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Analysis of Time-Dependent Flow-Sensitive PC-MRI Data
June 2012 (vol. 18 no. 6)
pp. 966-977
J. Guhring, Corp. Res., Imaging & Visualization, Siemens Corp., Princeton, NJ, USA
C. Garth, Inst. of Data Anal. & Visualization, Univ. of California, Davis, Davis, CA, USA
H. Krishnan, Inst. of Data Anal. & Visualization, Univ. of California, Davis, Davis, CA, USA
A. Greiser, Healthcare Sector, Siemens AG, Erlangen, Germany
M. A. Gulsun, Corp. Res., Imaging & Visualization, Siemens Corp., Princeton, NJ, USA
K. I. Joy, Inst. of Data Anal. & Visualization, Univ. of California, Davis, Davis, CA, USA
Many flow visualization techniques, especially integration-based methods, are problematic when the measured data exhibit noise and discretization issues. Particularly, this is the case for flow-sensitive phase-contrast magnetic resonance imaging (PC-MRI) data sets which not only record anatomic information, but also time-varying flow information. We propose a novel approach for the visualization of such data sets using integration-based methods. Our ideas are based upon finite-time Lyapunov exponents (FTLE) and enable identification of vessel boundaries in the data as high regions of separation. This allows us to correctly restrict integration-based visualization to blood vessels. We validate our technique by comparing our approach to existing anatomy-based methods as well as addressing the benefits and limitations of using FTLE to restrict flow. We also discuss the importance of parameters, i.e., advection length and data resolution, in establishing a well-defined vessel boundary. We extract appropriate flow lines and surfaces that enable the visualization of blood flow within the vessels. We further enhance the visualization by analyzing flow behavior in the seeded region and generating simplified depictions.

[1] M. Markl, A. Harloff, T.A. Bley, Z. M, J. B, W. E, M. Langer, J. Hennig, and A. Frydrychowicz, “Time-Resolved 3D MR Velocity Mapping at 3t: Improved Navigator-Gated Assessment of Vascular Anatomy and Blood Flow,” J. Magnetic Resonance Imaging, vol. 25, no. 4, pp. 824-831, 2007.
[2] A. Greiser, M. Gülsün, A. Littmann, J. Guehring, and E. Mueller, “Volumetric Phase Contrast Flow Imaging with Multiple Station Isocenter Acquisition Substantially Improves Flow Results,” Proc. Ann. Meeting of ISMRM, 2010.
[3] A. Stalder, M. Russe, J. Bock, J. Hennig, and M. Markl, “Quantitative 2D and 3D Phase Contrast MRI: Optimized Analysis of Blood Flow and Vessel Wall Parameters,” Soc. for Magnetic Resonance in Medicine, vol. 60, no. 5, pp. 1218-1231, 2008.
[4] A. Frydrychowicz, A. Berger, M.F. Russe, A. Stalder, A. Harloff, S. Dittrich, J. Hennig, M. Langer, and M. Markl, “Time-Resolved Magnetic Resonance Angiography and Flow-Sensitive 4-Dimensional Magnetic Resonance Imaging at 3 Tesla for Blood Flow and Wall Shear Stress Analysis,” J. Thoracic and Cardiovascular Surgery, vol. 136, pp. 400-407, 2008.
[5] R. van Pelt, J.O. Bescós, M. Breeuwer, R.E. Clough, M.E. Gröller, B.M. ter Haar Romenij, and A. Vilanova, “Exploration of 4D MRI Blood Flow Using Stylistic Visualization,” IEEE Trans. Visualization and Computer Graphics, vol. 16, no. 6, pp. 1339-1347, Nov./Dec. 2010.
[6] P. Richardson, I. Pivkin, G. Karniadakis, and D.H. Laidlaw, “Blood Flow at Arterial Branches: Complexities to Resolve for the Angioplasty Suite,” Proc. Int'l Conf. Computational Science, pp. 538-545, May 2006.
[7] Y. Chen, J. Cohen, and J. Krolik, “Similarity-Guided Streamline Placement with Error Evaluation,” IEEE Trans. Visualization and Computer Graphics, vol. 13, no. 6, pp. 1448-1455, Nov./Dec. 2007.
[8] F. Ferstl, K. Burger, H. Theisel, and R. Westermann, “Interactive Separating Streak Surfaces,” IEEE Trans. Visualization and Computer Graphics, vol. 16, no. 6, pp. 1569-1577, Nov./Dec. 2010.
[9] S. Shadden, F. Lekien, and J. Marsden, “Definition and Properties of Lagrangian Coherent Structures from Finite-Time Lyapunov Exponents in Two-Dimensional Aperiodic Flows,” Physica D: Nonlinear Phenomena, vol. 212, pp. 271-304, 2005.
[10] C. Garth, G.S. Li, X. Tricoche, C.D. Hansen, and H. Hagen, “Interactive Visualization of Coherent Structures in Transient Flows,” Proc. Topology-Based Methods in Visualization, 2007.
[11] F. Sadlo and R. Peikert, “Visualizing Lagrangian Coherent Structures and Comparison to Vector Field Topology,” Proc. Topology-Based Methods in Visualization, 2007.
