SCIENTIFIC COMPUTING AND IMAGING INSTITUTE
at the University of Utah

An internationally recognized leader in visualization, scientific computing, and image analysis

SCI Publications

2010


A.R.C. Paiva, T. Tasdizen. “Fast Semi-Supervised Image Segmentation by Novelty Selection,” In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Dallas, Texas, pp. 1054--1057. March, 2010.
DOI: 10.1109/ICASSP.2010.5495333


A.R.C. Paiva, I. Park, J.C. Principe. “Inner products for representation and learning in the spike train domain,” In Statistical Signal Processing for Neuroscience and Neurotechnology, Ch. 8, Edited by Karim G. Oweiss, Elsevier, pp. 265--309. 2010.
DOI: 10.1016/b978-0-12-375027-3.00008-9


A.R.C. Paiva, I. Park, J.C. Principe. “Optimization in Reproducing Kernel Hilbert Spaces of Spike Trains,” In Computational Neuroscience, Edited by W. Chaovalitwongse et al., Springer, pp. 3--29. 2010.
ISBN: 978-0-387-88629-9
DOI: 10.1007/978-0-387-88630-5_1


A.R.C. Paiva, I. Park, J.C. Principe. “A comparison of binless spike train measures,” In Neural Computing and Applications, Vol. 19, No. 3, pp. 405--419. 2010.


A.R.C. Paiva, T. Tasdizen. “Detection of Salient Image Points using Manifold Structure,” In Proc. IEEE Intl. Conference on Pattern Recognition, Istanbul, Turkey, pp. 1389--1392. 2010.
DOI: 10.1109/ICPR.2010.343


A.R.C. Paiva, E. Jurrus, T. Tasdizen. “Using Sequential Context for Image Analysis,” In Proc. IEEE Intl. Conference on Pattern Recognition, Istanbul, Turkey, pp. 2800--2803. 2010.
DOI: 10.1109/ICPR.2010.686


Y. Pan, R.T. Whitaker, A. Cheryauka, D. Ferguson. “Regularized 3D Iterative Reconstruction on a Mobile C-ARM CT,” In Proceedings of The First CT Meeting, Salt Lake City, UT, pp. (accepted). 2010.

ABSTRACT

3D iterative CT reconstruction is an active research area in medical imaging. Compared with analytic reconstruction methods such as FDK, iterative methods may provide better reconstruction results for incomplete and noisy projection data. The simultaneous algebraic reconstruction technique (SART), one of the most popular iterative reconstruction methods, is applied in the cone-beam geometry for highresolution reconstruction, with the help of graphics hardware (GPU) and total variation (TV) regularization. GPU greatly improves the efficiency of SART, which is computationally intense for CPU, and thus makes it suitable for clinical applications. TV regularization reduces the effects of noise and helps the convergence of SART for noisy data. Experimental results for both synthetic and real data are provided to evaluate the accuracy and efficiency of the proposed framework.

Keywords: Cone-beam CT, iterative reconstruction, SART, GPU, TV regularization


Y. Pan, R.T. Whitaker, A. Cheryauka, D. Ferguson. “TV-regularized Iterative Image Reconstruction on a Mobile C-ARM CT,” In Proceedings of SPIE Medical Imaging 2010, San Diego, CA, Vol. 7622, pp. (published online). 2010.
DOI: 10.1117/12.844398


F.V. Paulovich, C.T. Silva, L.G. Nonato. “Two-Phase Mapping for Projecting Massive Data Sets,” In IEEE Transactions on Visualization and Computer Graphics (TVCG), In IEEE Tr, Vol. 16, No. 6, pp. 1281--1290. Nov, 2010.
DOI: 10.1109/TVCG.2010.207

ABSTRACT

Most multidimensional projection techniques rely on distance (dissimilarity) information between data instances to embed high-dimensional data into a visual space. When data are endowed with Cartesian coordinates, an extra computational effort is necessary to compute the needed distances, making multidimensional projection prohibitive in applications dealing with interactivity and massive data. The novel multidimensional projection technique proposed in this work, called Part-Linear Multidimensional Projection (PLMP), has been tailored to handle multivariate data represented in Cartesian high-dimensional spaces, requiring only distance information between pairs of representative samples. This characteristic renders PLMP faster than previous methods when processing large data sets while still being competitive in terms of precision. Moreover, knowing the range of variation for data instances in the high-dimensional space, we can make PLMP a truly streaming data projection technique, a trait absent in previous methods.


V. Pegoraro, M. Schott, S.G. Parker. “A Closed-Form Solution to Single Scattering for General Phase Functions and Light Distributions,” In Computer Graphics Forum, Vol. 29, No. 4, Wiley-Blackwell, pp. 1365--1374. Aug, 2010.
DOI: 10.1111/j.1467-8659.2010.01732.x


T.A. Pilcher, J.D. Tate, J.G. Stinstra, E.V. Saarel, M.D. Puchalski, and R.S. MacLeod. “Partially extracted defibrillator coils and pacing leads alter defibrillation thresholds,” In Proceedings of the 15th International Academy of Cardiology World Congress of Cardiology, 2010.


K. Potter, J.M. Kniss, R. Riesenfeld, C.R. Johnson. “Visualizing Summary Statistics and Uncertainty,” In Computer Graphics Forum, Vol. 29, No. 3, Wiley-Blackwell, pp. 823--831. Aug, 2010.


M.W. Prastawa, N. Sadeghi, J.H. Gilmore, W. Lin, G. Gerig. “A new framework for analyzing white matter maturation in early brain development,” In Proceedings of the 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 97--100. April, 2010.
ISBN: 978-1-4244-4125-9
DOI: 10.1109/ISBI.2010.5490404


S.P. Reese, S.A. Maas, J.A. Weiss. “Micromechanical models of helical superstructures in ligament and tendon fibers predict large poisson's ratios,” In Journal of Biomechanics, Vol. 43, No. 7, pp. 1394--1400. 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