An efficient algorithm for mapping imaging data to 3D unstructured grids in computational biomechanics.

Geometries for organ scale and multiscale simulations of organ function are now routinely derived from imaging data. However, medical images may also contain spatially heterogeneous information other than geometry that are relevant to such simulations either as initial conditions or in the form of model parameters. In this manuscript, we present an algorithm for the efficient and robust mapping of such data to imaging-based unstructured polyhedral grids in parallel. We then illustrate the application of our mapping algorithm to three different mapping problems: (i) the mapping of MRI diffusion tensor data to an unstructured ventricular grid; (ii) the mapping of serial cyrosection histology data to an unstructured mouse brain grid; and (iii) the mapping of computed tomography-derived volumetric strain data to an unstructured multiscale lung grid. Execution times and parallel performance are reported for each case.

Host genes related to paneth cells and xenobiotic metabolism are associated with shifts in human ileum-associated microbial composition.

The aim of this study was to integrate human clinical, genotype, mRNA microarray and 16 S rRNA sequence data collected on 84 subjects with ileal Crohn's disease, ulcerative colitis or control patients without inflammatory bowel diseases in order to interrogate how host-microbial interactions are perturbed in inflammatory bowel diseases (IBD). Ex-vivo ileal mucosal biopsies were collected from the disease unaffected proximal margin of the ileum resected from patients who were undergoing initial intestinal surgery. Both RNA and DNA were extracted from the mucosal biopsy samples. Patients were genotyped for the three major NOD2 variants (Leufs1007, R702W, and G908R) and the ATG16L1T300A variant. Whole human genome mRNA expression profiles were generated using Agilent microarrays. Microbial composition profiles were determined by 454 pyrosequencing of the V3-V5 hypervariable region of the bacterial 16 S rRNA gene. The results of permutation based multivariate analysis of variance and covariance (MANCOVA) support the hypothesis that host mucosal Paneth cell and xenobiotic metabolism genes play an important role in host microbial interactions.

Branch-based model for the diameters of the pulmonary airways: accounting for departures from self-consistency and registration errors.

We examine a previously published branch-based approach for modeling airway diameters that is predicated on the assumption of self-consistency across all levels of the tree. We mathematically formulate this assumption, propose a method to test it and develop a more general model to be used when the assumption is violated. We discuss the effect of measurement error on the estimated models and propose methods that take account of error. The methods are illustrated on data from MRI and CT images of silicone casts of two rats, two normal monkeys, and one ozone-exposed monkey. Our results showed substantial departures from self-consistency in all five subjects. When departures from self-consistency exist, we do not recommend using the self-consistency model, even as an approximation, as we have shown that it may likely lead to an incorrect representation of the diameter geometry. The new variance model can be used instead. Measurement error has an important impact on the estimated morphometry models and needs to be addressed in the analysis.

LOCAL ORTHOGONAL CUTTING METHOD FOR COMPUTING MEDIAL CURVES AND ITS BIOMEDICAL APPLICATIONS.

Medial curves have a wide range of applications in geometric modeling and analysis (such as shape matching) and biomedical engineering (such as morphometry and computer assisted surgery). The computation of medial curves poses significant challenges, both in terms of theoretical analysis and practical efficiency and reliability. In this paper, we propose a definition and analysis of medial curves and also describe an efficient and robust method called local orthogonal cutting (LOC) for computing medial curves. Our approach is based on three key concepts: a local orthogonal decomposition of objects into substructures, a differential geometry concept called the interior center of curvature (ICC), and integrated stability and consistency tests. These concepts lend themselves to robust numerical techniques and result in an algorithm that is efficient and noise resistant. We illustrate the effectiveness and robustness of our approach with some highly complex, large-scale, noisy biomedical geometries derived from medical images, including lung airways and blood vessels. We also present comparisons of our method with some existing methods.

Fluid-Structure Interactions of the Mitral Valve and Left Heart: Comprehensive Strategies, Past, Present and Future.

