4D MRI Flow Coupled to Physics-Based Fluid Simulation for Blood-Flow Visualization
Modern MRI measurements deliver volumetric and time‐varying blood‐flow data of unprecedented quality. Visual analysis of these data potentially leads to a better diagnosis and risk assessment of various cardiovascular diseases. Recent advances have improved the speed and quality of the imaging data...
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| Published in | Computer graphics forum Vol. 33; no. 3; pp. 121 - 130 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.06.2014
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Modern MRI measurements deliver volumetric and time‐varying blood‐flow data of unprecedented quality. Visual analysis of these data potentially leads to a better diagnosis and risk assessment of various cardiovascular diseases. Recent advances have improved the speed and quality of the imaging data considerably. Nevertheless, the data remains compromised by noise and a lack of spatiotemporal resolution. Besides imaging data, also numerical simulations are employed. These are based on mathematical models of specific features of physical reality. However, these models require realistic parameters and boundary conditions based on measurements. We propose to use data assimilation to bring measured data and physically‐based simulation together, and to harness the mutual benefits. The accuracy and noise robustness of the coupled approach is validated using an analytic flow field. Furthermore, we present a comparative visualization that conveys the differences between using conventional interpolation and our coupled approach. |
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| AbstractList | Modern MRI measurements deliver volumetric and time-varying blood-flow data of unprecedented quality. Visual analysis of these data potentially leads to a better diagnosis and risk assessment of various cardiovascular diseases. Recent advances have improved the speed and quality of the imaging data considerably. Nevertheless, the data remains compromised by noise and a lack of spatiotemporal resolution. Besides imaging data, also numerical simulations are employed. These are based on mathematical models of specific features of physical reality. However, these models require realistic parameters and boundary conditions based on measurements. We propose to use data assimilation to bring measured data and physically-based simulation together, and to harness the mutual benefits. The accuracy and noise robustness of the coupled approach is validated using an analytic flow field. Furthermore, we present a comparative visualization that conveys the differences between using conventional interpolation and our coupled approach. [PUBLICATION ABSTRACT] Modern MRI measurements deliver volumetric and time-varying blood-flow data of unprecedented quality. Visual analysis of these data potentially leads to a better diagnosis and risk assessment of various cardiovascular diseases. Recent advances have improved the speed and quality of the imaging data considerably. Nevertheless, the data remains compromised by noise and a lack of spatiotemporal resolution. Besides imaging data, also numerical simulations are employed. These are based on mathematical models of specific features of physical reality. However, these models require realistic parameters and boundary conditions based on measurements. We propose to use data assimilation to bring measured data and physically-based simulation together, and to harness the mutual benefits. The accuracy and noise robustness of the coupled approach is validated using an analytic flow field. Furthermore, we present a comparative visualization that conveys the differences between using conventional interpolation and our coupled approach. |
| Author | de Hoon, N. Jalba, A. Vilanova, A. van Pelt, R. |
| Author_xml | – sequence: 1 givenname: N. surname: de Hoon fullname: de Hoon, N. organization: Computer Science and Mathematics, Eindhoven University of Technology, The Netherlands – sequence: 2 givenname: R. surname: van Pelt fullname: van Pelt, R. organization: Biomedical Engineering, Eindhoven University of Technology, The Netherlands – sequence: 3 givenname: A. surname: Jalba fullname: Jalba, A. organization: Computer Science and Mathematics, Eindhoven University of Technology, The Netherlands – sequence: 4 givenname: A. surname: Vilanova fullname: Vilanova, A. organization: Biomedical Engineering, Eindhoven University of Technology, The Netherlands |
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| CitedBy_id | crossref_primary_10_1016_j_media_2020_101773 crossref_primary_10_1098_rsif_2021_0751 crossref_primary_10_1002_mrm_28269 crossref_primary_10_1109_TIP_2024_3470553 crossref_primary_10_1016_j_media_2015_08_012 crossref_primary_10_1111_cgf_13394 crossref_primary_10_1007_s00366_022_01723_5 crossref_primary_10_1111_cgf_12803 |
