applied to two porcine experiments. AEM-based estimates were compared both to measured displacement data [from computed tomography (CT) scans] and to model solutions obtained without the guidance of sparse data, which we term the best prior estimate (BPE). Additionally, the sensitivity of the AEM solution to inverse parameter selection was investigated. The results suggest that it is most important to estimate the size of the variance in the measurement error correctly, make the correlation length long and estimate displacement (over stress) boundary conditions. Application of AEM shows an average 33% improvement over BPE. This paper represents the first evidence of successful use of the AEM technique in three dimensions with experimental data validation. The guidelines established for selection of model parameters are starting points for further optimization of the method under clinical conditions

Nonrigid brain registration: synthesizing full volume deformation fields from model basis solutions constrained by partial volume intraoperative data

Abstract During image-guided neurosurgery, maintaining accurate registration of the patient with the preoperative image volume is essential to any navigational system. Since the patient’s brain shifts during many OR procedures, we have developed a physically based deformation model to update images concurrent with surgery in order to achieve nonrigid registration between the brain and the preoperative scans. In this paper, we introduce a strategy for integrating sparse displacement data acquired during surgery with the computational model using an efficient and accurate approach. The complex boundary conditions that exist during surgery are estimated from sparse data through a synthesis of simpler precomputed sets of model basis solutions. These basis solutions are weighted in accordance with a minimization procedure that reduces the error between the observed and computed displacement fields. This method appears to be a promising technique for increasing the speed and accuracy of the computational estimate, thus making intraoperative updates more efficient. Furthermore, it has the advantage of incorporating intraoperatively acquired measurements of true displacements into the model to ensure a more accurate estimation of tissue motion. Results from in vivo pig brain experiments involving multiple retractions show that full volume deformation fields can be constructed throughout a series of retraction events from a single set of basis solutions with equal or increased accuracy.

Model-Updated Image-Guided Neurosurgery: Preliminary Analysis Using Intraoperative MR

In this paper, initial clinical data from an intraoperative MR system are compared to calculations made by a three-dimensional finite element model of brain deformation. The preoperative and intraoperative MR data was collected on a patient undergoing a resection of an astrocytoma, grade 3 with non-enhancing and enhancing regions. The image volumes were co-registered and cortical displacements as well as subsurface structure movements were measured retrospectively. These data were then compared to model predictions undergoing intraoperative conditions of gravity and simulated tumor decompression. Computed results demonstrate that gravity and decompression effects account for approximately 40% and 30%, respectively, totaling a 70% recovery of shifting structures with the model. The results also suggest that a non-uniform decompressive stress distribution may be present during tumor resection. Based on this preliminary experience, model predictions constrained by intraoperative surface data appear to be a promising avenue for correcting brain shift during surgery. However, additional clinical cases where volumetric intraoperative MR data is available are needed to improve the understanding of tissue mechanics during resection.

Intra-Operative Image Updating

Intraoperative brain shift and deformation pose challenges for image-guided surgery. One strategy to address these problems utilizes computational modeling coupled with intraoperatively acquired information from efficient and economical sources such as ultrasound and the optics of the operating microscope. Calibration algorithms for the accurate integration of these sparse data sources have been implemented. Assessment has been performed in both phantom and pig brain models, and accuracy better than 2 mm has been achieved. Methods of incorporating these data into the computational model are being developed.

Quantifying brain shift during neurosurgery using spatially tracked ultrasound

Brain shift during neurosurgery currently limits the effectiveness of stereotactic guidance systems that rely on preoperative image modalities like magnetic resonance (MR). The authors propose a process for quantifying intraoperative brain shift using spatially-tracked freehand intraoperative ultrasound (iUS). First, one segments a distinct feature from the preoperative MR (tumor, ventricle, cyst, or falx) and extracts a faceted surface using the marching cubes algorithm. Planar contours are then semi-automatically segmented from two sets of iUS b-planes obtained (a) prior to the dural opening and (b) after the dural opening. These two sets of contours are reconstructed in the reference frame of the MR, composing two distinct sparsely-sampled surface descriptions of the same feature segmented from MR. Using the Iterative Closest Point (ICP) algorithm one obtains discrete estimates of the feature deformation performing point-to-surface matching. Vector subtraction of the matched points then can be used as sparse deformation data inputs for inverse biomechanical brain tissue models. The results of these simulations are then used to modify the pre-operative MR to account for intraoperative changes. The proposed process has undergone preliminary evaluations in a phantom study and was applied to data from two clinical cases. In the phantom study, the process recovered controlled deformations with an RMS error of 1.1 mm. These results also suggest that clinical accuracy would be on the order of 1-2mm. This finding is consistent with prior work by the Dartmouth Image-Guided Neurosurgery (IGNS) group. In the clinical cases, the deformations obtained were used to produce qualitatively reasonable updated guidance volumes.