Research Papers

Immersive Visualization for Enhanced Computational Fluid Dynamics Analysis

[+] Author and Article Information
David J. Quam

Department of Biomedical Engineering,
Marquette University,
Milwaukee, WI 53233
e-mail: dj.quam@gmail.com

Timothy J. Gundert

Department of Biomedical Engineering,
Marquette University,
Milwaukee, WI 53233
e-mail: timothy.gundert@gmail.com

Laura Ellwein

Department of Biomedical Engineering,
Marquette University,
Milwaukee, WI 53233
e-mail: laura.ellwein@gmail.com

Christopher E. Larkee

MARquette Visualization Lab (MARVL),
College of Engineering,
Marquette University,
Milwaukee, WI 53233
e-mail: christopher.larkee@marquette.edu

Paul Hayden

Discovery World at Pier Wisconsin,
Milwaukee, WI 53202
e-mail: haydenp@discoveryworld.org

Raymond Q. Migrino

Phoenix VA Health Care System,
Phoenix, AZ 85012
Division of Cardiovascular Medicine,
Medical College of Wisconsin,
Milwaukee, WI 53226
e-mail: Raymond.migrino@va.gov

Hiromasa Otake

Kobe University Graduate School of Medicine,
Kobe, Japan
e-mail: hiro_yuki15@yahoo.co.jp

John F., LaDisa, Jr.

Department of Biomedical Engineering,
Marquette University,
Milwaukee, WI 53233
MARquette Visualization Lab (MARVL),
Marquette University,
Milwaukee, WI 53233
Laboratory for Translational,
Experimental and Computational Cardiovascular Research,
Marquette University
Medical College of Wisconsin,
Milwaukee, WI 53233
Division of Cardiovascular Medicine,
Medical College of Wisconsin,
Milwaukee, WI 53233

1Present address: Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, VA 23284.

2Corresponding author.

Manuscript received November 22, 2013; final manuscript received August 5, 2014; published online January 29, 2015. Assoc. Editor: Ender A. Finol.

This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Approved for public release; distribution is unlimited.

J Biomech Eng 137(3), 031004 (Mar 01, 2015) (12 pages) Paper No: BIO-13-1551; doi: 10.1115/1.4029017 History: Received November 22, 2013; Revised August 05, 2014; Online January 29, 2015

Modern biomedical computer simulations produce spatiotemporal results that are often viewed at a single point in time on standard 2D displays. An immersive visualization environment (IVE) with 3D stereoscopic capability can mitigate some shortcomings of 2D displays via improved depth cues and active movement to further appreciate the spatial localization of imaging data with temporal computational fluid dynamics (CFD) results. We present a semi-automatic workflow for the import, processing, rendering, and stereoscopic visualization of high resolution, patient-specific imaging data, and CFD results in an IVE. Versatility of the workflow is highlighted with current clinical sequelae known to be influenced by adverse hemodynamics to illustrate potential clinical utility.