| Cites_doi | 10.1007/s10915-011-9547-6 10.1109/TVCG.2013.189 10.1016/S0045-7825(98)80008-X 10.1016/0010-4655(88)90020-3 10.1146/annurev.bioeng.10.061807.160521 10.1090/S0025-5718-04-01678-3 10.1145/1276377.1276502 10.1002/jmri.23632 10.1109/CGI.1997.601299 10.1145/1073204.1073298 10.1137/1.9780898719604 10.1016/S0065-2687(08)60442-2 10.1002/mrm.1910340618 10.1109/PacificVis.2013.6596137 10.1201/b10635 10.1161/CIR.0b013e31828124ad 10.1002/jmri.1098 10.1002/cav.17 10.1145/311535.311548 10.1145/1531326.1531347 10.1093/mnras/181.3.375 10.1046/j.1525-1594.2002.07085.x 10.1002/mrm.21109 10.1145/1073204.1073282 10.1007/s10439-005-2495-2 10.1109/TVCG.2004.39 |
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| Copyright | 2014 The Author(s) Computer Graphics Forum © 2014 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2014 The Eurographics Association and John Wiley & Sons Ltd. |
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| References_xml | – reference: Köhler B., Gasteiger R., Preim U., Theisel H., Gutberlet M., Preim B.: Semi-Automatic vortex extraction in 4D PC-MRI cardiac blood flow data using line predicates. IEEE Transactions on Visualization and Computer Graphics 19, 12 (2013), 2773-2783. 2 – reference: Selle A., Rasmussen N., Fedkiw R.: A vortex particle method for smoke, water and explosions. ACM Transactions on Graphics 24, 3 (2005), 910-914. 3 – reference: Gudbjartsson H., Patz S.: The Rician distribution of noisy MRI data. Magnetic Resonance in Medicine 34, 6 (1995), 910-914. 6 – reference: Zhu Y., Bridson R.: Animating sand as a fluid. ACM Transactions on Graphics 24, 3 (2005), 965-972. 4 – reference: Markl M., Frydrychowicz A., Kozerke S., Hope M., Wieben O.: 4D flow MRI. Journal of Magnetic Resonance Imaging 36, 5 (2012), 1015-1036. 1, 2 – reference: Funamoto K., Hayase T., Shirai A., Saijo Y., Yambe T.: Fundamental study of ultrasonic-measurement-integrated simulation of real blood flow in the aorta. Annals of Biomedical Engineering 33, 4 (2005), 415-428. 3 – reference: Taylor C.A., Hughes T., Zarins C.: Finite element modeling of blood flow in arteries. Computer Methods in Applied Mechanical Engineering 7825, 97 (1998), 155-196. 5 – reference: Bammer R., Hope T., Aksoy M., Alley M.: Time-resolved 3D quantitative flow MRI of the major intracranial vessels: Initial experience and comparative evaluation at 1.5T and 3.0T in combination with parallel imaging. Magnetic Resonance in Medicine 57, 1 (2007), 127-140. 6 – reference: Potchen E.J., Haacke E.M.: Magnetic resonance angiography: concepts & applications. Mosby-Year Book, 1993. 7 – reference: Taylor C.A., Figueroa C.A.: Patient-specific modeling of cardiovascular mechanics. Annual Review of Biomedical Engineering 11 (2009), 109-34. 2 – reference: Wood N.B., Weston S.J., Kilner P.J., Gosman A.D., Firmin D.N.: Combined MR imaging and CFD simulation of flow in the human descending aorta. Journal of Magnetic Resonance Imaging 13, 5 (2001), 699-713. 3 – reference: Ghil M., Malanotte-Rizzoli P.: Data assimilation in meteorology and oceanography. Advances in Geophysics 33 (1991), 141-266. 2 – reference: Verma V., Pang A.: Comparative flow visualization. IEEE Transactions on Visualization and Computer Graphics 10, 6 (2004), 609-24. 7 – reference: Horvath C., Geiger W.: Directable, high-resolution simulation of fire on the GPU. ACM Transactions on Graphics 28, 3 (2009), 41:1-41:8. 8 – reference: D'Elia M., Perego M., Veneziani A.: A variational data assimilation procedure for the incompressible navierstokes equations in hemodynamics. Journal of Scientific Computing 52, 2 (2012), 340-359. 3 – reference: Gingold R.A., Monaghan J.J.: Smoothed particle hydrodynamics: theory and application to non-spherical stars. Monthly Notices of the Royal Astronomical Society 181 (1977), 375-389. 4 – reference: Zhao H.: A fast sweeping method for Eikonal equations. Mathematics of Computation 74 (2005), 603-627. 4 – reference: Batty C., Bertails F., Bridson R.: A fast variational framework for accurate solid-fluid coupling. ACM Transactions on Graphics 26, 3 (2007), 100:1-100:7. 4, 5, 8 – reference: Brackbill J., Kothe D., Ruppel H.: FLIP: A low-dissipation, particle-in-cell method for fluid flow. Computer Physics Communications 48, 1 (1988), 25-38. 3, 4 – reference: Go A.S., Mozaffarian D., Roger V.L., Benjamin E.J., Berry J.D., Borden W.B., Bravata D.M., Dai S., Ford E.S., Fox C.S., Franco S., Fullerton H.J., Gillespie C., Hailpern S.M., Heit J.A., Howard V.J., Huffman M.D., Kissela B.M., Kittner S.J., Lackland D.T., Lichtman J.H., Lisabeth L.D., Magid D., Marcus G.M., Marelli A., Matchar D.B., McGuire D.K., Mohler E.R., Moy C.S., Mussolino M.E., Nichol G., Paynter N.P., Schreiner P.J., Sorlie P.D.J. Stein T. N. T., Virani S.S., Wong N.D., Woo D., Turner M.B.: Heart disease and stroke statistics - 2013 update. Circulation 127, 1 (2013), e6-e245. 