SCIENTIFIC COMPUTING AND IMAGING INSTITUTE
at the University of Utah

A finite element formulation of neutral solute transport across a contact interface between deformable porous media is implemented and validated against analytical solutions. By reducing the integral statements of external virtual work on the two contacting surfaces into a single contact integral, the algorithm automatically enforces continuity of solute molar flux across the contact interface, whereas continuity of the effective solute concentration (a measure of the solute mechano-chemical potential) is achieved using a penalty method. This novel formulation facilitates the analysis of problems in biomechanics where the transport of metabolites across contact interfaces of deformable tissues may be of interest. This contact algorithm is the first to address solute transport across deformable interfaces, and is made available in the public domain, open-source finite element code FEBio (http://www.febio.org).

Keywords: FEBio, Finite element modeling, Contact mechanics, Solute transport, Porous media, Biphasic theory

This paper proposes a novel formulation to model and analyze the statistical characteristics of some types of segmentation problems that are based on combining label maps / templates / atlases. Such segmentation-by-example approaches are quite powerful on their own for several clinical applications and they provide prior information, through spatial context, when combined with intensity-based segmentation methods. The proposed formulation models a class of multiatlas segmentation problems as nonparametric regression problems in the high-dimensional space of images. The paper presents a systematic analysis of the nonparametric estimation's convergence behavior (i.e. characterizing segmentation error as a function of the size of the multiatlas database) and shows that it has a specific analytic form involving several parameters that are fundamental to the specific segmentation problem (i.e. chosen anatomical structure, imaging modality, registration method, label-fusion algorithm, etc.). We describe how to estimate these parameters and show that several brain anatomical structures exhibit the trends determined analytically. The proposed framework also provides per-voxel confidence measures for the segmentation. We show that the segmentation error for large database sizes can be predicted using small-sized databases. Thus, small databases can be exploited to predict the database sizes required (\"how many templates\") to achieve \"good\" segmentations having errors lower than a specified tolerance. Such cost-benefit analysis is crucial for designing and deploying multiatlas segmentation systems.

Background: The medial distal tibial angle (MDTA) is used to determine ankle alignment. The mortise view is the standard to measure MDTA, but the hindfoot alignment view (HAV) has become popular. The MDTA may vary between views, influencing the choice of surgery.

Methods: The MDTA was compared between the mortise and HAV in 146 ankles. MDTA was correlated to age and sagittal tibial tilt for each view. Differences in MDTA by gender and ethnicity were assessed. Diagnostic agreement (varus, valgus, normal) between views was calculated. Clinical assessment of alignment was determined and percent agreement between clinical and radiographic alignment was quantified.

Results: The MDTA measured from the mortise view and HAV radiographs was 89.0 (range, 81 to 96 degrees; SD = 2.8) degrees and 86.0 (range, 73 to 95 degrees; SD = 3.5) degrees, respectively. The MDTA was comparable for both genders for mortise (p = 0.356) and HAV (p = 0.621). The MDTA was comparable in all ethnic groups for mortise view (p = 0.616) and HAV (p = 0.916). Correlation between the measured MDTA and age was not statistically significant for both the mortise (r = 0.118; p = 0.158) and HAV (r = 0.148; p = 0.074). In only 47.3% of all ankles was the radiographic diagnosis of alignment the same between views. Agreement between clinical and radiographic classifications was 60.3% for the mortise view and 52.8% for the HAV.

Conclusion: Substantial disagreement in primary alignment was found between the mortise and HAV as quantified by the MDTA. Agreement between clinical and radiographic alignment was also poor. Clinical Relevance: Advanced imaging such as CT or MRI may better describe ankle alignment.

We introduce several useful utilities in development for the creation and analysis of real wildland fire simulations using WRF and SFIRE. These utilities exist as standalone programs and scripts as well as extensions to other well known software. Python web scrapers automate the process of downloading and preprocessing atmospheric and surface data from common sources. Other scripts simplify the domain setup by creating parameter files automatically. Integration with Google Earth allows users to explore the simulation in a 3D environment along with real surface imagery. Postprocessing scripts provide the user with a number of output data formats compatible with many commonly used visualization suites allowing for the creation of high quality 3D renderings. As a whole, these improvements build toward a unified web application that brings a sophisticated wildland fire modeling environment to scientists and users alike.

