Charles HansenVolume RenderingRay Tracing Graphics |
Valerio PascucciTopological MethodsData Streaming Big Data |
Chris JohnsonVolume RenderingUncertainty Visualization |
Mike KirbyUncertainty Visualization |
Ross WhitakerTopological MethodsUncertainty Visualization |
Topological and Statistical Methods for Complex Data, Subtitled “Tackling Large-Scale, High-Dimensional, and Multivariate Data Spaces,” J. Bennett, F. Vivodtzev, V. Pascucci (Eds.). Mathematics and Visualization, 2015. ISBN: 978-3-662-44899-1 This book contains papers presented at the Workshop on the Analysis of Large-scale, |
Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization, C.D. Hansen, M. Chen, C.R. Johnson, A.E. Kaufman, H. Hagen (Eds.). Mathematics and Visualization, Springer, 2014. ISBN: 978-1-4471-6496-8 |
Topological Methods in Data Analysis and Visualization III P.-T. Bremer, I. Hotz, V. Pascucci, R. Peikert. Mathematics and Visualization, 2014. ISBN: 978-3-319-04099-8 |
The Natural Helmholtz-Hodge Decomposition For Open-Boundary Flow Analysis H. Bhatia, V. Pascucci, P.-T. Bremer. In IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol. 99, pp. 1566--1578. 2014. DOI: 10.1109/TVCG.2014.2312012 The Helmholtz-Hodge decomposition (HHD) describes a flow as the sum of an incompressible, an irrotational, and a harmonic flow, and is a fundamental tool for simulation and analysis. Unfortunately, for bounded domains, the HHD is not uniquely defined, and traditionally, boundary conditions are imposed to obtain a unique solution. However, in general, the boundary conditions used during the simulation may not be known and many simulations use open boundary conditions. In these cases, the flow imposed by traditional boundary conditions may not be compatible with the given data, which leads to sometimes drastic artifacts and distortions in all three components, hence producing unphysical results. Instead, this paper proposes the natural HHD, which is defined by separating the flow into internal and external components. Using a completely data-driven approach, the proposed technique obtains uniqueness without assuming boundary conditions a priori. As a result, it enables a reliable and artifact-free analysis for flows with open boundaries or unknown boundary conditions. Furthermore, our approach computes the HHD on a point-wise basis in contrast to the existing global techniques, and thus supports computing inexpensive local approximations for any subset of the domain. Finally, the technique is easy to implement for a variety of spatial discretizations and interpolated fields in both two and three dimensions. |
Extracting Features from Time-Dependent Vector Fields Using Internal Reference Frames H. Bhatia, V. Pascucci, R.M. Kirby, P.-T. Bremer. In Computer Graphics Forum, Vol. 33, No. 3, pp. 21--30. June, 2014. DOI: 10.1111/cgf.12358 Extracting features from complex, time-dependent flow fields remains a significant challenge despite substantial research efforts, especially because most flow features of interest are defined with respect to a given reference frame. Pathline-based techniques, such as the FTLE field, are complex to implement and resource intensive, whereas scalar transforms, such as λ_{2}, often produce artifacts and require somewhat arbitrary thresholds. Both approaches aim to analyze the flow in a more suitable frame, yet neither technique explicitly constructs one. |
Ovis: A Framework for Visual Analysis of Ocean Forecast Ensembles T. Hollt, A. Magdy, P. Zhan, G. Chen, G. Gopalakrishnan, I. Hoteit, C.D. Hansen, M. Hadwiger. In IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol. PP, No. 99, pp. 1. 2014. DOI: 10.1109/TVCG.2014.2307892 We present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations of the sea surface height that is used in ocean forecasting. The position of eddies can be derived directly from the sea surface height and our visualization approach enables their interactive exploration and analysis. The behavior of eddies is important in different application settings of which we present two in this paper. First, we show an application for interactive planning of placement as well as operation of off-shore structures using real-world ensemble simulation data of the Gulf of Mexico. Off-shore structures, such as those used for oil exploration, are vulnerable to hazards caused by eddies, and the oil and gas industry relies on ocean forecasts for efficient operations. We enable analysis of the spatial domain, as well as the temporal evolution, for planning the placement and operation of structures. Eddies are also important for marine life. They transport water over large distances and with it also heat and other physical properties as well as biological organisms. In the second application we present the usefulness of our tool, which could be used for planning the paths of autonomous underwater vehicles, so called gliders, for marine scientists to study simulation data of the largely unexplored Red Sea. Keywords: Ensemble Visualization, Ocean Visualization, Ocean Forecast, Risk Estimation |
