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Patient-specific 3D Ultrasound Simulation Based on Convolutional Ray-tracing and Appearance Optimization

  • Mehrdad SalehiEmail author
  • Seyed-Ahmad Ahmadi
  • Raphael Prevost
  • Nassir Navab
  • Wolfgang Wein
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9350)

Abstract

The simulation of medical ultrasound from patient-specific data may improve the planning and execution of interventions e.g. in the field of neurosurgery. However, both the long computation times and the limited realism due to lack of acoustic information from tomographic scans prevent a wide adoption of such a simulation. In this work, we address these problems by proposing a novel efficient ultrasound simulation method based on convolutional ray-tracing which directly takes volumetric image data as input. We show how the required acoustic simulation parameters can be derived from a segmented MRI scan of the patient. We also propose an automatic optimization of ultrasonic simulation parameters and tissue-specific acoustic properties from matching ultrasound and MRI scan data. Both qualitative and quantitative evaluation on a database of 14 neurosurgical patients demonstrate the potential of our approach for clinical use.

Keywords

Acoustic Parameter Medical Ultrasound Bhattacharyya Distance Ultrasound Simulation Time Gain Compensation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Bamber, J.C., Dickinson, R.J.: Ultrasonic B-scanning: a computer simulation. Physics in Medicine and Biology 25(3), 463–479 (1980)CrossRefGoogle Scholar
  2. 2.
    Bürger, B., Bettinghausen, S., Rädle, M., Hesser, J.: Real-time GPU-based ultrasound simulation using deformable mesh models. IEEE Transactions on Medical Imaging 32(3), 609–618 (2013)CrossRefGoogle Scholar
  3. 3.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 564–577 (2003)CrossRefGoogle Scholar
  4. 4.
    Gao, H., Choi, H.F., Claus, P., Boonen, S., Jaecques, S., Van Lenthe, G.H., Van der Perre, G., Lauriks, W., D’hooge, J.: A fast convolution-based methodology to simulate 2-D/3-D cardiac ultrasound images. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 56(2), 404–409 (2009)CrossRefGoogle Scholar
  5. 5.
    Gao, H., Hergum, T.T.R., Torp, H., D’hooge, J.: Comparison of the performance of different tools for fast simulation of ultrasound data. Ultrasonics 52(5), 573–577 (2012)CrossRefGoogle Scholar
  6. 6.
    He, K., Sun, J., Tang, X.: Guided image filtering. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 1–14. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Hedrick, W.R., Starchman, D.E., Hykes, D.L.: Ultrasound physics and instrumentation, 4th edn. Elsevier Mosby, St. Louis (2005)Google Scholar
  8. 8.
    Jensen, J.A.: A multi-threaded version of Field II. In: 2014 IEEE International Ultrasonics Symposium, pp. 2229–2232, September 2014Google Scholar
  9. 9.
    Karamalis, A., Wein, W., Navab, N.: Fast ultrasound image simulation using the Westervelt equation. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part I. LNCS, vol. 6361, pp. 243–250. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Mercier, L., Del Maestro, R., Petrecca, K., Araujo, D., Haegelen, C., Collins, D.: Online Database of Clinical MR and Ultrasound Images of Brain Tumors. Medical Physics 39, 3253 (2012)CrossRefGoogle Scholar
  11. 11.
    Meunier, J., Bertrand, M.: Ultrasonic texture motion analysis: theory and simulation. IEEE Transactions on Medical Imaging 14(2), 293–300 (1995)CrossRefGoogle Scholar
  12. 12.
    Wagner, R.F., Insana, M.F., Brown, D.G.: Statistical properties of radio-frequency and envelope-detected signals with applications to medical ultrasound. Journal of the Optical Society of America. A, Optics and image Science 4, 910–922 (1987)Google Scholar
  13. 13.
    Wein, W., Brunke, S., Khamene, A., Callstrom, M., Navab, N.: Automatic CT-Ultrasound Registration for Diagnostic Imaging and Image-Guided Intervention. Medical Image Analysis 12(5), 577 (2008)CrossRefGoogle Scholar
  14. 14.
    Zhang, Y., Brady, M., Smith, S.: Segmentation of brain mr images through a hidden markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging 20(1), 45–57 (2001)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mehrdad Salehi
    • 1
    • 3
    Email author
  • Seyed-Ahmad Ahmadi
    • 2
  • Raphael Prevost
    • 1
  • Nassir Navab
    • 3
    • 4
  • Wolfgang Wein
    • 1
  1. 1.ImFusion GmbHMünchenGermany
  2. 2.Department of NeurologyKlinikum der Universität München, LMUMünchenGermany
  3. 3.Computer Aided Medical ProceduresTechnische Universität MünchenMünchenGermany
  4. 4.Computer Aided Medical ProceduresJohns Hopkins UniversityBaltimoreUSA

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