The NIH/NIGMS
Center for Integrative Biomedical Computing

SCI Publications

2008


J. Cates, P.T. Fletcher, M. Styner, H. Hazlett, R.T. Whitaker. “Particle-Based Shape Analysis of Multi-Object Complexes,” In Proceedings of the 11th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI '08), Lecture Notes In Computer Science (LCNS), pp. 477--485. 2008.
ISBN: 978-3-540-85987-1



S.E. Geneser, R.M. Kirby, R.S. MacLeod. “Application of Stochastic Finite Element Methods to Study the Sensitivity of ECG Forward Modeling to Organ Conductivity,” In IEEE Transations on Biomedical Engineering, Vol. 55, No. 1, pp. 31--40. January, 2008.



C.R. Johnson, X. Tricoche. “Biomedical Visualization,” In Advances in Biomedical Engineering, Ch. 6, Edited by Pascal Verdonck, Elsvier Science, pp. 209--272. 2008.



M. Jolley, J.G. Stinstra, S. Pieper, R.S. MacLeod, D.H. Brooks, F. Cecchin, J.K. Triedman. “A Computer Modeling Tool for Comparing Novel ICD Electrode Orientations in Children and Adults,” In Heart Rhythm, Vol. 5, No. 4, pp. 565--572. April, 2008.
PubMed ID: 18362024



J. Krüger, K. Potter, R.S. MacLeod, C.R. Johnson. “Unified Volume Format: A General System For Efficient Handling Of Large Volumetric Datasets,” In Proceedings of IADIS Computer Graphics and Visualization 2008 (CGV 2008), pp. 19--26. 2008.
PubMed ID: 20953270

ABSTRACT

With the continual increase in computing power, volumetric datasets with sizes ranging from only a few megabytes to petascale are generated thousands of times per day. Such data may come from an ordinary source such as simple everyday medical imaging procedures, while larger datasets may be generated from cluster-based scientific simulations or measurements of large scale experiments. In computer science an incredible amount of work worldwide is put into the efficient visualization of these datasets. As researchers in the field of scientific visualization, we often have to face the task of handling very large data from various sources. This data usually comes in many different data formats. In medical imaging, the DICOM standard is well established, however, most research labs use their own data formats to store and process data. To simplify the task of reading the many different formats used with all of the different visualization programs, we present a system for the efficient handling of many types of large scientific datasets (see Figure 1 for just a few examples). While primarily targeted at structured volumetric data, UVF can store just about any type of structured and unstructured data. The system is composed of a file format specification with a reference implementation of a reader. It is not only a common, easy to implement format but also allows for efficient rendering of most datasets without the need to convert the data in memory.



C. Ledergerber, G. Guennebaud, M.D. Meyer, M. Bacher, H. Pfister. “Volume MLS Ray Casting,” In IEEE Transactions on Visualization and Computer Graphics (Proceedings of Visualization 2008), Vol. 14, No. 6, pp. 1539--1546. 2008.

ABSTRACT

The method of Moving Least Squares (MLS) is a popular framework for reconstructing continuous functions from scattered data due to its rich mathematical properties and well-understood theoretical foundations. This paper applies MLS to volume rendering, providing a unified mathematical framework for ray casting of scalar data stored over regular as well as irregular grids. We use the MLS reconstruction to render smooth isosurfaces and to compute accurate derivatives for high-quality shading effects. We also present a novel, adaptive preintegration scheme to improve the efficiency of the ray casting algorithm by reducing the overall number of function evaluations, and an efficient implementation of our framework exploiting modern graphics hardware. The resulting system enables high-quality volume integration and shaded isosurface rendering for regular and irregular volume data.



S. Lew, C.H. Wolters, A. Anwander, S. Makeig, R.S. Macleod. “Improved EEG Source Analysis Using Low-Resolution Conductivity Estimation in a Four-Compartment Finite Element Head Model,” In Human Brain Mapping, Vol. 31, December, 2008.



G.-S. Li, X. Tricoche, D. Weiskopf, C.D. Hansen. “Flow Charts: Visualization of Vector Fields on Arbitrary Surfaces,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 14, No. 5, pp. 1--14. September/October, 2008.



