SCI Publications

2010


N. Sadeghi, M.W. Prastawa, J.H. Gilmore, W. Lin, G. Gerig. “Spatio-Temporal Analysis of Early Brain Development,” In Proceedings IEEE Asilomar Conference on Signals, Systems and Computers, pp. 777--781. 2010.
DOI: 10.1109/ACSSC.2010.5757670

ABSTRACT

Analysis of human brain development is a crucial step for improved understanding of neurodevelopmental disorders. We focus on normal brain development as is observed in the multimodal longitudinal MRI/DTI data of neonates to two years of age. We present a spatio-temporal analysis framework using Gompertz function as a population growth model with three different spatial localization strategies: voxel-based, data driven clustering and atlas driven regional analysis. Growth models from multimodal imaging channels collected at each voxel form feature vectors which are clustered using the Dirichlet Process Mixture Models (DPMM). Clustering thus combines growth information from different modalities to subdivide the image into voxel groups with similar properties. The processing generates spatial maps that highlight the dynamic progression of white matter development. These maps show progression of white matter maturation where primarily, central regions mature earlier compared to the periphery, but where more subtle regional differences in growth can be observed. Atlas based analysis allows a quantitative analysis of a specific anatomical region, whereas data driven clustering identifies regions of similar growth patterns. The combination of these two allows us to investigate growth patterns within an anatomical region. Specifically, analysis of anterior and posterior limb of internal capsule show that there are different growth trajectories within these anatomies, and that it may be useful to divide certain anatomies into subregions with distinctive growth patterns.


N. Sadeghi, M.W. Prastawa, J.H. Gilmore, W. Lin, G. Gerig. “Towards Analysis of Growth Trajectory through Multi-modal Longitudinal MR Imaging,” In SPIE Medical Imaging 2010: Image Processing, Vol. 7623, 76232U, Edited by Benoit M. Dawant and David R. Haynor, pp. (published online). March, 2010.
DOI: 10.1117/12.844526


A.A. Samsonov, J.V. Velikina, Y.K. Jung, E.G. Kholmovski, C.R. Johnson, W.F. Block. “POCS-enhanced correction of motion artifacts in parallel MRI,” In Magnetic Resonance in Medicine, Vol. 63, No. 4, pp. 1104--1110. May, 2010.


Allen R. Sanderson, Scott Kruger, and Stephane Either. “Visualization and Analysis Tools for Assisting in Evaluating Fusion Simulations,” In Proceedings of the Sherwood Fusion Theory Conference, 2010.


A.R. Sanderson, G. Chen, X. Tricoche, D. Pugmire, S. Kruger, J. Breslau. “Analysis of Recurrent Patterns in Toroidal Magnetic Fields,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 16, No. 6, IEEE, pp. 1431-1440. Nov, 2010.
DOI: 10.1109/tvcg.2010.133


J. Schmidt, M. Berzins. “Development of the Uintah Gateway for Fluid-Structure-Interaction Problems,” In Proceedings of the Teragrid 2010 Conference, ACM, 2010.
DOI: 10.1145/1838574.1838591


C.E. Scheidegger, T. Etiene, L.G. Nonato, C.T. Silva. “Edge Flows: Stratified Morse Theory for Simple, Correct Isosurface Extraction,” SCI Technical Report, No. UUSCI-2010-002, SCI Institute, University of Utah, 2010.


M. Schott, P. Grosset, T. Martin, V. Pegoraro, C.D. Hansen. “Depth of Field Effects for Interactive Direct Volume Rendering,” SCI Technical Report, No. UUSCI-2010-005, SCI Institute, University of Utah, 2010.


C. Schlimper, O. Nemitz, U. Dorenbeck, J. Scorzin, R.T. Whitaker, T. Tasdizen, M. Rumpf, K. Schaller. “Restoring three-dimensional magnetic resonance angiography images with mean curvature motion,” In Neurological Research, Vol. 32, No. 1, Note: Epub 2009 Nov 26, pp. 87--93. February, 2010.
DOI: 10.1179/016164110X12556180206077
PubMed ID: 19941735


N.M. Segerson, M. Daccarett, T.J. Badger, A. Shabaan, N. Akoum, E.N. Fish, S. Rao, N.S. Burgon, Y. Adjei-Poku, E. Kholmovski, S. Vijayakumar, E.V. DiBella, R.S. MacLeod, N.F. Marrouche. “Magnetic resonance imaging-confirmed ablative debulking of the left atrial posterior wall and septum for treatment of persistent atrial fibrillation: rationale and initial experience,” In Journal of Cardiovascular Electrophysiology, Vol. 21, No. 2, pp. 126--132. 2010.
PubMed ID: 19804549


M. Seyedhosseini, A.R.C. Paiva, T. Tasdizen. “Image Parsing with a Three-State Series Neural Network Classifier,” In Proc. IEEE Intl. Conference on Pattern Recognition, Istanbul, Turkey, pp. 4508--4511. 2010.
DOI: 10.1109/ICPR.2010.1095


C. Silva, E. Anderson, E. Santos, J. Freire. “Using VisTrails and Provenance for Teaching Scientific Visualization,” In EUROGRAPHICS 2010 Educator Program, Note: Awarded Best Paper!, 2010.


