Mesenchymal stem cells (MSC) have a therapeutic potential in patients with fractures to reduce the time of healing and treat non-unions. The use of MSC to treat fractures is attractive as it would be implementing a reparative process that should be in place but occurs to be defective or protracted and MSC effects would be needed only for the repairing time that is relatively brief. 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-LacZ-MSC, we found that MSC engrafted within the callus endosteal niche. Using MSC from BMP-2-Lac-Z mice genetically modified using a bacterial artificial chromosome system to be β-gal reporters for BMP-2 expression, we found that MSC contributed to the callus initiation by expressing BMP-2. The knowledge of the multiple MSC regenerative abilities in fracture healing will allow to design novel MSC-based therapies to treat fractures.

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.

Current mathematical models of tumor growth are limited in their clinical application because they require input data that are nearly impossible to obtain with sufficient spatial resolution in patients even at a single time point—for example, extent of vascularization, immune infiltrate, ratio of tumor-to-normal cells, or extracellular matrix status. Here we propose the use of emerging, quantitative tumor imaging methods to initialize a new generation of predictive models. In the near future, these models could be able to forecast clinical outputs, such as overall response to treatment and time to progression, which will provide opportunities for guided intervention and improved patient care.

While there is considerable data on the use of mathematical modeling to describe tumor growth and response to therapy, previous approaches are often not of the form that can be easily applied to clinical data to generate testable predictions in individual patients. Thus, there is a clear need to develop and apply clinically-relevant oncological models that are amenable to available patient data and yet retain the most salient features of response prediction. In this study we show how a biomechanical model of tumor growth can be initialized and constrained by serial patient-specific magnetic resonance imaging data, obtained at two time points early in the course of therapy (before initiation and following one cycle of therapy), to predict the response for individual patients with breast cancer undergoing neoadjuvant therapy. Using our mechanics coupled modeling approach, we are able to predict, after the first cycle of therapy, breast cancer patients that would eventually achieve a complete pathological response and those who would not, with receiver operating characteristic area under the curve (AUC) of 0.87, sensitivity of 92%, and specificity of 84%. Our approach significantly outperformed the AUCs achieved by standard (i.e., not mechanically coupled)reaction-diffusion predictive modeling (0.75), simple analysis of the tumor cellularity estimated from imaging data (0.73), and the Response Evaluation Criteria in Solid Tumors (RECIST; 0.71). Thus, we show the potential for mathematical model prediction for use as a prognostic indicator of response to therapy. The work indicates the considerable promise of image-driven biophysical modeling for predictive frameworks within therapeutic applications.

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.

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

Compensating for intraoperative brain shift using computational models has shown promising results. Since computational time is an important factor during neurosurgery, a priori knowledge of the possible sources of deformation can increase the accuracy of model-updated image-guided systems. In this paper, a strategy to compensate for distributed loading conditions in the brain such as brain sag, volume changes due to drug reactions, and brain swelling due to edema is presented. An atlas of model deformations based on these complex loading conditions is computed preoperatively and used with a constrained linear inverse model to predict the intraoperative distributed brain shift. This relatively simple inverse finite-element approach is investigated within the context of a series of phantom experiments, two in vivo cases, and a simulation study. Preliminary results indicate that the approach recaptured on average 93% of surface shift for the simulation, phantom, and in vivo experiments. With respect to subsurface shift, comparisons were only made with simulation and phantom experiments and demonstrated an ability to recapture 85% of the shift. This translates to a remaining surface and subsurface shift error of 0.7 ± 0.3 mm, and 1.0 ± 0.4 mm, respectively, for deformations on the order of 1 cm.

Recent advances in the field of stereotactic 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 we 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, we 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.