SCI Publications

2010


M. Berger, L.G. Nonato, V. Pascucci, C.T. Silva. “Fiedler Trees for Multiscale Surface Analysis,” In Computer & Graphics, Vol. 34, No. 3, Note: Special Issue of Sha, pp. 272--281. June, 2010.
DOI: 10.1016/j.cag.2010.03.009

ABSTRACT

In this work we introduce a new hierarchical surface decomposition method for multiscale analysis of surface meshes. In contrast to other multiresolution methods, our approach relies on spectral properties of the surface to build a binary hierarchical decomposition. Namely, we utilize the first nontrivial eigenfunction of the Laplace–Beltrami operator to recursively decompose the surface. For this reason we coin our surface decomposition the Fiedler tree. Using the Fiedler tree ensures a number of attractive properties, including: mesh-independent decomposition, well-formed and nearly equi-areal surface patches, and noise robustness. We show how the evenly distributed patches can be exploited for generating multiresolution high quality uniform meshes. Additionally, our decomposition permits a natural means for carrying out wavelet methods, resulting in an intuitive method for producing feature-sensitive meshes at multiple scales.


M. Berzins, J. Luitjens, Q. Meng, T. Harman, C.A. Wight, J.R. Peterson. “Uintah: A Scalable Framework for Hazard Analysis,” In Proceedings of the Teragrid 2010 Conference, TG 10, Note: Awarded Best Paper in the Science Track!, pp. (published online). July, 2010.
ISBN: 978-1-60558-818-6
DOI: 10.1145/1838574.1838577

ABSTRACT

The Uintah Software system was developed to provide an environment for solving a fluid-structure interaction problems on structured adaptive grids on large-scale, long-running, data-intensive problems. Uintah uses a novel asynchronous task-based approach with fully automated load balancing. The application of Uintah to a petascale problem in hazard analysis arising from "sympathetic" explosions in which the collective interactions of a large ensemble of explosives results in dramatically increased explosion violence, is considered. The advances in scalability and combustion modeling needed to begin to solve this problem are discussed and illustrated by prototypical computational results.

Keywords: Uintah, csafe


M. Berzins. “Nonlinear Data-Bounded Polynomial Approximations and their Applications in ENO Methods,” In Numerical Algorithms, Vol. 55, No. 2, pp. 171. 2010.


J.J.E. Blauer, J. Cates, C.J. McGann, E.G. Kholmovski, A. Alexander, M.W. Prastawa, S. Joshi, N.F. Marrouche, R.S. MacLeod. “MRI Based Injury Characterization Immediately Following Ablation of Atrial Fibrillation,” In Computing in Cardiology, Vol. 37, pp. 165--168. 2010.
ISSN: 0276−6574


C. Brownlee, V. Pegoraro, S. Shankar, P. McCormick, C.D. Hansen. “Physically-Based Interactive Schlieren Flow Visualization,” In Proceedings of IEEE Pacific Visualization 2010, Note: Won Best Paper Award!, 2010.


J.R. Bronson, J.A. Levine, R.T. Whitaker. “Particle Systems for Adaptive, Isotropic Meshing of CAD Models,” In Proceedings of the 19th International Meshing Roundtable, Note: Awarded Best Paper, Springer, pp. 279-296. 2010.
DOI: 10.1007/978-3-642-15414-0_17

ABSTRACT

We present a particle-based approach for generating adaptive triangular surface and tetrahedral volume meshes from CAD 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.


A. Chaturvedi, C.R. Butson, S.F. Lempka, S.E. Cooper, C.C. McIntyre. “Patient-specific models of deep brain stimulation: influence of field model complexity on neural activation predictions,” In Brain Stimulation, Vol. 3, No. 2, Elsevier Inc., pp. 65--67. April, 2010.
ISSN: 1935-861X
DOI: 10.1016/j.brs.2010.01.003
PubMed ID: 20607090

ABSTRACT

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Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has become the surgical therapy of choice for medically intractable Parkinson's disease. However, quantitative understanding of the interaction between the electric field generated by DBS and the underlying neural tissue is limited. Recently, computational models of varying levels of complexity have been used to study the neural response to DBS. The goal of this study was to evaluate the quantitative impact of incrementally incorporating increasing levels of complexity into computer models of STN DBS. Our analysis focused on the direct activation of experimentally measureable fiber pathways within the internal capsule (IC). Our model system was customized to an STN DBS patient and stimulation thresholds for activation of IC axons were calculated with electric field models that ranged from an electrostatic, homogenous, isotropic model to one that explicitly incorporated the voltage-drop and capacitance of the electrode-electrolyte interface, tissue encapsulation of the electrode, and diffusion-tensor based 3D tissue anisotropy and inhomogeneity. The model predictions were compared to experimental IC activation defined from electromyographic (EMG) recordings from eight different muscle groups in the contralateral arm and leg of the STN DBS patient. Coupled evaluation of the model and experimental data showed that the most realistic predictions of axonal thresholds were achieved with the most detailed model. Furthermore, the more simplistic neurostimulation models substantially overestimated the spatial extent of neural activation.