The remodeling that occurs after a posterolateral myocardial infarction can alter mitral valve function by creating conformational abnormalities in the mitral annulus and in the posteromedial papillary muscle, leading to mitral regurgitation (MR). It is generally assumed that this remodeling is caused by a volume load and is mediated by an increase in diastolic wall stress. Thus, mitral regurgitation can be both the cause and effect of an abnormal cardiac stress environment. Computational modeling of ischemic MR and its surgical correction is attractive because it enables an examination of whether a given intervention addresses the correction of regurgitation (fluid-flow) at the cost of abnormal tissue stress. This is significant because the negative effects of an increased wall stress due to the intervention will only be evident over time. However, a meaningful fluid-structure interaction model of the left heart is not trivial; it requires a careful characterization of the in-vivo cardiac geometry, tissue parameterization though inverse analysis, a robust coupled solver that handles collapsing Lagrangian interfaces, automatic grid-generation algorithms that are capable of accurately discretizing the cardiac geometry, innovations in image analysis, competent and efficient constitutive models and an understanding of the spatial organization of tissue microstructure. In this manuscript, we profile our work toward a comprehensive fluid-structure interaction model of the left heart by reviewing our early work, presenting our current work and laying out our future work in four broad categories: data collection, geometry, fluid-structure interaction and validation.

Adaptive generation of multimaterial grids from imaging data for biomedical Lagrangian fluid-structure simulations.

Spatial discretization of complex imaging- derived fluid-solid geometries, such as the cardiac environment, is a critical but often overlooked challenge in biomechanical computations. This is particularly true in problems with Lagrangian interfaces, where the fluid and solid phases share a common interface geometrically. For simplicity and better accuracy, it is also highly desirable for the two phases to have a matching surface mesh at the interface between them. We outline a method for solving this problem, and illustrate the approach with a 3D fluid-solid mesh of the mouse heart. An MRI dataset of a perfusion-fixed mouse heart with 50 microm isotropic resolution was semi-automatically segmented using a customized multimaterial connected-threshold approach that divided the volume into non-overlapping regions of blood, tissue, and background. Subsequently a multimaterial marching cubes algorithm was applied to the segmented data to produce two detailed, compatible isosurfaces, one for blood and one for tissue. Both isosurfaces were simultaneously smoothed with a multimaterial smoothing algorithm that exactly conserves the volume for each phase. Using these two isosurfaces, we developed and applied novel automated meshing algorithms to generate anisotropic hybrid meshes on arbitrary biological geometries with the number of layers and the desired element anisotropy for each phase as the only input parameters. Since our meshes adapt to the local feature sizes and include boundary layer prisms, they are more efficient and accurate than non-adaptive, isotropic meshes, and the fluid-structure interaction computations will tend to have relative error equilibrated over the whole mesh.

Automatic identification and truncation of boundary outlets in complex imaging-derived biomedical geometries.

Efficient and accurate reconstruction of imaging-derived geometries and subsequent quality mesh generation are enabling technologies for both clinical and research simulations. A challenging part of this process is the introduction of computable, orthogonal boundary patches, namely, the outlets, into treed structures, such as vasculature, arterial or airway trees. We present efficient and robust algorithms for automatically identifying and truncating the outlets for complex geometries. Our approach is based on a conceptual decomposition of objects into tips, segments, and branches, where the tips determine the outlets. We define the tips by introducing a novel concept called the average interior center of curvature and identify the tips that are stable and noise resistant. We compute well-defined orthogonal planes, which truncate the tips into outlets. The rims of the outlets are connected into curves, and the outlets are then closed using Delaunay triangulation. We illustrate the effectiveness and robustness of our approach with a variety of complex lung and coronary artery geometries.

Variational Generation of Prismatic Boundary-Layer Meshes for Biomedical Computing.

Boundary-layer meshes are important for numerical simulations in computational fluid dynamics, including computational biofluid dynamics of air flow in lungs and blood flow in hearts. Generating boundary-layer meshes is challenging for complex biological geometries. In this paper, we propose a novel technique for generating prismatic boundary-layer meshes for such complex geometries. Our method computes a feature size of the geometry, adapts the surface mesh based on the feature size, and then generates the prismatic layers by propagating the triangulated surface using the face-offsetting method. We derive a new variational method to optimize the prismatic layers to improve the triangle shapes and edge orthogonality of the prismatic elements and also introduce simple and effective measures to guarantee the validity of the mesh. Coupled with a high-quality tetrahedral mesh generator for the interior of the domain, our method generates high-quality hybrid meshes for accurate and efficient numerical simulations. We present comparative study to demonstrate the robustness and quality of our method for complex biomedical geometries.