Skip to main content

Web-Based Vascular Flow Simulation Visualization with Lossy Data Compression for Fast Transmission

  • Conference paper
  • First Online:
Book cover Augmented Reality, Virtual Reality, and Computer Graphics (AVR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10851))

Abstract

In this paper, we present a web-based system for visualization of flow simulation results in the vascular system for use with consumer-level hardware. The presented tool allows users to design, execute and visualize a flow simulation with a simple workflow on a desktop computer or a mobile device. The web interface allows users to select a vascular model, define the flow simulation parameters, execute the simulation, and interactively visualize the simulation results in real time using multiple visualization techniques. The server-side prepares the model for simulation and performs the simulation using SimVascular. To provide a more efficient transfer of the large amounts of simulation results to the web client, as well as reduce storage requirements on the server, we introduce a novel hybrid lossy compression method. The method uses an octree data subdivision approach combined with an iterative approach that regresses the data points to a B-Spline volume. The evaluation results show that our method achieves compression ratios of up to 5.7 for the tested examples at a given error rate, comparable to other approaches while specifically intended for visualization purposes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Mendis, S., Puska, P., Norrving, B., et al.: Global Atlas on Cardiovascular Disease Prevention and Control. World Health Organization (2011)

    Google Scholar 

  2. Doost, S.N., Ghista, D., Su, B., Zhong, L., Morsi, Y.S.: Heart blood flow simulation: a perspective review. Biomed. Eng. Online 15(1), 101 (2016)

    Article  Google Scholar 

  3. Arts, T., et al.: Patient-specific modeling of cardiovascular dynamics with a major role for adaptation. In: Kerckhoffs, R. (ed.) Patient-Specific Modeling of the Cardiovascular System, pp. 21–41. Springer, New York (2010). https://doi.org/10.1007/978-1-4419-6691-9_2

    Chapter  Google Scholar 

  4. Taylor, C.A., Hughes, T.J., Zarins, C.K.: Finite element modeling of blood flow in arteries. Comput. Methods Appl. Mech. Eng. 158(1–2), 155–196 (1998)

    Article  MathSciNet  Google Scholar 

  5. Wilson, N., Wang, K., Dutton, R.W., Taylor, C.: A software framework for creating patient specific geometric models from medical imaging data for simulation based medical planning of vascular surgery. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 449–456. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45468-3_54

    Chapter  Google Scholar 

  6. Antiga, L., Piccinelli, M., Botti, L., Ene-Iordache, B., Remuzzi, A., Steinman, D.A.: An image-based modeling framework for patient-specific computational hemodynamics. Med. Biol. Eng. Comput. 46(11), 1097 (2008)

    Article  Google Scholar 

  7. Marchenko, Y., Volkau, I., Nowinski, W.L.: Vascular editor: from angiographic images to 3D vascular models. J. Digit. Imaging 23(4), 386–398 (2010)

    Article  Google Scholar 

  8. Kretschmer, J., Godenschwager, C., Preim, B., Stamminger, M.: Interactive patient-specific vascular modeling with sweep surfaces. IEEE Trans. Vis. Comput. Graph. 19(12), 2828–2837 (2013)

    Article  Google Scholar 

  9. Updegrove, A., Wilson, N.M., Merkow, J., Lan, H., Marsden, A.L., Shadden, S.C.: Simvascular: an open source pipeline for cardiovascular simulation. Ann. Biomed. Eng. 45(3), 525–541 (2017)

    Article  Google Scholar 

  10. Zhou, M., Sahni, O., Kim, H.J., Figueroa, C.A., Taylor, C.A., Shephard, M.S., Jansen, K.E.: Cardiovascular flow simulation at extreme scale. Comput. Mech. 46(1), 71–82 (2010)

    Article  MathSciNet  Google Scholar 

  11. Meier, S., Hennemuth, A., Tchipev, N., Harloff, A., Markl, M., Preusser, T.: Towards patient-individual blood flow simulations based on PC-MRI measurements. Inform. J. 41, 4–7 (2011)

    Google Scholar 

  12. Mazzeo, M., Coveney, P.: HemeLB: a high performance parallel lattice-Boltzmann code for large scale fluid flow in complex geometries. Comput. Phys. Commun. 178(12), 894–914 (2008)

    Article  MathSciNet  Google Scholar 

  13. Bernaschi, M., Melchionna, S., Succi, S., Fyta, M., Kaxiras, E., Sircar, J.: MUPHY: a parallel multi physics/scale code for high performance bio-fluidic simulations. Comput. Phys. Commun. 180(9), 1495–1502 (2009)