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Hansson, G. K., 2005, “Inflammation, Atherosclerosis, and Coronary Artery Disease,” N. Engl. J. Med., 352(16), pp. 1685–1695. [CrossRef] [PubMed]
Ku, D. N., Giddens, D. P., Zarins, C. K., and Glagov, S., 1985, “Pulsatile Flow and Atherosclerosis in the Human Carotid Bifurcation. Positive Correlation Between Plaque Location and Low Oscillating Shear Stress,” Arteriosclerosis, 5(3), pp. 293–302. [CrossRef] [PubMed]
Malek, A. M., Alper, S. L., and Izumo, S., 1999, “Hemodynamic Shear Stress and Its Role in Atherosclerosis,” JAMA, 282(21), pp. 2035–2042. [CrossRef] [PubMed]
Moore, J. E., Jr., Xu, C., Glagov, S., Zarins, C. K., and Ku, D. N., 1994, “Fluid Wall Shear Stress Measurements in a Model of the Human Abdominal Aorta: Oscillatory Behavior and Relationship to Atherosclerosis,” Atherosclerosis, 110(2), pp. 225–240. [CrossRef] [PubMed]
Schroeder, W., Martin, K., Lorensen, B., Avila, L. S., Avila, R., and Law, C. C., 2006, The Visualization Toolkit, Kitware, Inc, Clifton Park, NY. [CrossRef]
Chaudhry, A., Sutton, C., Wood, J., Stone, R., and McCloy, R., 1999, “Learning Rate for Laparoscopic Surgical Skills on Mist Vr, a Virtual Reality Simulator: Quality of Human-Computer Interface,” Ann. R. Coll. Surg. Engl., 81(4), pp. 281–286. [PubMed]
Jordan, J. A., Gallagher, A. G., McGuigan, J., and McClure, N., 2001, “Virtual Reality Training Leads to Faster Adaptation to the Novel Psychomotor Restrictions Encountered by Laparoscopic Surgeons,” Surg. Endosc., 15(10), pp. 1080–1084. [CrossRef] [PubMed]
Steinman, D. A. H., and Steinman, D. A., 2007, “The Art and Science of Visualizing Simulated Blood-Flow Dynamics,” Leonardo, 40(1), pp. 71–76. [CrossRef]
Drascic, D., Milgram, P., and Grodski, J., 1989, “Learning Effects in Telemanipulation with Monoscopic Versus Stereoscopic Remote Viewing,” IEEE International Conference on Systems, Man and Cybernetics, Cambridge, MA, Nov. 14–17, pp. 1244–1249. [CrossRef]
Ward, J. W., Phillips, R., Williams, T., Shang, C., Page, L., Prest, C., and Beavis, A. W., 2007, Immersive Visualization With Automated Collision Detection for Radiotherapy Treatment Planning, Studies in Health Technology and Informatics, IOS, Hull, UK.
Chu, J., Gong, X., Cai, Y., Kirk, M. C., Zusag, T. W., Shott, S., Rivard, M. J., Melhus, C. S., Cardarelli, G., Hurley, A., Hepel, J. T., Napoli, J., Stutsman, S., and Abrams, R. A., 2009, “Application of Holographic Display in Radiotherapy Treatment Planning Ii: A Multi-Institutional Study,” J. Appl. Clin. Med. Phys., 10(3), pp. 115–124. [CrossRef]
Boejen, A., and Grau, C., 2011, “Virtual Reality in Radiation Therapy Training,” Surg. Oncol., 20(3), pp. 185–188. [CrossRef] [PubMed]
Votanopoulos, K., Brunicardi, F. C., Thornby, J., and Bellows, C. F., 2008, “Impact of Three-Dimensional Vision in Laparoscopic Training,” World J. Surg., 32(1), pp. 110–118. [CrossRef] [PubMed]
Forsberg, A., Laidlaw, D. H., Van Dam, A., Kirby, R. M., Kafniadakis, G. E., and Elion, J. L., 2000, “Immersive Virtual Reality for Visualizing Flow Through an Artery,” Proceedings of Visualization, Salt Lake City, UT, Oct. 13, pp. 457–460. [CrossRef]
LaDisa, J. F., Jr., Bowers, M., Harmann, L., Prost, R., Doppalapudi, A. V., Mohyuddin, T., Zaidat, O., and Migrino, R. Q., 2010, “Time-Efficient Patient-Specific Quantification of Regional Carotid Artery Fluid Dynamics and Spatial Correlation With Plaque Burden,” Med. Phys., 37(2), pp. 784–792. [CrossRef] [PubMed]
