Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Deep brain stimulation
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.

SCI Publications

2006


 M. Styner, M. Jomier, G. Gerig. “Closed and Open Source Neuroimage Analysis Tools and Libraries at UNC,” In Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI), Special Session: Open Source, pp. 702--705. 2006.


D.M. Tartakovsky, D. Xiu. “Stochastic Analysis of Transport in Tubes with Rough Walls,” In Journal of Computational Physics, Vol. 217, No. 1, pp. 248--259. 2006.
DOI: 10.1016/j.jcp.2006.02.029

ABSTRACT

Flow and transport in tubes with rough surfaces play an important role in a variety of applications. Often the topology of such surfaces cannot be accurately described in all of its relevant details due to either insufficient data or measurement errors or both. In such cases, this topological uncertainty can be efficiently handled by treating rough boundaries as random fields, so that an underlying physical phenomenon is described by deterministic or stochastic differential equations in random domains. To deal with this class of problems, we use a computational framework, which is based on stochastic mappings to transform the original deterministic/stochastic problem in a random domain into a stochastic problem in a deterministic domain. The latter problem has been studied more extensively and existing analytical/numerical techniques can be readily applied. In this paper, we employ both a generalized polynomial chaos and Monte Carlo simulations to solve the transformed stochastic problem. We use our approach to describe transport of a passive scalar in Stokes' flow and to quantify the corresponding predictive uncertainty.

Keywords: Random domain, Stochastic inputs, Differential equations, Uncertainty quantification, Stokes flow, Dispersion


 X. Tricoche, X. Zheng, A. Pang. “Visualizing the Topology of Symmetric, Second-Order, Time-varying Two-Dimensional Tensor Fields,” In Visualization and Processing of Tensor Fields, Springer, pp. 225--240. 2006.


X. Tricoche, C. Garth. “Topological Methods for Visualizing Vortical Flows,” In Mathematical Foundations of Visualization, Computer Graphics, and Massive Data Exploration, Springer, 2006.


A.I. Veress, W.P. Segars, J.A. Weiss, B.M.W. Tsui, G.T. Gullberg. “Normal and Pathological NCAT Image and Phantom Data Based on Physiologically Realistic Left Ventricle Finite Element Models,” In IEEE Transactions on Medical Imaging, Vol. 25, No. 12, pp. 1604--1616. 2006.


K. Vieira, A. Silva, N. Pinto, E.S. Moura, J. Cavalcanti, J. Freire. “A Fast and Robust Method for Web Page Template Detection and Removal,” In Proceedings of the 15th ACM International Conference on Information and Knowledge Management (CIKM), Arlington, VA, pp. 258--267. November, 2006.


I. Wald, V. Havran. “On building fast kd-Trees for Ray Tracing, and on doing that in O(N log N),” SCI Institute Technical Report, No. UUSCI-2006-009, University of Utah, 2006.


I. Wald, T. Ize, A. Kensler, A. Knoll, S.G. Parker. “Ray Tracing Animated Scenes using Coherent Grid Traversal,” In Proceedings of the ACM SIGGRAPH 2006 Conference, ACM, New York, NY, USA pp. 485--493. 2006.
ISSN: 0730-0301
DOI: 10.1145/1179352.1141913


I. Wald, S. Boulosy, P. Shirley. “Ray Tracing Deformable Scenes using Dynamic Bounding Volume Hierarchies,” SCI Institute Technical Report, No. UUSCI-2006-015, University of Utah, 2006.


I. Wald, T. Ize, A. Kensler, A. Knoll, S.G. Parker. “Ray Tracing Animated Scenes using Coherent Grid Traversal,” SCI Institute Technical Report, No. UUSCI-2006-014, University of Utah, 2006.


I. Wald, S. Boulos, P. Shirley. “Ray Tracing Deformable Scenes using Dynamic Bounding Volume Hierarchies,” SCI Institute Technical Report, No. UUSCI-2006-023, Note: Updated version of UUSCI-2006-015, University of Utah, 2006.


I. Wald. “Realtime Ray Tracing and Interactive Global Illumination,” In IT - Information Technology, Note: Invited article in german., 2006.


I. Wald, A. Dietrich, C. Benthin, A. Efremov, T. Dahmen, J. Gunther, V. Havran, P. Slusallek, H.-P. Seidel. “Applying Ray Tracing for Virtual Reality and Industrial Design,” In Proceedings of the 2006 IEEE Symposium on Interactive Ray Tracing, pp. 177--185. 2006.


J.A. Weiss, B.J. Maakestad. “Permeability of Human Medial Collateral Ligament Transverse to the Collagen Fiber Direction,” In J. Biomech., Vol. 39, No. 2, pp. 276--283. 2006.


J.A. Weiss, A.I. Veress, G.T. Gullberg, N.S. Phatak, Q. Sun, D.L. Parker, R.D. Rabbitt. “Strain measurement using deformable image registration,” In Mechanics of Biological Tissue, Edited by G.A. Holzapfel, R.W. Ogden, Springer, pp. 489--501. 2006.
DOI: 10.1007/3-540-31184-x_35


C.H. Wolters, A. Anwander, X. Tricoche, D.M. Weinstein, M.A. Koch, R.S. MacLeod. “Influence of Tissue Conductivity Anisotropy on EEG/MEG Field and Return Current Computation in a Realistic Head Model: A Simulation and Visualization Study Using High-Resolution Finite Element Modeling,” In Neuroimage, Vol. 30, No. 3, pp. 813--826. April, 2006.


C.H. Wolters, A. Anwander, G. Berti, U. Hartmann. “Geometry-Adapted Hexahedral Meshes Improve Accuracy of Finite Element Method Based EEG Source Analysis,” In IEEE Transactions on Biomedical Engineering, Vol. 54, No. 8, pp. 1446--1453. August, 2006.


D. Güllmar, J. Haueisen, M. Eiselt, F. Giessler, L. Flemming, A. Anwander, T. Knösche, C.H. Wolters, M. Dümpelmann, D.S. Tuch, J.R. Reichenbach. “Influence of Anisotropic Conductivity on EEG Source Reconstruction: Investigations in a Rabbit Model,” In IEEE Trans. Biomed. Eng., Vol. 53, No. 9, pp. 1841--1850. 2006.


C. Wyman, S.G. Parker, P. Shirley, C.D. Hansen. “Interactive Display of Isosurfaces with Global Illumination,” In IEEE Transactions on Visualization and Comptuer Graphics, Vol. 12, No. 2, March/April, 2006.


D. Xiu, D.M. Tartakovsky. “Numerical Methods for Differential Equations in Random Domains,” In SIAM Journal on Scientific Computing, Vol. 28, No. 3, pp. 1167--1185. 2006.
DOI: 10.1137/040613160

ABSTRACT

Physical phenomena in domains with rough boundaries play an important role in a variety of applications. Often the topology of such boundaries cannot be accurately described in all of its relevant detail due to either insufficient data or measurement errors or both. This topological uncertainty can be efficiently handled by treating rough boundaries as random fields, so that an underlying physical phenomenon is described by deterministic or stochastic differential equations in random domains. To deal with this class of problems, we propose a novel computational framework, which is based on using stochastic mappings to transform the original deterministic/stochastic problem in a random domain into a stochastic problem in a deterministic domain. The latter problem has been studied more extensively, and existing analytical/numerical techniques can be readily applied. In this paper, we employ both a stochastic Galerkin method and Monte Carlo simulations to solve the transformed stochastic problem. We demonstrate our approach by applying it to an elliptic problem in single- and double-connected random domains, and comment on the accuracy and convergence of the numerical methods.

Keywords: random domain, stochastic inputs, differential equations, uncertainty quantification