    Article  Google Scholar 

  14. Köhler, B., Born, S., van Pelt, R.F.P., Hennemuth, A., Preim, U., Preim, B.: A survey of cardiac 4D PC-MRI data processing. Comput. Graph. Forum 36(6), 5–35 (2017)

    Article  Google Scholar 

  15. Anastasi, G., Bramanti, P., Di Bella, P., Favaloro, A., Trimarchi, F., Magaudda, L., Gaeta, M., Scribano, E., Bruschetta, D., Milardi, D.: Volume rendering based on magnetic resonance imaging: advances in understanding the three-dimensional anatomy of the human knee. J. Anat. 211(3), 399–406 (2007)

    Article  Google Scholar 

  16. Ueng, S.K., Sikorski, K., Ma, K.L.: Fast algorithms for visualizing fluid motion in steady flow on unstructured grids. In: Proceedings of the IEEE Conference on Visualization, pp. 313–320 (1995)

    Google Scholar 

  17. Schroeder, W., Martin, K., Lorensen, B.: The Visualization Toolkit, 4th edn. Kitware, New York (2006)

    Google Scholar 

  18. Jourdain, S., Ayachit, U., Geveci, B.: ParaViewWeb: a web framework for 3D visualization and data processing. Int. J. Comput. Inf. Syst. Ind. Manag. Appl. 3, 870–877 (2011)

    Google Scholar 

  19. Lindstrom, P., Isenburg, M.: Fast and efficient compression of floating-point data. IEEE Trans. Vis. Comput. Graph. 12(5), 1245–1250 (2006)

    Article  Google Scholar 

  20. Lindstrom, P.: Fixed-rate compressed floating-point arrays. IEEE Trans. Vis. Comput. Graph. 20(12), 2674–2683 (2014)

    Article  Google Scholar 

  21. Belhadef, L., Maaza, Z.M.: Lossless 4D medical images compression with motion compensation and lifting wavelet transform. Int. J. Signal Process. Syst. 4(2), 168–171 (2016)

    Google Scholar 

  22. Sakai, R., Sasaki, D., Obayashi, S., Nakahashi, K.: Wavelet-based data compression for flow simulation on block-structured Cartesian mesh. Int. J. Numer. Methods Fluids 73(5), 462–476 (2013)

    Article  Google Scholar 

  23. Al-Khafaji, G., George, L.E.: Fast lossless compression of medical images based on polynomial. Int. J. Comput. Appl. 70(15), 28–32 (2013)

    Google Scholar 

  24. Nguyen, K.G., Saupe, D.: Rapid high quality compression of volume data for visualization. Comput. Graph. Forum 20(3), 49–57 (2001)

    Article  Google Scholar 

  25. 754-2008: IEEE standard for floating-point arithmetic. Standard. IEEE, August 2008

    Google Scholar 

  26. Sohn, B.S., Bajaj, C., Siddavanahalli, V.: Feature based volumetric video compression for interactive playback. In: Proceedings of the 2002 IEEE Symposium on Volume Visualization and Graphics, VVS 2002, Piscataway, pp. 89–96. IEEE Press (2002)

    Google Scholar 

  27. Lehmann, H., Werzner, E., Mendes, M.A.A., Trimis, D., Jung, B., Ray, S.: In situ data compression algorithm for detailed numerical simulation of liquid metal filtration through regularly structured porous media. Adv. Eng. Mater. 15(12), 1260–1269 (2013)

    Article  Google Scholar 

  28. Iverson, J., Kamath, C., Karypis, G.: Fast and effective lossy compression algorithms for scientific datasets. In: Kaklamanis, C., Papatheodorou, T., Spirakis, P.G. (eds.) Euro-Par 2012. LNCS, vol. 7484, pp. 843–856. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32820-6_83

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ciril Bohak .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Oblak, R., Bohak, C., Marolt, M. (2018). Web-Based Vascular Flow Simulation Visualization with Lossy Data Compression for Fast Transmission. In: De Paolis, L., Bourdot, P. (eds) Augmented Reality, Virtual Reality, and Computer Graphics. AVR 2018. Lecture Notes in Computer Science(), vol 10851. Springer, Cham. https://doi.org/10.1007/978-3-319-95282-6_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-95282-6_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95281-9

  • Online ISBN: 978-3-319-95282-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics