We present a framework for modeling gliomas growth and their mechanical impact on the surrounding brain tissue (the so-called, mass-effect). We employ an Eulerian continuum approach that results in a strongly coupled system of nonlinear Partial Differential Equations (PDEs): a reaction-diffusion model for the tumor growth and a piecewise linearly elastic material for the background tissue. To estimate unknown model parameters and enable patient-specific simulations we formulate and solve a PDE-constrained optimization problem. Our two main goals are the following: (1) to improve the deformable registration from images of brain tumor patients to a common stereotactic space, thereby assisting in the construction of statistical anatomical atlases; and (2) to develop predictive capabilities for glioma growth, after the model parameters are estimated for a given patient. To our knowledge, this is the first attempt in the literature to introduce an adjoint-based, PDE-constrained optimization formulation in the context of image-driven modeling spatio-temporal tumor evolution. In this paper, we present the formulation, and the solution method and we conduct 1D numerical experiments for preliminary evaluation of the overall formulation/methodology.

Meningococcal conjugate vaccines induce herd protection by preventing nasopharyngeal meningococcal acquisition, which is a prerequisite for invasive disease. Thus, meningococcal carriage epidemiology is important in understanding relationships between carriage and disease. A literature search traced information on meningococcal carriage in 27 EU countries. Meningococcal carriage prevalence differed within and between countries, varying across age groups, serogroup distribution and over time. Carriage prevalence increased during childhood, peaking in 15-24-year-olds. While serogroup B was usually the dominant serogroupable carried serogroup, serogroups C, W-135 and Y were also frequently carried. Current carriage studies in Europe are limited. New studies using standardized methods are needed to improve our understanding of meningococcal disease etiology and transmission, and to monitor the impact of meningococcal conjugate vaccines in populations.

MRSA not only replaced methicillin susceptible S. aureus (MSSA) isolates as a percentage of all S. aureus isolates, but its hospitalization rates increased over and above the replacement process. This trend also applies to CA-MRSA over hospital-acquired (HA) MRSA.

A comprehensive continuum model of solid tumor evolution and development is investigated in detail numerically, both under the assumption of spherical symmetry and for arbitrary two-dimensional growth. The level set approach is used to obtain solutions for a recently developed multi-cell transport model formulated as a moving boundary problem for the evolution of the tumor. The model represents both the avascular and the vascular phase of growth, and is able to simulate when the transition occurs; progressive formation of a necrotic core and a rim structure in the tumor during the avascular phase are also captured. In terms of transport processes, the interaction of the tumor with the surrounding tissue is realistically incorporated. The two-dimensional simulation results are presented for different initial configurations. The computational framework, based on a Cartesian mesh/narrow band level-set method, can be applied to similar models that require the solution of coupled advection-diffusion equations with a moving boundary inside a fixed domain. The solution algorithm is designed so that extension to three-dimensional simulations is straightforward.

We present a framework for black-box and flexible simulation of soft tissue deformation for medical imaging and surgical planning applications. Our main motivation in the present work is to develop robust algorithms that allow batch processing for registration of brains with tumors to statistical atlases of normal brains and construction of brain tumor atlases. We describe a fully Eulerian formulation able to handle large deformations effortlessly, with a level-set-based approach for evolving fronts. We use a regular grid-fictitious domain method approach, in which we approximate coefficient discontinuities, distributed forces and boundary conditions. This approach circumvents the need for unstructured mesh generation, which is often a bottleneck in the modeling and simulation pipeline. Our framework employs penalty approaches to impose boundary conditions and uses a matrix-free implementation coupled with a multigrid-accelerated Krylov solver. The overall scheme results in a scalable method with minimal storage requirements and optimal algorithmic complexity. We illustrate the potential of our framework to simulate realistic brain tumor mass effects at reduced computational cost, for aiding the registration process towards the construction of brain tumor atlases.

State-of-the art algorithms for deformable image registration are based on the minimization of an image similarity functional that is regularized by adding a penalty term on the deformation map. The penalty function typically represents a smoothness regularization. In this article, we use a constrained optimization formulation in which the image similarity functional is coupled to a biophysical model. This formulation is pertinent when the data have been generated by imaging tissue that undergoes deformations due to an actual biophysical phenomenon. Such is the case of coregistering tumor-bearing brain images from the same individual. We present an approximate model that couples tumor growth with the mechanical deformations of the surrounding brain tissue. We consider primary brain tumors-in particular, gliomas. Glioma growth is modeled by a reaction-advection-diffusion PDE, with a two-way coupling with the underlying tissue elastic deformation. Tumor bulk, infiltration, and subsequent mass effects are not regarded separately but are captured by the model itself in the course of its evolution. Our formulation allows for updating the tumor diffusion coefficient following structural displacements caused by tumor growth/infiltration. Our forward problem implementation builds on the PETSc library of Argonne National Laboratory. Our reformulation results in a very small parameter space, and we use the derivative-free optimization library APPSPACK of Sandia National Laboratories. We test the forward model and the optimization framework by using landmarkbased similarity functions and by applying it to brain tumor data from clinical and animal studies. State-of-theart registration algorithms fail in such problems due to excessive deformations. We compare our results with previous work in our group, and we observed up to 50% improvement in landmark deformation prediction. We present preliminary validation results in which we were able to reconstruct deformation fields using four degrees of freedom. Our study demonstrates the validity of our formulation and points to the need for richer datasets and fast optimization algorithms. Abstract. State-of-the art algorithms for deformable image registration are based on the minimization of an image similarity functional that is regularized by adding a penalty term on the deformation map. The penalty function typically represents a smoothness regularization. In this article, we use a constrained optimization formulation in which the image similarity functional is coupled to a biophysical model. This formulation is pertinent when the data have been generated by imaging tissue that undergoes deformations due to an actual biophysical phenomenon. Such is the case of coregistering tumor-bearing brain images from the same individual. We present an approximate model that couples tumor growth with the mechanical deformations of the surrounding brain tissue. We consider primary brain tumors-in particular, gliomas. Glioma growth is modeled by a reaction-advection-diffusion PDE, with...

Although a variety of diffeomorphic deformable registration methods exist in the literature, application of these methods in the presence of space-occupying lesions is not straightforward. The motivation of this work is spatial normalization of MR images from patients with brain tumors in a common stereotaxic space, aiming to pool data from different patients into a common space in order to perform group analyses. Additionally, transfer of structural and functional information from neuroanatomical brain atlases into the individual patient's space can be achieved via the inverse mapping, for the purpose of segmenting brains and facilitating surgical or radiotherapy treatment planning. A method that estimates the brain tissue loss and replacement by tumor is applied for achieving equivalent image content between an atlas and a patient's scan, based on a biomechanical model of tumor growth. Automated estimation of the parameters modeling brain tissue loss and displacement is performed via optimization of an objective function reflecting feature-based similarity and elastic stretching energy, which is optimized in parallel via APPSPACK (Asynchronous Parallel Pattern Search). The results of the method, applied to 21 brain tumor patients, indicate that the registration accuracy is relatively high in areas around the tumor, as well as in the healthy portion of the brain. Also, the calculated deformation in the vicinity of the tumor is shown to correlate highly with expert-defined visual scores indicating the tumor mass effect, thereby potentially leading to an objective approach to quantification of mass effect, which is commonly used in diagnosis.

Despite longstanding recommendations for routine vaccination against influenza; pneumococcal; tetanus, diphtheria, acellular pertussis (Tdap); and herpes zoster (HZ) among the United States general adult population, vaccine uptake remains low. Understanding factors that influence adult vaccination and coverage variability beyond the national level are important steps toward developing targeted strategies for increasing vaccination coverage. A retrospective analysis was conducted using data from the Behavioral Risk Factor Surveillance System (2011–2014). Multivariable logistic regression modeling was employed to identify individual factors associated with vaccination (socio-demographics, health status, healthcare utilization, state of residence) and generate adjusted vaccination coverage and compliance estimates nationally and by state. Results indicated that multiple characteristics were consistently associated with a higher likelihood of vaccination across all four vaccines, including female sex, increased educational attainment, and annual household income. Model-adjusted vaccination coverage estimates varied widely by state, with inter-state variability for the most recent year of data as follows: influenza (aged ≥18 years) 30.2–49.5%; pneumococcal (aged ≥65 years) 64.0–74.7%; Tdap (aged ≥18 years) 18.7–46.6%; and HZ (aged ≥60 years) 21.3–42.9%. Model-adjusted compliance with age-appropriate recommendations across vaccines was low and also varied by state: influenza+Tdap (aged 18–59 years) 7.9–24.7%; influenza+Tdap+HZ (aged 60–64 years) 4.1–14.4%; and influenza+Tdap+HZ+pneumococcal (aged ≥65 years) 3.0–18.3%. In summary, after adjusting for individual characteristics associated with vaccination, substantial heterogeneity across states remained, suggesting that other local factors (e.g. state policies) may be impacting adult vaccines uptake. Further research is needed to understand such factors, focusing on differences between states with high versus low vaccination coverage.