Michael I. Miga

Intraoperative Brain Shift and Deformation: A Quantitative Analysis of Cortical Displacement in 28 Cases

OBJECTIVE A quantitative analysis of intraoperative cortical shift and deformation was performed to gain a better understanding of the nature and extent of this problem and the resultant loss of spatial accuracy in surgical procedures coregistered to preoperative imaging studies. METHODS Three-dimensional feature tracking and two-dimensional image analysis of the cortical surface were used to quantify the observed motion. Data acquisition was facilitated by a ceiling-mounted robotic platform, which provided a number of precision tracking capabilities. The patients head position and the size and orientation of the craniotomy were recorded at the start of surgery. Error analysis demonstrated that the surface displacement measuring methodology was accurate to 1 to 2 mm. Statistical tests were performed to examine correlations between the amount of displacement and the type of surgery, the nature of the cranial opening, the region of the brain involved, the duration of surgery, and the degree of invasiveness. RESULTS The results showed that a displacement of an average of 1 cm occurred, with the dominant directional component being associated with gravity. The mean displacement was determined to be independent of the size and orientation of the cranial opening. CONCLUSION These data suggest that loss of spatial registration with preoperative images is gravity-dominated and of sufficient extent that attention to errors resulting from misregistration during the course of surgery is warranted.

Regenerative effects of transplanted mesenchymal stem cells in fracture healing.

Mesenchymal stem cells (MSC) have a therapeutic potential in patients with fractures to reduce the time of healing and treat nonunions. The use of MSC to treat fractures is attractive for several reasons. First, MSCs would be implementing conventional reparative process that seems to be defective or protracted. Secondly, the effects of MSCs treatment would be needed only for relatively brief duration of reparation. However, an integrated approach to define the multiple regenerative contributions of MSC to the fracture repair process is necessary before clinical trials are initiated. In this study, using a stabilized tibia fracture mouse model, we determined the dynamic migration of transplanted MSC to the fracture site, their contributions to the repair process initiation, and their role in modulating the injury‐related inflammatory responses. Using MSC expressing luciferase, we determined by bioluminescence imaging that the MSC migration at the fracture site is time‐ and dose‐dependent and, it is exclusively CXCR4‐dependent. MSC improved the fracture healing affecting the callus biomechanical properties and such improvement correlated with an increase in cartilage and bone content, and changes in callus morphology as determined by micro‐computed tomography and histological studies. Transplanting CMV‐Cre‐R26R‐Lac Z‐MSC, we found that MSCs engrafted within the callus endosteal niche. Using MSCs from BMP‐2‐Lac Z mice genetically modified using a bacterial artificial chromosome system to be β‐gal reporters for bone morphogenic protein 2 (BMP‐2) expression, we found that MSCs contributed to the callus initiation by expressing BMP‐2. The knowledge of the multiple MSC regenerative abilities in fracture healing will allow design of novel MSC‐based therapies to treat fractures. STEM CELLS 2009;27:1887–1898

A computational model for tracking subsurface tissue deformation during stereotactic neurosurgery

Recent advances in the field of sterotactic neurosurgery have made it possible to coregister preoperative computed tomography (CT) and magnetic resonance (MR) images with instrument locations in the operating field. However, accounting for intraoperative movement of brain tissue remains a challenging problem. While intraoperative CT and MR scanners record concurrent tissue motion, there is motivation to develop methodologies which would be significantly lower in cost and more widely available. The approach the authors present is a computational model of brain tissue deformation that could be used in conjunction with a limited amount of concurrently obtained operative data to estimate subsurface tissue motion. Specifically, the authors report on the initial development of a finite element model of brain tissue adapted from consolidation theory. Validations of the computational mathematics in two and three dimensions are shown with errors of 1%-2% for the discretizations used. Experience with the computational strategy for estimating surgically induced brain tissue motion in vivo is also presented. While the predicted tissue displacements differ from measured values by about 15%, they suggest that exploiting a physics-based computational framework for updating preoperative imaging databases during the course of surgery has considerable merit. However, additional model and computational developments are needed before this approach can become a clinical reality.

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

Image-guided neurosurgery relies on accurate registration of the patient, the preoperative image series, and the surgical instruments in the same coordinate space. Recent clinical reports have documented the magnitude of gravity-induced brain deformation in the operating room and suggest these levels of tissue motion may compromise the integrity of such systems. The authors are investigating a model-based strategy which exploits the wealth of readily-available preoperative information in conjunction with intraoperatively acquired data to construct and drive a three dimensional (3-D) computational model which estimates volumetric displacements in order to update the neuronavigational image set. Using model calculations, the preoperative image database can be deformed to generate a more accurate representation of the surgical focus during an operation. In this paper, the authors present a preliminary study of four patients that experienced substantial brain deformation from gravity and correlate cortical shift measurements with model predictions. Additionally, they illustrate their image deforming algorithm and demonstrate that preoperative image resolution is maintained. Results over the four cases show that the brain shifted, on average, 5.7 mm in the direction of gravity and that model predictions could reduce this misregistration error to an average of 1.2 mm.

An overlapping subzone technique for MR-based elastic property reconstruction.