With the onset of extreme-scale computing, I/O constraints make it increasingly difficult for scientists to save a sufficient amount of raw simulation data to persistent storage. One potential solution is to change the data analysis pipeline from a post-process centric to a concurrent approach based on either in-situ or in-transit processing. In this context computations are considered in-situ if they utilize the primary compute resources, while in-transit processing refers to offloading computations to a set of secondary resources using asynchronous data transfers. In this paper we explore the design and implementation of three common analysis techniques typically performed on large-scale scientific simulations: topological analysis, descriptive statistics, and visualization. We summarize algorithmic developments, describe a resource scheduling system to coordinate the execution of various analysis workflows, and discuss our implementation using the DataSpaces and ADIOS frameworks that support efficient data movement between in-situ and in-transit computations. We demonstrate the efficiency of our lightweight, flexible framework by deploying it on the Jaguar XK6 to analyze data generated by S3D, a massively parallel turbulent combustion code. Our framework allows scientists dealing with the data deluge at extreme scale to perform analyses at increased temporal resolutions, mitigate I/O costs, and significantly improve the time to insight.

This report provides a summary of the status of the Uintah Computation Framework (UCF) software. Uintah is uniquely equipped to tackle large-scale multi-physics science and engineering problems on disparate length and time scales. The Uintah framework makes it possible to run adaptive computations on modern HPC architectures with tens and now hundreds of thousands of cores with complex communication/memory hierarchies. Uintah was orignally developed in the University of Utah Center for Simulation of Accidental Fires and Explosions (C-SAFE), a DOE-funded academic alliance project and then extended to the broader NSF snd DOE science and engineering communities. As Uintah is applicable to a wide range of engineering problems that involve fl uid-structure interactions with highly deformable structures it is used for a number of NSF-funded and DOE engineering projects. In this report the Uintah framework software is outlined and typical applications are illustrated. Uintah is open-source software that is available through the MIT open-source license at http://www.uintah.utah.edu/.

The task-based approach to software and parallelism is well-known and has been proposed as a potential candidate, named the silver model, for exascale software. This approach is not yet widely used in the large-scale multi-core parallel computing of complex systems of partial differential equations. After surveying task-based approaches we investigate how well the Uintah software and an extension named Wasatch fit in the task-based paradigm and how well they perform on large scale parallel computers. The conclusion is that these approaches show great promise for petascale but that considerable algorithmic challenges remain.

Keywords: DOD, Uintah, CSAFE

The placement of tasks in a parallel application on specific nodes of a supercomputer can significantly impact performance. Traditionally, this task mapping has focused on reducing the distance between communicating tasks on the physical network. This minimizes the number of hops that point-to-point messages travel and thus reduces link sharing between messages and contention. However, for applications that use collectives over sub-communicators, this heuristic may not be optimal. Many collectives can benefit from an increase in bandwidth even at the cost of an increase in hop count, especially when sending large messages. For example, placing communicating tasks in a cube configuration rather than a plane or a line on a torus network increases the number of possible paths messages might take. This increases the available bandwidth which can lead to significant performance gains.

We have developed Rubik, a tool that provides a simple and intuitive interface to create a wide variety of mappings for structured communication patterns. Rubik supports a number of elementary operations such as splits, tilts, or shifts, that can be combined into a large number of unique patterns. Each operation can be applied to disjoint groups of processes involved in collectives to increase the effective bandwidth. We demonstrate the use of Rubik for improving performance of two parallel codes, pF3D and Qbox, which use collectives over sub-communicators.