Keywords: Action Potentials, Action Potentials: physiology, Computer Simulation, Deep Brain Stimulation, Deep Brain Stimulation: instrumentation, Deep Brain Stimulation: methods, Humans, Male, Middle Aged, Models, Neurological, Parkinson Disease, Parkinson Disease: therapy, Subthalamic Nucleus, Subthalamic Nucleus: physiology


A.N.M. Imroz Choudhury, M.D. Steffen, J.E. Guilkey, S.G. Parker. “Enhanced Understanding of Particle Simulations Through Deformation-Based Visualization,” In Computer Modeling in Engineering & Sciences, Vol. 63, No. 2, pp. 117--136. 2010.


J. Cui, P. Rosen, V. Popescu, C. Hoffmann. “A Curved Ray Camera for Handling Occlusions through Continuous Multiperspective Visualization,” In IEEE Transactions on Visualization and Computer Graphics (Visualization 2010), pp. 1235--1242. November/December, 2010.


E.B. Dam, P.T. Fletcher, S.M. Pizer. “Automatic shape model building based on principal geodesic analysis bootstrapping,” In Medical Image Analysis, Vol. 12, No. 2, Note: Epub Feb 2 2010, pp. 136--151. 2010.
PubMed ID: 18178124


J. Daniels, E.W. Anderson, L.G. Nonato, C.T. Silva. “Interactive Vector Field Feature Identification,” In IEEE Transactions on Visualization and Computer Graphics, Proceedings of the 2010 IEEE Visualization Conference, Vol. 16, No. 6, pp. 1560--1568. 2010.
DOI: 10.1109/TVCG.2010.170
PubMed ID: 20975198


B.C. Davis, P.T. Fletcher, E. Bullitt, S. Joshi. “Population Shape Regression from Random Design Data,” In International Journal of Computer Vision, Vol. 90, No. 1, Note: Marr Prize Special Issue, pp. 255--266. October, 2010.
DOI: 10.1109/ICCV.2007.4408977


N.J. Drury, B.J. Ellis, J.A. Weiss, P.J. McMahon, R.E. Debski. “The impact of glenoid labrum thickness and modulus on labrum and glenohumeral capsule function,” In Journal of Biomechanical Engineering, Vol. 132, No. 12, Note: Awarded 2010 Skalak Best Paper!, pp. 121003--121010. 2010.
DOI: 10.1115/1.4002622


S. Durrleman, X. Pennec, A. Trouvé, N. Ayache, J. Braga. “Comparison of the endocast growth of chimpanzees and bonobos via temporal regression and spatiotemporal registration,” In Proceedings of the MICCAI Workshop on Spatio-Temporal Image Analysis for Longitudinal and Time-Series Image Data, Beijing, China, pp. (in press). September, 2010.


S. Durrleman, P. Fillard, X. Pennec, A. Trouvé, N. Ayache. “Registration, Atlas Estimation and Variability Analysis of White Matter Fiber Bundles Modeled as Currents,” In NeuroImage, Vol. 55, No. 3, pp. 1073--1090. 2010.
DOI: 10.1016/j.neuroimage.2010.11.056


M. El-Sayed, R.G. Steen, M.D. Poe, T.C. Bethea, G. Gerig, J. Lieberman, L. Sikich. “Deficits in gray matter volume in psychotic youth with schizophrenia-spectrum disorders are not evident in psychotic youth with mood disorders,” In J Psychiatry Neurosci, July, 2010.


M. El-Sayed, R.G. Steen, M.D. Poe, T.C. Bethea, G. Gerig, J. Lieberman, L. Sikich. “Brain volumes in psychotic youth with schizophrenia and mood disorders,” In Journal of Psychiatry and Neuroscience, Vol. 35, No. 4, pp. 229--236. July, 2010.
PubMed ID: 20569649


T. Etiene, L.G. Nonato, C.E. Scheidegger, J. Tierny, T.J. Peters, V. Pascucci, R.M. Kirby, C.T. Silva. “Topology Verification for Isosurface Extraction,” SCI Technical Report, No. UUSCI-2010-003, SCI Institute, University of Utah, 2010.


P.T. Fletcher, R.T. Whitaker, R. Tao, M.B. DuBray, A. Froehlich, C. Ravichandran, A.L. Alexander, E.D. Bigler, N. Lange, J.E. Lainhart. “Microstructural connectivity of the arcuate fasciculus in adolescents with high-functioning autism,” In NeuroImage, Vol. 51, No. 3, Note: Epub Feb 2 2010, pp. 1117--1125. July, 2010.
PubMed ID: 20132894


T. Fogal, J. Krüger. “Tuvok, an Architecture for Large Scale Volume Rendering,” In Proceedings of the 15th International Workshop on Vision, Modeling, and Visualization, pp. 139--146. November, 2010.
DOI: 10.2312/PE/VMV/VMV10/139-146