Y. Livnat, S.G. Parker, C.R. Johnson. “Fast Isosurface Extraction Methods for Large Image Data Sets,” In Handbook of Medical Image Processing and Analysis, 2nd edition, Ch. 47, Note: (to appear), Edited by Isaac N. Bankman, Elsevier, pp. 801--816. 2008.



C.J. McGann, E.G. Kholmovski, R.S. Oakes, J.J. Blauer, M. Daccarett, N. Segerson, K.J. Airey, N. Akoum, E. Fish, T.J. Badger, E.V. DiBella, D.L. Parker, R.S. MacLeod, N.F. Marrouche. “New Magnetic Resonance Imaging-Based Method for Defining the Extent of Left Atrial Wall Injury After the Ablation of Atrial Fibrillation,” In Journal of the American College of Cardiology, Vol. 52, No. 15, pp. 1263--1271. Oct 7, 2008.



M.D. Meyer, R.T. Whitaker, R.M. Kirby, C. Ledergerber, H. Pfister. “Particle-based Sampling and Meshing of Surfaces in Multimaterial Volumes,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 14, No. 6, pp. 1539--1546. 2008.



I. Oguz, J. Cates, P.T. Fletcher, R.T. Whitaker, D. Cool, S. Aylward, M. Styner. “Cortical Correspondence using Entropy-Based Particle Systems and Local Features,” In 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008. (ISBI 2008), pp. 1637--1640. 2008.



S. Pizer, M. Styner, T. Terriberry, R. Broadhurst, S. Joshi, E. Chaney, P.T. Fletcher. “Statistical Applications with Deformable M-Reps,” In Computational Imaging and Vision, Springer, pp. 269--308. 2008.
DOI: 10.1007/978-1-4020-8658-8_9



K. Potter, J. Krüger, C.R. Johnson. “Towards the Visualization of Multi-Dimentional Stochastic Distribution Data,” In Proceedings of The International Conference on Computer Graphics and Visualization (IADIS) 2008, pp. 191--196. 2008.



J.F. Shepherd, C.R. Johnson. “Hexahedral Mesh Generation Constraints,” In Journal of Engineering with Computers, Vol. 24, No. 3, pp. 195--213. 2008.



A.G. Shvedko, M.D. Warren, S. Shome, J.G. Stinstra, A.V. Zaitsev. “Influence of the Skeletal Muscle Activity on Time and Frequency Domain Properties of the Body Surface ECG During Evolving Ventricular Fibrillation in the Pig,” In Resuscitation, Vol. 78, No. 2, pp. 215--223. May 24, 2008.
DOI: 10.1016/j.resuscitation.2008.03.010



J.G. Stinstra, M.A. Jolley, J.D. Tate, D.H. Brooks, J.K. Triedman, R.S. MacLeod. “The Role of Volume Conductivities in Simulation of Implantable Defibrillators,” In Computers in Cardiology, Bologna, Italy, 2008.



J.G. Stinstra, M. Jolley, M. Callahan, D.M. Weinstein, M. Cole, D.H. Brooks, J. Triedman, R.S. MacLeod. “Evaluation of Different Meshing Algorithms in the Computation of Defibrillation Thresholds in Children,” In Proceedings of the 29th Annual International Conference of the IEEE EMBS, pp. 1422-1425. 2008.
ISBN: 978-1-4244-0787-3



D.R. Sutherland, Q. Ni, R.S. MacLeod, R.L. Lux, B.B. Punske. “Experimental Measures of Ventricular Activation and Synchrony,” In Pacing and Clinical Electrophysiology, Vol. 31, No. 12, pp. 1560--1570. December, 2008.



B. Taccardi, B.B. Punske, E. Macchi, R.S. MacLeod, P.R. Ershler. “Epicardial and Intramural Excitation During Ventricular Pacing: Effect of Myocardial Structure,” In The American Journal of Physiology - Heart and Circulatory Physiology, Vol. 294, No. 4, pp. H1753--1766. April, 2008.
DOI: 10.1152/ajpheart.01400.2007