[12] C. Garth, F. Gerhardt, X. Tricoche, and H. Hagen, “Efficient Computation and Visualization of Coherent Structures in Fluid Flow Applications,” IEEE Trans. Visualization and Computer Graphics, vol. 13, no. 6, pp. 1464-1471, Nov./Dec. 2007.
[13] C. Garth, H. Krishnan, X. Tricoche, T. Tricoche, and K.I. Joy, “Generation of Accurate Integral Surfaces in Time-Dependent Vector Fields,” IEEE Trans. Visualization and Computer Graphics, vol. 14, no. 6, pp. 1404-1411, Nov./Dec. 2008.
[14] H. Krishnan, C. Garth, and K. Joy, “Time and Streak Surfaces for Flow Visualization in Large Time-Varying Data Sets,” IEEE Trans. Visualization and Computer Graphics, vol. 15, no. 6, pp. 1267-1274, Nov./Dec. 2009.
[15] K. Buerger, F. Ferstl, H. Theisel, and R. Westermann, “Interactive Streak Surface Visualization on the GPU,” IEEE Trans. Visualization and Computer Graphics, vol. 15, no. 6, pp. 1259-1266, Nov./Dec. 2009.
[16] W. von Funck, T. Weinkauf, H. Theisel, and H.-P. Seidel, “Smoke Surfaces: An Interactive Flow Visualization Technique Inspired by Real-world Flow Experiments,” IEEE Trans. Visualization and Computer Graphics, vol. 14, no. 6, pp. 1396-1403, Nov./Dec. 2008.
[17] A.C.S. Chung, “Image Segmentation Methods for Detecting Blood Vessels in Angiography,” Proc. Int'l Conf. Control, Automation, Robotics, and Vision (ICARCV), pp. 1-6, 2006.
[18] L.G. Brown, “A Survey of Image Registration Techniques,” ACM Computing Survey, vol. 24, no. 4, pp. 325-376, 1992.
[19] T. McInerney and D. Terzopoulos, “Deformable Models in Medical Image Analysis: A Survey,” Medical Image Analysis, vol. 1, no. 2, pp. 91-108, 1996.
[20] M. Gülsün and H. Tek, “Robust Vessel Tree Modeling,” Proc. Int'l Conf. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2008.
[21] M. Gülsün and H. Tek, “Segmentation of Carotid Arteries by Graph-Cuts Using Centerline Models,” Proc. SPIE, vol. 7625, 2010.
[22] G. Haller, “Lagrangian Structures and the Rate of Strain in a Partition of Two-dimensional Turbulence,” Physics of Fluids, vol. 13, no. 11, pp. 3365-3385, 2001.
[23] G. Haller, “Lagrangian Coherent Structures from Approximate Velocity Data,” Physics of Fluids, vol. 14, no. 6, pp. 1851-1861, June 2002.
[24] S. Shadden, J. Dabiri, and J. Marsden, “Lagrangian Analysis of Fluid Transport in Empirical Vortex Ring Flows,” Physics of Fluids, vol. 18, pp. 047105-1-047105-11, 2006.
[25] E. Hairer, S.P. Nørsett, and G. Wanner, Solving Ordinary Differential Equations I, second ed., vol. 8. Springer-Verlag, 1993.
[26] P.J. Prince and J.R. Dormand, “High Order Embedded Runge-Kutta Formulae,” J. Computational and Applied Math., vol. 7, no. 1, pp. 67-75, 1981.
[27] D. Stalling, “Fast Texture-Based Algorithms for Vector Field Visualization,” PhD dissertation, Freie Universität Berlin, 1998.
[28] B. Soni, D. Thompson, and R. Machiraju, “Visualizing Particle/Flow Structure Interactions in the Small Bronchial Tubes,” IEEE Trans. Visualization and Computer Graphics, vol. 14, no. 6, pp. 1412-1427, Nov./Dec. 2008.
[29] A. Frydrychowicz, M. Markl, A. Harloff, A.F. Stalder, J. Bock, T.A. Bley, A. Berger, M.F. Russe, C. Schlensak, J. Hennig, and M. Langer, “Flow-Sensitive in-Vivo 4D MR Imaging at 3T for the Analysis of Aortic Hemodynamics and Derived Vessel Wall Parameters,” Rofo, vol. 179, no. 5, pp. 463-472, 2007.

Index Terms:
medical computing,biomedical MRI,blood vessels,data visualisation,flow visualisation,haemodynamics,integration,integration-based visualization methods,blood flow visualization techniques,flow-sensitive phase-contrast magnetic resonance imaging data sets,anatomic information,time-varying flow information,finite-time Lyapunov exponents,blood vessel boundary identification,advection length,data resolution,flow line extraction,surface extraction,MRI,Data visualization,Magnetic resonance imaging,Blood,Biomedical imaging,Trajectory,Visualization,medical visualization.,Flow analysis,time-varying and time-series visualization,surface extraction,flow-sensitive MRI
Citation:
J. Guhring, C. Garth, H. Krishnan, A. Greiser, M. A. Gulsun, K. I. Joy, "Analysis of Time-Dependent Flow-Sensitive PC-MRI Data," IEEE Transactions on Visualization and Computer Graphics, vol. 18, no. 6, pp. 966-977, June 2012, doi:10.1109/TVCG.2011.80
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