Justice, J., Bergerud, M., Garrison, J., Cafiero, D., and Churches, L., 2009, eon studio7, EON Reality, Irvine, CA.
Murata, A., Wallace-Bradley, D., Tellez, A., Alviar, C., Aboodi, M., Sheehy, A., Coleman, L., Perkins, L., Nakazawa, G., Mintz, G., Kaluza, G. L., Virmani, R., and Granada, J. F., 2010, “Accuracy of Optical Coherence Tomography in the Evaluation of Neointimal Coverage after Stent Implantation,” JACC: Cardiovasc. Imaging, 3(1), pp. 76–84. [CrossRef] [PubMed]
Ellwein, L. M., Otake, H., Gundert, T. J., Koo, B. K., Shinke, T., Honda, Y., Shite, J., and LaDisa, J. F., Jr., 2011, “Optical Coherence Tomography for Patient-Specific 3D Artery Reconstruction and Evaluation of Wall Shear Stress in a Left Circumflex Coronary Artery,” Cardiovasc. Eng. Technol., 2(3), pp. 212–217. [CrossRef]
Yarnykh, V. L., Terashima, M., Hayes, C. E., Shimakawa, A., Takaya, N., Nguyen, P. K., Brittain, J. H., McConnell, M. V., and Yuan, C., 2006, “Multicontrast Black-Blood MRI of Carotid Arteries: Comparison Between 1.5 and 3 Tesla Magnetic Field Strengths,” J. Magn. Reson. Imaging, 23(5), pp. 691–698. [CrossRef] [PubMed]
Kerwin, W. S., Liu, F., Yarnykh, V., Underhill, H., Oikawa, M., Yu, W., Hatsukami, T. S., and Yuan, C., 2008, “Signal Features of the Atherosclerotic Plaque at 3.0 Tesla Versus 1.5 Tesla: Impact on Automatic Classification,” J. Magn. Reson. Imaging, 28(4), pp. 987–995. [CrossRef] [PubMed]
Gundert, T. J., Shadden, S. C., Williams, A. R., Koo, B. K., Feinstein, J. A., and LaDisa, J. F., Jr., 2011, “A Rapid and Computationally Inexpensive Method to Virtually Implant Current and Next-Generation Stents Into Subject-Specific Computational Fluid Dynamics Models,” Annu. Biomed. Eng., 39(5), pp. 1423–1437. [CrossRef]
Muller, J., Sahni, O., Li, X., Jansen, K. E., Shephard, M. S., and Taylor, C. A., 2005, “Anisotropic Adaptive Finite Element Method for Modelling Blood Flow,” Comput. Methods Biomech. Biomed. Eng., 8(5), pp. 295–305. [CrossRef]
Sahni, O., Muller, J., Jansen, K. E., Shephard, M. S., and Taylor, C. A., 2006, “Efficient Anisotropic Adaptive Discretization of the Cardiovascular System,” Comput. Methods Appl. Mech. Eng., 195(41–43), pp. 5634–5655. [CrossRef]
Gundert, T. J., Dholakia, R. J., McMahon, D., and LaDisa, J. F., 2013, “Computational Fluid Dynamics Evaluation of Equivalency in Hemodynamic Alterations Between Driver, Integrity, and Similar Stents Implanted Into an Idealized Coronary Artery,” J. Med. Dev., 7(1), p. 011004. [CrossRef]
Williams, A. R., Koo, B. K., Gundert, T. J., Fitzgerald, P. J., and LaDisa, J. F., Jr., 2010, “Local Hemodynamic Changes Caused by Main Branch Stent Implantation and Subsequent Virtual Side Branch Balloon Angioplasty in a Representative Coronary Bifurcation,” J. Appl. Physiol. (1985), 109(2), pp. 532–540. [CrossRef] [PubMed]
Wendell, D. C., Samyn, M. M., Cava, J. R., Ellwein, L. M., Krolikowski, M. M., Gandy, K. L., Pelech, A. N., Shadden, S. C., and LaDisa, J. F., Jr., 2013, “Including Aortic Valve Morphology in Computational Fluid Dynamics Simulations: Initial Findings and Application to Aortic Coarctation,” Med. Eng. Phys., 35(6), pp. 723–735. [CrossRef] [PubMed]
Holdsworth, D. W., Norley, C. J., Frayne, R., Steinman, D. A., and Rutt, B. K., 1999, “Characterization of Common Carotid Artery Blood-Flow Waveforms in Normal Human Subjects,” Physiol. Meas., 20(3), pp. 219–240. [CrossRef] [PubMed]
