Charles HansenVolume RenderingRay Tracing Graphics |
Valerio PascucciTopological MethodsData Streaming Big Data |
Chris JohnsonScalar, Vector, andTensor Field Visualization, Uncertainty Visualization |
Mike KirbyUncertainty Visualization |
Ross WhitakerTopological MethodsUncertainty Visualization |
Miriah MeyerInformation Visualization |
Yarden LivnatInformation Visualization |
Alex LexInformation Visualization |
Bei WangInformation VisualizationScientific Visualization Topological Data Analysis |
Adaptive Sampling with Topological Scores D. Maljovec, Bei Wang, A. Kupresanin, G. Johannesson, V. Pascucci, P.-T. Bremer. In Int. J. Uncertainty Quantification, Vol. 3, No. 2, Begell House, pp. 119--141. 2013. DOI: 10.1615/int.j.uncertaintyquantification.2012003955 Understanding and describing expensive black box functions such as physical simulations is a common problem in many application areas. One example is the recent interest in uncertainty quantification with the goal of discovering the relationship between a potentially large number of input parameters and the output of a simulation. Typically, the simulation of interest is expensive to evaluate and thus the sampling of the parameter space is necessarily small. As a result choosing a "good" set of samples at which to evaluate is crucial to glean as much information as possible from the fewest samples. While space-filling sampling designs such as Latin hypercubes provide a good initial cover of the entire domain, more detailed studies typically rely on adaptive sampling: Given an initial set of samples, these techniques construct a surrogate model and use it to evaluate a scoring function which aims to predict the expected gain from evaluating a potential new sample. There exist a large number of different surrogate models as well as different scoring functions each with their own advantages and disadvantages. In this paper we present an extensive comparative study of adaptive sampling using four popular regression models combined with six traditional scoring functions compared against a space-filling design. Furthermore, for a single high-dimensional output function, we introduce a new class of scoring functions based on global topological rather than local geometric information. The new scoring functions are competitive in terms of the root mean squared prediction error but are expected to better recover the global topological structure. Our experiments suggest that the most common point of failure of adaptive sampling schemes are ill-suited regression models. Nevertheless, even given well-fitted surrogate models many scoring functions fail to outperform a space-filling design. |
Diffusion imaging quality control via entropy of principal direction distribution, M. Farzinfar, I. Oguz, R.G. Smith, A.R. Verde, C. Dietrich, A. Gupta, M.L. Escolar, J. Piven, S. Pujol, C. Vachet, S. Gouttard, G. Gerig, S. Dager, R.C. McKinstry, S. Paterson, A.C. Evans, M.A. Styner. In NeuroImage, Vol. 82, pp. 1--12. 2013. ISSN: 1053-8119 DOI: 10.1016/j.neuroimage.2013.05.022 Diffusion MR imaging has received increasing attention in the neuroimaging community, as it yields new insights into the microstructural organization of white matter that are not available with conventional MRI techniques. While the technology has enormous potential, diffusion MRI suffers from a unique and complex set of image quality problems, limiting the sensitivity of studies and reducing the accuracy of findings. Furthermore, the acquisition time for diffusion MRI is longer than conventional MRI due to the need for multiple acquisitions to obtain directionally encoded Diffusion Weighted Images (DWI). This leads to increased motion artifacts, reduced signal-to-noise ratio (SNR), and increased proneness to a wide variety of artifacts, including eddy-current and motion artifacts, “venetian blind” artifacts, as well as slice-wise and gradient-wise inconsistencies. Such artifacts mandate stringent Quality Control (QC) schemes in the processing of diffusion MRI data. Most existing QC procedures are conducted in the DWI domain and/or on a voxel level, but our own experiments show that these methods often do not fully detect and eliminate certain types of artifacts, often only visible when investigating groups of DWI's or a derived diffusion model, such as the most-employed diffusion tensor imaging (DTI). Here, we propose a novel regional QC measure in the DTI domain that employs the entropy of the regional distribution of the principal directions (PD). The PD entropy quantifies the scattering and spread of the principal diffusion directions and is invariant to the patient's position in the scanner. High entropy value indicates that the PDs are distributed relatively uniformly, while low entropy value indicates the presence of clusters in the PD distribution. The novel QC measure is intended to complement the existing set of QC procedures by detecting and correcting residual artifacts. Such residual artifacts cause directional bias in the measured PD and here called dominant direction artifacts. Experiments show that our automatic method can reliably detect and potentially correct such artifacts, especially the ones caused by the vibrations of the scanner table during the scan. The results further indicate the usefulness of this method for general quality assessment in DTI studies. Keywords: Diffusion magnetic resonance imaging, Diffusion tensor imaging, Quality assessment, Entropy |