N.P. Singh, P.T. Fletcher, J.S. Preston, L. Ha, R. King, J.S. Marron, M. Wiener, S. Joshi. “Multivariate Statistical Analysis of Deformation Momenta Relating Anatomical Shape to Neuropsychological Measures,” In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010, Lecture Notes in Computer Science (LCNS), Vol. 6363/2010, pp. 529-537. 2010.
DOI: 10.1007/978-3-642-15711-0_66
PubMed ID: 20879441


M. Steffen, R.M. Kirby, M. Berzins. “Decoupling and Balancing of Space and Time Errors in the Material Point Method (MPM),” In International Journal for Numerical Methods in Engineering, Vol. 82, No. 10, pp. 1207--1243. 2010.


J.G. Stinstra, R.S. MacLeod, C.S. Henriquez. “Incorporating Histology into a 3D Microscopic Computer Model of Myocardium to Study Propagation at a Cellular Level,” In Annals of Biomedical Engineering (ABME), Vol. 38, No. 4, pp. 3399--1414. 2010.
DOI: 10.1007/s10439-009-9883-y


D. Swenson, J.A. Levine, Z. Fu, J.D. Tate, R.S. MacLeod. “The Effect of Non-Conformal Finite Element Boundaries on Electrical Monodomain and Bidomain Simulations,” In Computing in Cardiology, Vol. 37, IEEE, pp. 97--100. 2010.
ISSN: 0276-6547


T. Tasdizen, P. Koshevoy, B.C. Grimm, J.R. Anderson, B.W. Jones, C.B. Watt, R.T. Whitaker, R.E. Marc. “Automatic mosaicking and volume assembly for high-throughput serial-section transmission electron microscopy,” In Journal of Neuroscience Methods, Vol. 193, No. 1, pp. 132--144. 2010.
PubMed ID: 20713087


J.D. Tate, J.G. Stinstra, T.A. Pilcher, R.S. MacLeod. “Implantable Cardioverter Defibrillator Predictive Simulation Validation,” In Computing in Cardiology, pp. 853-–856. September, 2010.

ABSTRACT

Despite the growing use of implantable cardioverter defibrillators (ICDs) in adults and children, there has been little progress in optimizing device and electrode placement. To facilitate effective placement of ICDs, especially in unique cases of children with congenital heart defects, we have developed a predictive model that evaluates the efficacy of a delivered shock. Most recently, we have also developed and carried out an experimental validation approach based on measurements from clinical cases. We have developed a method to obtain body surface potential maps of ICD discharges during implantation surgery and compared these measured potentials with simulated surface potentials to determine simulation accuracy.

Each study began with an full torso MRI or CT scan of the subject, from which we created patient specific geometric models. Using a customized limited leadset applied to the anterior surface of the torso away from the sterile field, we recorded body surface potentials during ICD testing. Subsequent X-ray images documented the actual location of ICD and electrodes for placement of the device in the geometric model. We then computed the defibrillation field, including body surface potentials, and compared them to the measured values.

Comparison of the simulated and measured potentials yielded very similar patterns and a typical correlation between 0.8 and 0.9 and a percentage error between 0.2 and 0.35. The high correlation of the potential maps suggest that the predictive simulation generates realistic potential values. Ongoing sensi- tivity studies will determine the robustness of the results and pave the way for use of this approach for predictive computational optimization studies before device implantation.


R. Tchoua, S. Klasky, N. Podhorszki, B. Grimm, A. Khan, E. Santos, C.T. Silva, P. Mouallem, M. Vouk. “Collaborative Monitoring and Analysis for Simulation Scientists,” In Proceedings of The 2010 International Symposium on Collaborative Technologies and Systems (CTS 2010), pp. 235--244. 2010.
DOI: 10.1109/CTS.2010.5478506

ABSTRACT

Collaboratively monitoring and analyzing large scale simulations from petascale computers is an important area of research and development within the scientific community. This paper addresses these issues when teams of colleagues from different research areas work together to help understand the complex data generated from these simulations. In particular, we address the issues when geographically diverse teams of disparate researchers work together to understand the complex science being simulated on high performance computers. Most application scientists want to focus on the sciences and spend a minimum amount of time learning new tools or adopting new techniques to monitor and analyze their simulation data. The challenge of eSimMon, our web-based system is to decrease or eliminate some of the hurdles on the scientists' path to scientific discovery, and allow these collaborations to flourish.

Keywords: collaboration, simulation


J. Tierny, J. Daniels II, L.G. Nonato, V. Pascucci, C.T. Silva. “Interactive Quadrangulation with Reeb Atlases and Connectivity Textures,” SCI Technical Report, No. UUSCI-2010-006, SCI Institute, University of Utah, 2010.