Gow, B. S., Schonfeld, D., and Patel, D. J., 1974, “The Dynamic Elastic Properties of the Canine Left Circumflex Coronary Artery,” J. Biomech., 7(5), pp. 389–395. [CrossRef] [PubMed]
Stergiopulos, N., Meister, J. J., and Westerhof, N., 1994, “Simple and Accurate Way for Estimating Total and Segmental Arterial Compliance: The Pulse Pressure Method,” Annu. Biomed. Eng., 22(4), pp. 392–397. [CrossRef]
Stergiopulos, N., Segers, P., and Westerhof, N., 1999, “Use of Pulse Pressure Method for Estimating Total Arterial Compliance In Vivo,” Am. J. Physiol., 276(2 Pt 2), pp. H424–H428. [PubMed]
Vignon-Clementel, I. E., Figueroa, C. A., Jansen, K. E., and Taylor, C. A., 2006, “Outflow Boundary Conditions for Three-Dimensional Finite Element Modeling of Blood Flow and Pressure in Arteries,” Comput. Methods Appl. Mech. Eng., 195(29–32), pp. 3776–3796. [CrossRef]
Van Huis, G. A., Sipkema, P., and Westerhof, N., 1987, “Coronary Input Impedance During Cardiac Cycle as Determined by Impulse Response Method,” Am. J. Physiol., 253(2 Pt 2), pp. H317–H324. [PubMed]
Esmaily-Moghadam, M., Bazilevs, Y., and Marsden, A. L., 2013, “A New Preconditioning Technique for Implicitly Coupled Multidomain Simulations With Applications to Hemodynamics,” Comput. Mech., 52(5), pp. 1141–1152. [CrossRef]
Quam, D. J., 2012, “Advanced Visualization and Intuitive User Interface Systems for Biomedical Applications,” Master's thesis, Marquette University, Milwaukee, WI.
Cruz-Neira, C., Sandin, D. J., Defanti, T. A., Kenyon, R. V., and Hart, J. C., 1992, “The Cave: Audio Visual Experience Automatic Virtual Environment,” Commun. ACM, 35(6), pp. 64–72. [CrossRef]
Slater, M., and Wilbur, S., 1997, “A Framework for Immersive Virtual Environments (Five): Speculations on the Role of Presence in Virtual Environments,” Presence Teleoperators Virtual Environ., 6(6), pp. 603–616.
Van Dam, A., Forsberg, A., Laidlaw, D. H., Laviola, J. J., and Simpson, R. M., 2000, “Immersive VR for Scientific Visualization: A Progress Report,” IEEE Comput. Graphics Appl., 20(6), pp. 26–52. [CrossRef]
Presson, C. C., Delange, N., and Hazelrigg, M. D., 1987, “Orientation-Specificity in Kinesthetic Spatial Learning: The Role of Multiple Orientations,” Mem. Cogn., 15(3), pp. 225–229. [CrossRef]
Dede, C., 2009, “Immersive Interfaces for Engagement and Learning,” Science, 323(5910), pp. 66–69. [CrossRef] [PubMed]
Gnasso, A., Irace, C., Carallo, C., De Franceschi, M. S., Motti, C., Mattioli, P. L., and Pujia, A., 1997, “In Vivo Association between Low Wall Shear Stress and Plaque in Subjects With Asymmetrical Carotid Atherosclerosis,” Stroke, 28(5), pp. 993–998. [CrossRef] [PubMed]
Marshall, I., Zhao, S., Papathanasopoulou, P., Hoskins, P., and Xu, Y., 2004, “Mri and Cfd Studies of Pulsatile Flow in Healthy and Stenosed Carotid Bifurcation Models,” J. Biomech., 37(5), pp. 679–687. [CrossRef] [PubMed]
Zhao, S. Z., Xu, X. Y., Hughes, A. D., Thom, S. A., Stanton, A. V., Ariff, B., and Long, Q., 2000, “Blood Flow and Vessel Mechanics in a Physiologically Realistic Model of a Human Carotid Arterial Bifurcation,” J. Biomech., 33(8), pp. 975–984. [CrossRef] [PubMed]
Phillips, D. J., Greene, F. M., Jr., Langlois, Y., Roederer, G. O., and Strandness, D. E., Jr., 1983, “Flow Velocity Patterns in the Carotid Bifurcations of Young, Presumed Normal Subjects,” Ultrasound Med. Biol., 9(1), pp. 39–49. [CrossRef] [PubMed]
