The Architecture of Specialized GPU Clusters Used for Solving the Inverse Problems of 3D Low-Frequency Ultrasonic Tomography

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 793)


This paper is dedicated to the development of the architecture of specialized GPU clusters that can be used as computing systems in medical ultrasound tomographic facilities that are currently being developed. The inverse problem of ultrasonic tomography is formulated as a coefficient inverse problem for a hyperbolic equation. An approximate solution is constructed using an iterative process of minimizing the residual functional between the measured and simulated wave fields. The algorithms used to solve the inverse problem are optimized for a GPU. The requirements for the architecture of a GPU cluster are formulated. The proposed architecture accelerates the reconstruction of ultrasonic tomographic images by 1000 times compared to what is achieved by a personal computer.


Ultrasonic tomography Coefficient inverse problems Finite-difference time-domain (FDTD) method GPU clusters Medical imaging 



This research was supported by Russian Science Foundation (project No. 17–11-01065). The study was carried out at the Lomonosov Moscow State University.


  1. 1.
    Duric, N., Littrup, P., Li, C., Roy, O., et al.: Breast ultrasound tomography: bridging the gap to clinical practice. Proc. SPIE. Med. Imaging 8320, 83200O (2012)CrossRefGoogle Scholar
  2. 2.
    Wiskin, J., Borup, D., Andre, M., Klock, J., et al.: Three-dimensional nonlinear inverse scattering: quantitative transmission algorithms, refraction corrected reflection, scanner design, and clinical results. J. Acoust. Soc. Am. 133, 3229 (2013)CrossRefGoogle Scholar
  3. 3.
    Birk, M., Dapp, R., Ruiter, N.V., Becker, J.: GPU-based iterative transmission reconstruction in 3D ultrasound computer tomography. J. Parallel Distrib. Comput. 74, 1730–1743 (2014)CrossRefGoogle Scholar
  4. 4.
    Goncharsky, A.V., Romanov, S.Y.: Inverse problems of ultrasound tomography in models with attenuation. Phys. Med. Biol. 59, 1979–2004 (2014)CrossRefGoogle Scholar
  5. 5.
    Goncharsky, A.V., Romanov, S.Y., Seryozhnikov, S.Y.: A computer simulation study of soft tissue characterization using low-frequency ultrasonic tomography. Ultrasonics 67, 136–150 (2016)CrossRefGoogle Scholar
  6. 6.
    Goncharsky, A.V., Romanov, S.Y., Seryozhnikov, S.Y.: Inverse problems of 3D ultrasonic tomography with complete and incomplete range data. Wave Motion 51, 389–404 (2014)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Goncharskii, A.V., Romanov, S.Y.: Two approaches to the solution of coefficient inverse problems for wave equations. Comput. Math. Math. Phys. 52, 245–251 (2012)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Goncharsky, A.V., Romanov, S.Y.: Iterative methods for solving coefficient inverse problems of wave tomography in models with attenuation. Inverse Prob. 33(2), 025003 (2017)CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Natterer, F.: Possibilities and limitations of time domain wave equation imaging. In: Contemporary Mathematics, vol. 559, pp. 151–162. American Mathematical Society, Providence (2011)Google Scholar
  10. 10.
    Beilina, L., Klibanov, M.V., Kokurin, M.Y.: Adaptivity with relaxation for ill-posed problems and global convergence for a coefficient inverse problem. J. Math. Sci. 167, 279–325 (2010)CrossRefzbMATHMathSciNetGoogle Scholar
  11. 11.
    Mu, S.-Y., Chang, H.-W.: Dispersion and local-error analysis of compact LFE-27 formula for obtaining sixth-order accurate numerical solutions of 3D Helmholtz equation. Prog. Electromagnet. Res. 143, 285–314 (2013)CrossRefGoogle Scholar
  12. 12.
    Engquist, B., Majda, A.: Absorbing boundary conditions for the numerical simulation of waves. Math. Comput. 31, 629–651 (1977)CrossRefzbMATHMathSciNetGoogle Scholar
  13. 13.
    Voevodin, V.V., Zhumatiy, S.A., Sobolev, S.I., Antonov, A.S., Bryzgalov, P.A., Nikitenko, D.A., Stefanov, K.S., Voevodin, V.V.: Practice of “Lomonosov” supercomputer. Open Syst. J. 7, 36–39 (2012)Google Scholar
  14. 14.
    Zhang, L., Du, Y., Wu, D.: GPU-Accelerated FDTD simulation of human tissue using C++ AMP. In: 31st International Review of Progress in Applied Computational Electromagnetics (ACES), Williamsburg, VA, pp. 1–2 (2015)Google Scholar

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Lomonosov Moscow State UniversityMoscowRussia

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