A finite element–based nonlinear inversion scheme for magnetic resonance (MR) elastography is detailed. The algorithm operates on small overlapping subzones of the total region of interest, processed in a hierarchical order as determined by progressive error minimization. This zoned approach allows for a high degree of spatial discretization, taking advantage of the data‐rich environment afforded by the MR. The inversion technique is tested in simulation under high‐noise conditions (15% random noise applied to the displacement data) with both complicated user‐defined stiffness distributions and realistic tissue geometries obtained by thresholding MR image slices. In both cases the process has proved successful and has been capable of discerning small inclusions near 4 mm in diameter. Magn Reson Med 42:779–786, 1999.

Cortical surface registration for image-guided neurosurgery using laser-range scanning

In this paper, a method of acquiring intraoperative data using a laser range scanner (LRS) is presented within the context of model-updated image-guided surgery. Registering textured point clouds generated by the LRS to tomographic data is explored using established point-based and surface techniques as well as a novel method that incorporates geometry and intensity information via mutual information (SurfaceMI). Phantom registration studies were performed to examine accuracy and robustness for each framework. In addition, an in vivo registration is performed to demonstrate feasibility of the data acquisition system in the operating room. Results indicate that SurfaceMI performed better in many cases than point-based (PBR) and iterative closest point (ICP) methods for registration of textured point clouds. Mean target registration error (TRE) for simulated deep tissue targets in a phantom were 1.0 /spl plusmn/ 0.2,2.0 /spl plusmn/ 0.3, and 1.2 /spl plusmn/ 0.3 mm for PBR, ICP, and SurfaceMI, respectively. With regard to in vivo registration, the mean TRE of vessel contour points for each framework was 1.9 /spl plusmn/ 1.0, 0.9 /spl plusmn/ 0.6, and 1.3 /spl plusmn/ 0.5 for PBR, ICP, and SurfaceMI, respectively. The methods discussed in this paper in conjunction with the quantitative data provide impetus for using LRS technology within the model-updated image-guided surgery framework.

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.

Three‐dimensional subzone‐based reconstruction algorithm for MR elastography

Accurate characterization of harmonic tissue motion for realistic tissue geometries and property distributions requires knowledge of the full three‐dimensional displacement field because of the asymmetric nature of both the boundaries of the tissue domain and the location of internal mechanical heterogeneities. The implications of this for magnetic resonance elastography (MRE) are twofold. First, for MRE methods which require the measurement of a harmonic displacement field within the tissue region of interest, the presence of 3D motion effects reduces or eliminates the possibility that simpler, lower‐dimensional motion field images will capture the true dynamics of the entire stimulated tissue. Second, MRE techniques that exploit model‐based elastic property reconstruction methods will not be able to accurately match the observed displacements unless they are capable of accounting for 3D motion effects. These two factors are of key importance for MRE techniques based on linear elasticity models to reconstruct mechanical tissue property distributions in biological samples. This article demonstrates that 3D motion effects are present even in regular, symmetric phantom geometries and presents the development of a 3D reconstruction algorithm capable of discerning elastic property distributions in the presence of such effects. The algorithm allows for the accurate determination of tissue mechanical properties at resolutions equal to that of the MR displacement image in complex, asymmetric biological tissue geometries. Simulation studies in a realistic 3D breast geometry indicate that the process can accurately detect 1‐cm diameter hard inclusions with 2.5× elasticity contrast to the surrounding tissue. Magn Reson Med 45:827–837, 2001.

In vivo quantification of a homogeneous brain deformation model for updating preoperative images during surgery

Clinicians using image-guidance for neurosurgical procedures have recently recognized that intraoperative deformation from surgical loading can compromise the accuracy of patient registration in the operating room. While whole brain intraoperative imaging is conceptually appealing it presents significant practical limitations. Alternatively, a promising approach may be to combine incomplete intraoperatively acquired data with a computational model of brain deformation to update high resolution preoperative images during surgery. The success of such an approach is critically dependent on identifying a valid model of brain deformation physics. Towards this end, the authors evaluate a three-dimensional finite element consolidation theory model for predicting brain deformation in vivo through a series of controlled repeat-experiments. This database is used to construct an interstitial pressure boundary condition calibration curve which is prospectively tested in a fourth validation experiment. The computational model is found to recover 75%-85% of brain motion occurring under loads comparable to clinical conditions. Additionally, the updating of preoperative images using the model calculations is presented and demonstrates that model-updated image-guided neurosurgery may be a viable option for addressing registration errors related to intraoperative tissue motion.

Intraoperatively Updated Neuroimaging Using Brain Modeling and Sparse Data

OBJECTIVE Image-guided neurosurgery incorporating preoperatively obtained imaging information is subject to spatial error resulting from intraoperative brain displacement and deformation. A strategy to update preoperative imaging using readily available intraoperative information has been developed and implemented. METHODS Preoperative magnetic resonance imaging is used to generate a patient-specific three-dimensional finite element model of the brain by which deformation resulting from multiple surgical processes may be simulated. Sparse imaging data obtained subsequently, such as from digital cameras or ultrasound, are then used to prescribe the displacement of selected points within the model. Based on the model, interpolation to the resolution of preoperative imaging may then be performed. RESULTS The algorithms for generation of the finite element model and for its subsequent deformation were successfully validated using a pig brain model. In these experiments, the method recovered 84% of the intraoperative shift resulting from surgically induced tissue motion. Preliminary clinical application in the operating room has demonstrated feasibility. CONCLUSION A strategy by which intraoperative brain deformation may be accounted for has been developed, validated in an animal model, and demonstrated clinically.