DTIPrep: Quality Control of Diffusion-Weighted Images I. Oguz, M. Farzinfar, J. Matsui, F. Budin, Z. Liu, G. Gerig, H.J. Johnson, M.A. Styner. In Frontiers in Neuroinformatics, Vol. 8, No. 4, 2014. DOI: 10.3389/fninf.2014.00004 In the last decade, diffusion MRI (dMRI) studies of the human and animal brain have been used to investigate a multitude of pathologies and drug-related effects in neuroscience research. Study after study identifies white matter (WM) degeneration as a crucial biomarker for all these diseases. The tool of choice for studying WM is dMRI. However, dMRI has inherently low signal-to-noise ratio and its acquisition requires a relatively long scan time; in fact, the high loads required occasionally stress scanner hardware past the point of physical failure. As a result, many types of artifacts implicate the quality of diffusion imagery. Using these complex scans containing artifacts without quality control (QC) can result in considerable error and bias in the subsequent analysis, negatively affecting the results of research studies using them. However, dMRI QC remains an under-recognized issue in the dMRI community as there are no user-friendly tools commonly available to comprehensively address the issue of dMRI QC. As a result, current dMRI studies often perform a poor job at dMRI QC. Thorough QC of diffusion MRI will reduce measurement noise and improve reproducibility, and sensitivity in neuroimaging studies; this will allow researchers to more fully exploit the power of the dMRI technique and will ultimately advance neuroscience. Therefore, in this manuscript, we present our open-source software, DTIPrep, as a unified, user friendly platform for thorough quality control of dMRI data. These include artifacts caused by eddy-currents, head motion, bed vibration and pulsation, venetian blind artifacts, as well as slice-wise and gradient-wise intensity inconsistencies. This paper summarizes a basic set of features of DTIPrep described earlier and focuses on newly added capabilities related to directional artifacts and bias analysis. Keywords: diffusion MRI, Diffusion Tensor Imaging, Quality control, Software, open-source, preprocessing |
International Journal for Uncertainty Quantification, Subtitled “Special Issue on Working with Uncertainty: Representation, Quantification, Propagation, Visualization, and Communication of Uncertainty,” C.R. Johnson, A. Pang (Eds.). Vol. 3, No. 3, Begell House, Inc., 2013. ISSN: 2152-5080 DOI: 10.1615/Int.J.UncertaintyQuantification.v3.i3 |
International Journal for Uncertainty Quantification, Subtitled “Special Issue on Working with Uncertainty: Representation, Quantification, Propagation, Visualization, and Communication of Uncertainty,” C.R. Johnson, A. Pang (Eds.). Vol. 3, No. 2, Begell House, Inc., 2013. ISSN: 2152-5080 DOI: 10.1615/Int.J.UncertaintyQuantification.v3.i2 |
Uncertainty Visualization in HARDI based on Ensembles of ODFs F. Jiao, J.M. Phillips, Y. Gur, C.R. Johnson. In Proceedings of 2013 IEEE Pacific Visualization Symposium, pp. 193--200. 2013. PubMed ID: 24466504 PubMed Central ID: PMC3898522 In this paper, we propose a new and accurate technique for uncertainty analysis and uncertainty visualization based on fiber orientation distribution function (ODF) glyphs, associated with high angular resolution diffusion imaging (HARDI). Our visualization applies volume rendering techniques to an ensemble of 3D ODF glyphs, which we call SIP functions of diffusion shapes, to capture their variability due to underlying uncertainty. This rendering elucidates the complex heteroscedastic structural variation in these shapes. Furthermore, we quantify the extent of this variation by measuring the fraction of the volume of these shapes, which is consistent across all noise levels, the certain volume ratio. Our uncertainty analysis and visualization framework is then applied to synthetic data, as well as to HARDI human-brain data, to study the impact of various image acquisition parameters and background noise levels on the diffusion shapes. |
Topology analysis of time-dependent multi-fluid data using the Reeb graph F. Chen, H. Obermaier, H. Hagen, B. Hamann, J. Tierny, V. Pascucci. In Computer Aided Geometric Design, Vol. 30, No. 6, pp. 557--566. 2013. DOI: 10.1016/j.cagd.2012.03.019 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. Keywords: Multi-phase fluid, Level set, Topology method, Point-based multi-fluid simulation |