1 – reference: Schwenke M., Hennemuth A., Fischer B., Friman O.: Blood flow computation in phase-contrast MRI by minimal paths in anisotropic media. Medical Image Computing and Computer-Assisted Intervention 14, 1 (2011), 436-443. 2 – reference: Anderson E., Bai Z., Bischof C., Blackford S., Demmel J., Dongarra J., Du Croz J., Greenbaum A., Hammarling S., McKenney A., Sorensen D.: LAPACK Users' Guide, third ed. Society for Industrial and Applied Mathematics, 1999. 8 – reference: Bridson R.: Fluid Simulation for Computer Graphics. A.K. Peters, 2008. 3, 8 – reference: Glor F.P., Westenberg J. J. M., Vierendeels J., Danilouchkine M., Verdonck P.: Validation of the coupling of magnetic resonance imaging velocity measurements with computational fluid dynamics in a U bend. Artificial Organs 26, 7 (2002), 622-35. 3 – reference: Hong J.-M., Kim C.-H.: Controlling fluid animation with geometric potential. Computer Animation and Virtual Worlds 15, 3-4 (2004), 147-157. 3, 9 – volume: 28 start-page: 41:1 issue: 3 year: 2009 end-page: 41:8 article-title: Directable, high‐resolution simulation of fire on the GPU publication-title: ACM Transactions on Graphics – volume: 13 start-page: 699 issue: 5 year: 2001 end-page: 713 article-title: Combined MR imaging and CFD simulation of flow in the human descending aorta publication-title: Journal of Magnetic Resonance Imaging – volume: 26 start-page: 100:1 issue: 3 year: 2007 end-page: 100:7 article-title: A fast variational framework for accurate solid‐fluid coupling publication-title: ACM Transactions on Graphics – start-page: 129 year: 2013 end-page: 136 – volume: 26 start-page: 622 issue: 7 year: 2002 end-page: 35 article-title: Validation of the coupling of magnetic resonance imaging velocity measurements with computational fluid dynamics in a U bend publication-title: Artificial Organs – volume: 24 start-page: 965 issue: 3 year: 2005 end-page: 972 article-title: Animating sand as a fluid publication-title: ACM Transactions on Graphics – start-page: 219 year: 2008 end-page: 228 – volume: 127 start-page: e6 issue: 1 year: 2013 article-title: Heart disease and stroke statistics – 2013 update publication-title: Circulation – volume: 33 start-page: 141 year: 1991 end-page: 266 article-title: Data assimilation in meteorology and oceanography publication-title: Advances in Geophysics – volume: 36 start-page: 1015 issue: 5 year: 2012 end-page: 1036 article-title: 4D flow MRI publication-title: Journal of Magnetic Resonance Imaging – start-page: 121 year: 1999 end-page: 128 – volume: 14 start-page: 436 issue: 1 year: 2011 end-page: 443 article-title: Blood flow computation in phase‐contrast MRI by minimal paths in anisotropic media publication-title: Medical Image Computing and Computer‐Assisted Intervention – volume: 57 start-page: 127 issue: 1 year: 2007 end-page: 140 article-title: Time‐resolved 3D quantitative flow MRI of the major intracranial vessels: Initial experience and comparative evaluation at 1.5T and 3.0T in combination with parallel imaging publication-title: Magnetic Resonance in Medicine – volume: 19 start-page: 2773 issue: 12 year: 2013 end-page: 2783 article-title: Semi‐Automatic vortex extraction in 4D PC‐MRI cardiac blood flow data using line predicates publication-title: IEEE Transactions on Visualization and Computer Graphics – year: 2012 – volume: 181 start-page: 375 year: 1977 end-page: 389 article-title: Smoothed particle hydrodynamics: theory and application to non‐spherical stars publication-title: Monthly Notices of the Royal Astronomical Society – start-page: 178 year: 1997 end-page: 188 – volume: 74 start-page: 603 year: 2005 end-page: 627 article-title: A fast sweeping method for Eikonal equations publication-title: Mathematics of Computation – year: 2008 – volume: 24 start-page: 910 issue: 3 year: 2005 end-page: 914 article-title: A vortex particle method for smoke, water and 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| Snippet | Modern MRI measurements deliver volumetric and time‐varying blood‐flow data of unprecedented quality. Visual analysis of these data potentially leads to a... Modern MRI measurements deliver volumetric and time-varying blood-flow data of unprecedented quality. Visual analysis of these data potentially leads to a... |
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| SubjectTerms | Analysis Cardiovascular disease Categories and Subject Descriptors (according to ACM CCS) Computer graphics Computer simulation Data assimilation Diseases G.1.1 [Mathematics of Computing] Numerical Analysis-Interpolation I.3.8 [Computer Graphics] Applications-4D PC-MRI Blood-Flow I.6.8 [Simulation and Modeling] Types of Simulation-Combined Imaging Joining Mathematical models Medical imaging Noise Risk Simulation Studies Visualization |
| Title | 4D MRI Flow Coupled to Physics-Based Fluid Simulation for Blood-Flow Visualization |
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