Nicholls, S. C., Phillips, D. J., Primozich, J. F., Lawrence, R. L., Kohler, T. R., Rudd, T. G., and Strandness, D. E., Jr., 1989, “Diagnostic Significance of Flow Separation in the Carotid Bulb,” Stroke, 20(2), pp. 175–182. [CrossRef] [PubMed]
Steinke, W., Kloetzsch, C., and Hennerici, M., 1990, “Variability of Flow Patterns in the Normal Carotid Bifurcation,” Atherosclerosis, 84(2–3), pp. 121–127. [CrossRef] [PubMed]
Stone, P. H., Coskun, A. U., Yeghiazarians, Y., Kinlay, S., Popma, J. J., Kuntz, R. E., and Feldman, C. L., 2003, “Prediction of Sites of Coronary Atherosclerosis Progression: In Vivo Profiling of Endothelial Shear Stress, Lumen, and Outer Vessel Wall Characteristics to Predict Vascular Behavior,” Curr. Opin. Cardiol., 18(6), pp. 458–470. [CrossRef] [PubMed]
Papafaklis, M. I., Bourantas, C. V., Theodorakis, P. E., Katsouras, C. S., Naka, K. K., Fotiadis, D. I., and Michalis, L. K., 2010, “The Effect of Shear Stress on Neointimal Response Following Sirolimus- and Paclitaxel-Eluting Stent Implantation Compared With Bare-Metal Stents in Humans,” JACC Cardiovasc. Interventions, 3(11), pp. 1181–1189. [CrossRef]
LaDisa, J. F., Jr., Olson, L. E., Hettrick, D. A., Warltier, D. C., Kersten, J. R., and Pagel, P. S., 2005, “Axial Stent Strut Angle Influences Wall Shear Stress After Stent Implantation: Analysis Using 3D Computational Fluid Dynamics Models of Stent Foreshortening,” Biomed. Eng. Online, 4, p. 59. [CrossRef] [PubMed]
Farooq, V., Gogas, B. D., and Serruys, P. W., 2011, “Restenosis: Delineating the Numerous Causes of Drug-Eluting Stent Restenosis,” Circ. Cardiovasc. Interventions, 4(2), pp. 195–205. [CrossRef]
Quam, D. J., Ellwein, L. M., Otake, H., Migrino, R. Q., and Ladisa, J. F., 2011, “Mobile Virtual Reality System for Cardiovascular Cfd Analysis,” Biomedical Engineering Society (BMES) Annual Meeting, Hartford, CT, Oct. 12–15, p. 56.
Aumüller, M., Lang, R., Rainer, D., Schulze, J. P., Werner, A., Wolf, P., and Wössner, U., 2008, COVISE (COllaborative VIsualization and Simulation Environment), Ver. 6.5, High Performance Computing Center Stuttgart, Universität Stuttgart, Stuttgart, DE.
Leigh, J., Renambot, L., Johnson, A., Jeong, B., Jagodic, R., Schwarz, N., Svistula, D., Singh, R., Aguilera, J., Wang, X., Vishwanath, V., Lopez, B., Sandin, D., Peterka, T., Girado, J., Kooima, R., Ge, J., Long, L., Verlo, A., Defanti, T. A., Brown, M., Cox, D., Patterson, R., Dorn, P., Wefel, P., Levy, S., Talandis, J., Reitzer, J., Prudhomme, T., Coffin, T., Davis, B., Wielinga, P., Stolk, B., Bum Koo, G., Kim, J., Han, S., Kim, J., Corrie, B., Zimmerman, T., Boulanger, P., and Garcia, M., 2006, “The Global Lambda Visualization Facility: An International Ultra-High-Definition Wide-Area Visualization Collaboratory,” Future Gener. Comput. Syst., 22(8), pp. 964–971. [CrossRef]
Reda, K., Knoll, A., Nomura, K. I., Papka, M. E., Johnson, A. E., and Leigh, J., 2013, “Visualizing Large-Scale Atomistic Simulations in Ultra-Resolution Immersive Environments,” Proceedings of the IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV’13), Atlanta, GA, Oct. 13–14, pp. 59–65. [CrossRef]
Gascon, J., Bayona, J. M., Espadero, J. M., and Otaduy, M., 2011, “Blendercave: Easy Vr Authoring for Multi-Screen Displays,” SIACG 2011: Ibero-American Symposium in Computer Graphics, Faro, Portugal, June 1–3.
Kitware, Inc., 2012, paraview.