Ray tracing in scientific visualization allows for substantial gains in performance and rendering quality with large scale polygonal datasets compared to brute-force rasterization, however implementing new rendering architectures into existing tools is often costly and time consuming. This paper presents a library, GLuRay, which intercepts OpenGL calls from many common visualization applications and renders them with the CPU ray tracer Manta without modification to the underlying visualization tool. Rendering polygonal models such as isosurfaces can be done identically to an OpenGL implementation using provided material and camera properties or superior rendering can be achieved using enhanced settings such as dielectric materials or pinhole cameras with depth of field effects. Comparative benchmarks were conducted on the Texas Advanced Computing Center’s Longhorn cluster using the popular visualization packages ParaView, VisIt, Ensight, and VAPOR. Through the parallel ren- dering package ParaView, scaling up to 64 nodes is demonstrated. With our tests we show that using OpenGL interception to accelerate and enhance visualization programs provides a viable enhancement to existing tools with little overhead and no code modification while allowing for the creation of publication quality renderings using advanced effects and greatly improved large-scale software rendering performance within tools that scientists are currently using.

Keywords: kaust, scidac

Large-scale analysis and visualization is becoming increasingly important as supercomputers and their simulations produce larger and larger data. These large data sizes are pushing the limits of traditional rendering algorithms and tools thus motivating a study exploring these limits and their possible resolutions through alternative rendering algorithms . In order to better understand real-world performance with large data, this paper presents a detailed timing study on a large cluster with the widely used visualization tools ParaView and VisIt. The software ray tracer Manta was integrated into these programs in order to show that improved performance could be attained with software ray tracing on a distributed memory, GPU enabled, parallel visualization resource. Using the Texas Advanced Computing Center’s Longhorn cluster which has multi-core CPUs and GPUs with large-scale polygonal data, we find multi-core CPU ray tracing to be significantly faster than both software rasterization and hardware-accelerated rasterization in existing scientific visualization tools with large data.

Keywords: kaust, scidac

We introduce a new algorithm for generating tetrahedral meshes that conform to physical boundaries in volumetric domains consisting of multiple materials. The proposed method allows for an arbitrary number of materials, produces high-quality tetrahedral meshes with upper and lower bounds on dihedral angles, and guarantees geometric delity. Moreover, the method is combinatoric so its implementation enables rapid mesh construction. These meshes are structured in a way that also allows grading, in order to reduce element counts in regions of homogeneity.

We present a particle-based approach for generating adaptive triangular surface and tetrahedral volume meshes from computer-aided design models. Input shapes are treated as a collection of smooth, parametric surface patches that can meet non-smoothly on boundaries. Our approach uses a hierarchical sampling scheme that places particles on features in order of increasing dimensionality. These particles reach a good distribution by minimizing an energy computed in 3D world space, with movements occurring in the parametric space of each surface patch. Rather than using a pre-computed measure of feature size, our system automatically adapts to both curvature as well as a notion of topological separation. It also enforces a measure of smoothness on these constraints to construct a sizing field that acts as a proxy to piecewise-smooth feature size. We evaluate our technique with comparisons against other popular triangular meshing techniques for this domain.

In recent years there has been significant growth in the use of patient-specific models to predict the effects of deep brain stimulation (DBS). However, translating these models from a research environment to the everyday clinical workflow has been a challenge. In this paper, we deploy the interactive visualization system ImageVis3D Mobile in an evaluation environment to visualize models of Parkinson’s disease patients who received DBS therapy. We used ImageVis3D Mobile to provide models to movement disorders clinicians and asked them to use the software to determine: 1) which of the four DBS electrode contacts they would select for therapy; and 2) what stimulation settings they would choose. We compared the stimulation protocol chosen from the software versus the stimulation protocol that was chosen via clinical practice (independently of the study). Lastly, we compared the amount of time required to reach these settings using the software versus the time required through standard practice. We found that the stimulation settings chosen using ImageVis3D Mobile were similar to those used in standard of care, but were selected in drastically less time. We show how our visualization system can be used to guide clinical decision making for selection of DBS settings.