The Helmholtz-Hodge Decomposition - A Survey H. Bhatia, G. Norgard, V. Pascucci, P.-T. Bremer. In IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol. 19, No. 8, Note: Selected as Spotlight paper for August 2013 issue, pp. 1386--1404. 2013. DOI: 10.1109/TVCG.2012.316 The Helmholtz-Hodge Decomposition (HHD) describes the decomposition of a flow field into its divergence-free and curl-free components. Many researchers in various communities like weather modeling, oceanology, geophysics, and computer graphics are interested in understanding the properties of flow representing physical phenomena such as incompressibility and vorticity. The HHD has proven to be an important tool in the analysis of fluids, making it one of the fundamental theorems in fluid dynamics. The recent advances in the area of flow analysis have led to the application of the HHD in a number of research communities such as flow visualization, topological analysis, imaging, and robotics. However, because the initial body of work, primarily in the physics communities, research on the topic has become fragmented with different communities working largely in isolation often repeating and sometimes contradicting each others results. |
Visualizing Robustness of Critical Points for 2D Time-Varying Vector Fields, Bei Wang, P. Rosen, P. Skraba, H. Bhatia, V. Pascucci. In Computer Graphics Forum, Vol. 32, No. 3, Wiley-Blackwell, pp. 221--230. jun, 2013. DOI: 10.1111/cgf.12109 Analyzing critical points and their temporal evolutions plays a crucial role in understanding the behavior of vector fields. A key challenge is to quantify the stability of critical points: more stable points may represent more important phenomena or vice versa. The topological notion of robustness is a tool which allows us to quantify rigorously the stability of each critical point. Intuitively, the robustness of a critical point is the minimum amount of perturbation necessary to cancel it within a local neighborhood, measured under an appropriate metric. In this paper, we introduce a new analysis and visualization framework which enables interactive exploration of robustness of critical points for both stationary and time-varying 2D vector fields. This framework allows the end-users, for the first time, to investigate how the stability of a critical point evolves over time. We show that this depends heavily on the global properties of the vector field and that structural changes can correspond to interesting behavior. We demonstrate the practicality of our theories and techniques on several datasets involving combustion and oceanic eddy simulations and obtain some key insights regarding their stable and unstable features. |
Morse-Smale Regression S. Gerber, O. Reubel, P.-T. Bremer, V. Pascucci, R.T. Whitaker. In Journal of Computational and Graphical Statistics, Vol. 22, No. 1, pp. 193--214. 2013. DOI: 10.1080/10618600.2012.657132 This paper introduces a novel partition-based regression approach that incorporates topological information. Partition-based regression typically introduce a quality-of-fit-driven decomposition of the domain. The emphasis in this work is on a topologically meaningful segmentation. Thus, the proposed regression approach is based on a segmentation induced by a discrete approximation of the Morse-Smale complex. This yields a segmentation with partitions corresponding to regions of the function with a single minimum and maximum that are often well approximated by a linear model. This approach yields regression models that are amenable to interpretation and have good predictive capacity. Typically, regression estimates are quantified by their geometrical accuracy. For the proposed regression, an important aspect is the quality of the segmentation itself. Thus, this paper introduces a new criterion that measures the topological accuracy of the estimate. The topological accuracy provides a complementary measure to the classical geometrical error measures and is very sensitive to over-fitting. The Morse-Smale regression is compared to state-of-the-art approaches in terms of geometry and topology and yields comparable or improved fits in many cases. Finally, a detailed study on climate-simulation data demonstrates the application of the Morse-Smale regression. Supplementary materials are available online and contain an implementation of the proposed approach in the R package msr, an analysis and simulations on the stability of the Morse-Smale complex approximation and additional tables for the climate-simulation study. |
Comments on the “Meshless Helmholtz-Hodge decomposition” H. Bhatia, G. Norgard, V. Pascucci, P.-T. Bremer. In IEEE Transactions on Visualization and Computer Graphics, Vol. 19, No. 3, pp. 527--528. 2013. DOI: 10.1109/TVCG.2012.62 The Helmholtz-Hodge decomposition (HHD) is one of the fundamental theorems of fluids describing the decomposition of a flow field into its divergence-free, curl-free and harmonic components. Solving for an HDD is intimately connected to the choice of boundary conditions which determine the uniqueness and orthogonality of the decomposition. This article points out that one of the boundary conditions used in a recent paper \"Meshless Helmholtz-Hodge decomposition\" [5] is, in general, invalid and provides an analytical example demonstrating the problem. We hope that this clarification on the theory will foster further research in this area and prevent undue problems in applying and extending the original approach. |
enRoute: Dynamic Path Extraction from Biological Pathway Maps for In-Depth Experimental Data Analysis C Partl, A Lex, M Streit, D Kalkofen, K Kashofer, D Schmalstieg. In Proceedings of the IEEE Symposium on Biological Data Visualization (BioVis '12), IEEE, pp. 107--114. 2012. DOI: 10.1109/BioVis.2012.6378600 Pathway maps are an important source of information when analyzing functional implications of experimental data on biological processes. However, associating large quantities of data with nodes on a pathway map and allowing in depth-analysis at the same time is a challenging task. While a wide variety of approaches for doing so exist, they either do not scale beyond a few experiments or fail to represent the pathway appropriately. To remedy this, we introduce enRoute, a new approach for interactively exploring experimental data along paths that are dynamically extracted from pathways. By showing an extracted path side-by-side with experimental data, enRoute can present large amounts of data for every pathway node. It can visualize hundreds of samples, dozens of experimental conditions, and even multiple datasets capturing different aspects of a node at the same time. Another important property of this approach is its conceptual compatibility with arbitrary forms of pathways. Most notably, enRoute works well with pathways that are manually created, as they are available in large, public pathway databases. We demonstrate enRoute with pathways from the well-established KEGG database and expression as well as copy number datasets from humans and mice with more than 1,000 experiments. We validate enRoute using case studies with domain experts, who used enRoute to explore data for glioblastoma multiforme in humans and a model of steatohepatitis in mice. |
StratomeX: Visual Analysis of Large-Scale Heterogeneous Genomics Data for Cancer Subtype Characterization A. Lex, M. Streit, H. Schulz, C. Partl, D. Schmalstieg, P.. Park, N. Gehlenborg. In Computer Graphics Forum (EuroVis '12), Vol. 31, No. 3, pp. 1175--1184. 2012. ISSN: 0167-7055 DOI: 10.1111/j.1467-8659.2012.03110.x dentification and characterization of cancer subtypes are important areas of research that are based on the integrated analysis of multiple heterogeneous genomics datasets. Since there are no tools supporting this process, much of this work is done using ad-hoc scripts and static plots, which is inefficient and limits visual exploration of the data. To address this, we have developed StratomeX, an integrative visualization tool that allows investigators to explore the relationships of candidate subtypes across multiple genomic data types such as gene expression, DNA methylation, or copy number data. StratomeX represents datasets as columns and subtypes as bricks in these columns. Ribbons between the columns connect bricks to show subtype relationships across datasets. Drill-down features enable detailed exploration. StratomeX provides insights into the functional and clinical implications of candidate subtypes by employing small multiples, which allow investigators to assess the effect of subtypes on molecular pathways or outcomes such as patient survival. As the configuration of viewing parameters in such a multi-dataset, multi-view scenario is complex, we propose a meta visualization and configuration interface for dataset dependencies and data-view relationships. StratomeX is developed in close collaboration with domain experts. We describe case studies that illustrate how investigators used the tool to explore subtypes in large datasets and demonstrate how they efficiently replicated findings from the literature and gained new insights into the data. |