Grahic Jump Location
Fig. 1

Flowchart describing the immersive visualization workflow. Steps a–f are common to many vascular CFD studies, while steps g through m are the focus of the current work and present new methods applied beyond conventional CFD processing. Medical imaging data is obtained and segmented (a) to create a 3D model (b) of the vasculature. In some cases, a stent is virtually implanted (c) prior to creating the finite-element model (d). Boundary conditions are then applied (e) before conducting a CFD simulation (f). The postprocessing stage begins by resampling the 3D model and hemodynamic results (g), the data is then transformed to meet the scale of the IVE (h) and custom 3D content is generated from the simulation results (j). The data is then prepared for the IVE (k) and the medical images are registered with the 3D model (i) prior to presentation in the IVE (m).

Grahic Jump Location
Fig. 2

A male user of average height is shown standing within the four-walled IVE of the current investigation (a). The LCCA CFD results have been rendered within the IVE and the user is wearing active shutter glasses to allow him to view the results of the workflow in stereoscopic 3D. Blood velocity is communicated by the length and color of the velocity vectors that are pointing into the plane of the screen. A pressure versus time curve plotted with a temporal indicator to communicate position within the cardiac cycle can also be toggled on in the upper left corner of the front screen. A spatial indicator of location within the artery can similarly be toggled on. A schematic illustration of the IVE is shown in (b). The black dots in the image represent high-resolution projectors. The front and side screens are rear-projected, while the floor is top-projected. A schematic illustration of the system components is also provided in (c).

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Fig. 5

Predetermined vantage points facilitating smooth navigation during collaboration for the LCCA case. (a) shows the entire section from a wide vantage point. (b) shows the vantage point viewing into the vessel in the direction of blood flow proximal to the carotid bifurcation. (c) shows a vantage within the vessel upstream of the flow divider. (d) is an external view of the LCCA that is optimized for viewing the MR imaging slices. (e) and (f) show the vessel distal to the bifurcation, in each respective daughter branch.

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Fig. 6

IVE images showing CFD data at two time points- immediately following the stent implantation (left) and at 6-month follow-up (right). OCT was used to acquire all slices. A single OCT slice at corresponding locations is shown along with its relationship to TAWSS. A region of thrombus with high TAWSS after stenting (left) that subsequently remodels or migrates to yield a region of relatively diminished TAWSS (right) at 6-month follow-up is also shown. The images on the bottom of the figure show a user viewing OCT images and corresponding TAWSS results from the spatial location of interest where thrombus was observed after stenting. The poststenting immersive results are shown above the follow-up results.

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Fig. 3

3D rendering of spatial image orientation for cases 1 and 2. Slices A–E have been segmented to differentiate the plaque morphology according to the legend on the bottom left. In each slice the inner-most layer (the lumen) is used to create the vessel model. The MR acquisition sequence collects axial images. The eye icon indicates the user's position in virtual space, which controls the opacity of slices in close proximity. OCT images (F–L) are acquired in such a way that images are not necessarily orthogonal to the vessel's central axis. The outline of the stent and lumen are well resolved as seen in the turquoise and white traces, respectively. The thrombus seen in slice J is well resolved with OCT. TAWSS is colocated with the respective medical imaging modalities for both cases. At lower left the user can appreciate the colocation of the region of low TAWSS with increased wall thickness (slice C). At lower right, OCT images elucidate the relationship between stent location and regions of altered TAWSS (slice K). The image on the bottom of the figure shows a user viewing LCCA imaging data, spatially colocalized TAWSS results, and peak systolic velocity vectors within the IVE used for the current investigation. Note the differences in scale between TAWSS in the LCCA results shown in the top (0–30 dyn/cm2) and bottom (0–15 dyn/cm2) images.

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Fig. 4

Temporal and time-averaged hemodynamic quantities rendered in IVE. a–c show velocity vectors at specific temporal locations as indicated on the corresponding pressure waveform. The velocity is communicated by the vector magnitude and color; after reaching maximum velocity immediately prior to peak systole, the velocity decreases with time. The collection of velocity results are used to create TAWSS results as visualized in (d) and (e). The inset of velocity images (c) also shows a region of flow reversal which is consistent with the high values of OSI (f). The image on the bottom of the figure shows a user viewing LCCA velocity vectors within the IVE used for the current investigation. The velocity vectors correspond to the time denoted by time point B (b) above, and the user has navigated into the vessel so that TAWSS on the back (e) of the LCCA is being viewed simultaneously.



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