Keywords: scidac, dbs

As neuromodulation therapy has grown, so has the recognition that computational models can provide important insights into the design, operation, and clinical application of neurostimulation systems. Models of deep brain stimulation and spinal cord stimulation have advanced over recent decades from simple, stereotyped models to sophisticated patient-specific models that can incorporate many important details of the stimulation system and the attributes of individual subjects. Models have been used to make detailed predictions of the bioelectric fields produced during stimulation. These predictions have been used as a starting point for further analyses such as stimulation safety, neural response, neurostimulation system design, or clinical outcomes. This chapter provides a review of recent advances and anticipated future directions in computational modeling of neuromodulation.

Background: Increasing evidence suggests that autism is a disorder of distributed neural networks that may exhibit abnormal developmental trajectories. Characterization of white matter early in the developmental course of the disorder is critical to understanding these aberrant trajectories.

Methods: A cross-sectional study of 2-6 year old children with autism was conducted using diffusion tensor imaging combined with a novel statistical approach employing fractional anisotropy distributions. 58 children aged 18-79 months were imaged: 33 were diagnosed with autism, 8 with general developmental delay (DD), and 17 were typically developing (TD). Fractional anisotropy values within global white matter, cortical lobes, and the cerebellum were measured and transformed to random F distributions for each subject. Each distribution of values for a region was summarized by estimating delta, the estimated mean and standard deviation of the approximating F for each distribution.

Results: The estimated delta parameter, delta-hat, was significantly decreased in individuals with autism compared to the combined control group. This was true in all cortical lobes, as well as in the cerebellum, but differences were strongest in the temporal lobe. Predicted developmental trajectories of delta-hat across the age range in the sample showed patterns that partially distinguished the groups. Exploratory analyses suggested that the variability, rather than the central tendency, component of delta-hat was the driving force behind these results. Conclusions: White matter in young children with autism appears to be abnormally homogeneous, which may reflect poorly organized or differentiated pathways, particularly in the temporal lobe, which is important for social and emotional cognition.

Objective
During neovascularization, the end result is a new functional microcirculation composed of a network of mature microvessels with specific topologies. Although much is known concerning the mechanisms underlying the initiation of angiogenesis, it remains unclear how the final architecture of microcirculatory beds is regulated. To begin to address this, we determined the impact of angiogenic neovessel prepatterning on the final microvascular network topology using a model of implant neovascularization.

Methods and Results
We used 3D direct-write bioprinting or physical constraints in a manner permitting postangiogenesis vascular remodeling and adaptation to pattern angiogenic microvascular precursors (neovessels formed from isolated microvessel segments) in 3D collagen gels before implantation and subsequent network formation. Neovasculatures prepatterned into parallel arrays formed functional networks after 4 weeks postimplantation but lost the prepatterned architecture. However, maintenance of uniaxial physical constraints during postangiogenesis remodeling of the implanted neovasculatures produced networks with aligned microvessels, as well as an altered proportional distribution of arterioles, capillaries, and venules.

Conclusion
Here we show that network topology resulting from implanted microvessel precursors is independent from prepatterning of precursors but can be influenced by a patterning stimulus involving tissue deformation during postangiogenesis remodeling and maturation.

Liquid–liquid extraction is a typical multi-fluid problem in chemical engineering where two types of immiscible fluids are mixed together. Mixing of two-phase fluids results in a time-varying fluid density distribution, quantitatively indicating the presence of liquid phases. For engineers who design extraction devices, it is crucial to understand the density distribution of each fluid, particularly flow regions that have a high concentration of the dispersed phase. The propagation of regions of high density can be studied by examining the topology of isosurfaces of the density data. We present a topology-based approach to track the splitting and merging events of these regions using the Reeb graphs. Time is used as the third dimension in addition to two-dimensional (2D) point-based simulation data. Due to low time resolution of the input data set, a physics-based interpolation scheme is required in order to improve the accuracy of the proposed topology tracking method. The model used for interpolation produces a smooth time-dependent density field by applying Lagrangian-based advection to the given simulated point cloud data, conforming to the physical laws of flow evolution. Using the Reeb graph, the spatial and temporal locations of bifurcation and merging events can be readily identified supporting in-depth analysis of the extraction process.