DOI: https://doi.org/10.26089/NumMet.v18r327

The problem of choosing initial approximations in inverse problems of ultrasound tomography

Authors

  • A.V. Goncharsky
  • S.Yu. Romanov
  • S.Yu. Seryozhnikov

Keywords:

ultrasound tomography
wave equation
nonlinear coefficient inverse problem
iterative algorithms
initial approximation

Abstract

This paper is devoted to developing efficient iterative methods to solve nonlinear inverse problems of wave tomography. The iterative algorithms used to obtain an approximate solution of the inverse problem are based on an explicit representation of the gradient of the residual functional between the measured and computed wave fields. The choice of the initial approximation is of great importance for the convergence of the iterative process in a nonlinear inverse problem. The possibility of using an initial approximation to the sound speed obtained via solving the inverse problem in the ray approximation is studied. The efficiency of this approach is illustrated by solving model problems using a supercomputer. These model problems are designed for the ultrasound tomographic imaging of soft tissues in medicine.


Published

2017-08-14

Issue

Section

Section 1. Numerical methods and applications

Author Biographies

A.V. Goncharsky

Lomonosov Moscow State University
• Head of Laboratory

S.Yu. Romanov

Lomonosov Moscow State University
• Leading Researcher

S.Yu. Seryozhnikov


References

  1. R. J. Lavarello and M. L. Oelze, “Tomographic Reconstruction of Three- Dimensional Volumes Using the Distorted Born Iterative Method,” IEEE Trans. Med. Imaging 28 (10), 1643-1653 (2009).
  2. A. V. Goncharskii, S. L. Ovchinnikov, and S. Yu. Romanov, “On the One Problem of Wave Diagnostic,” Vestn. Mosk. Univ., Ser. 15: Vychisl. Mat. Kibern., No. 1, 7-13 (2010) [Moscow Univ. Comput. Math. Cybern. 34 (1), 1-7 (2010)].
  3. F. Natterer, “Possibilities and Limitations of Time Domain Wave Equation Imaging,” in Contemporary Mathematics (Am. Math. Soc. Press, Providence, 2011), Vol. 559, pp. 151-162.
  4. L. Beilina, M. V. Klibanov, and M. Yu. Kokurin, “Adaptivity with Relaxation for Ill-Posed Problems and Global Convergence for a Coefficient Inverse Problem,” J. Math. Sci. 167 (3), 279-325 (2010).
  5. A. V. Goncharskii and S. Yu. Romanov, “Two Approaches to the Solution of Coefficient Inverse Problems for Wave Equations,” Zh. Vychisl. Mat. Mat. Fiz. 52 (2), 263-269 (2012) [Comput. Math. Math. Phys. 52 (2), 245-251 (2012)].
  6. A. V. Goncharsky and S. Y. Romanov, “Supercomputer Technologies in Inverse Problems of Ultrasound Tomography,” Inverse Probl. 29 (7) (2013).
    doi 10.1088/0266-5611/29/7/075004
  7. A. V. Goncharsky and S. Y. Romanov, “Iterative Methods for Solving Coefficient Inverse Problems of Wave Tomography in Models with Attenuation,” Inverse Probl. 33 (2) (2017).
    doi 10.1088/1361-6420/33/2/025003
  8. M. M. Lavrentiev, Some Improperly Posed Problems of Mathematical Physics (Springer, New York, 1967).
  9. V. A. Burov, D. I. Zotov, and O. D. Rumyantseva, “Reconstruction of the Sound Velocity and Absorption Spatial Distributions in Soft Biological Tissue Phantoms from Experimental Ultrasound Tomography Data,” Akust. Zh. 61 (2), 254-273 (2015) [Acoust. Phys. 61 (2), 231-248 (2015)].
  10. C. H. Chang, S. W. Huang, H. C. Yang, et al., “Reconstruction of Ultrasonic Sound Velocity and Attenuation Coefficient Using Linear Arrays: Clinical Assessment,” Ultrasound Med. Biol. 33 (11), 1681-1687 (2007).
  11. N. Duric, P. Littrup, C. Li, et al., “Breast Ultrasound Tomography: Bridging the Gap to Clinical Practice,” in Proc. SPIE 8320 Medical Imaging 2012: Ultrasonic Imaging, Tomography, and Therapy (2012).
    doi 10.1117/12.910988
  12. H. Gemmeke, L. Berger, M. Birk, et al., “Hardware Setup for the Next Generation of 3D Ultrasound Computer Tomography,” in Proc. IEEE Nucl. Sci. Symp. Conf. Rec., Knoxville, USA, October 30-November 6, 2010
    doi 10.1109/NSSMIC.2010.5874228
  13. J. Wiskin, D. Borup, M. Andre, et al., “Three-Dimensional Nonlinear Inverse Scattering: Quantitative Transmission Algorithms, Refraction Corrected Reflection, Scanner Design, and Clinical Results,” J. Acoust. Soc. Am. 133 (2013).
    doi 10.1121/1.4805138
  14. A. V. Goncharsky, S. Y. Romanov, and S. Y. Seryozhnikov, “A Computer Simulation Study of Soft Tissue Characterization Using Low-Frequency Ultrasonic Tomography,” Ultrasonics 67, 136-150 (2016).
  15. A. V. Goncharskii, S. Yu. Romanov and S. Yu. Seryozhnikov, “Low-Frequency Three-Dimensional Ultrasonic Tomography,” Dokl. Akad. Nauk 468 (3), 268-271 (2016). [Phys. Dokl. 61 (5), 211-214 (2016)].
  16. A. S. Leonov and A. B. Bakushinsky, “Economic Numerical Method of Solving Coefficient Inverse Problem for 3D Wave Equation,” arXiv preprint: 1703.01216v1 [math.NA] (Cornell Univ. Library, Ithaca, 2017), available at
    https://arxiv.org/abs/1703.01216.
  17. A. G. Ramm, Multidimensional Inverse Scattering Problems (Longman Group, London, 1992).
  18. S. A. Matveev, D. A. Zheltkov, E. E. Tyrtyshnikov, and A. P. Smirnov, “Tensor Train Versus Monte Carlo for the Multicomponent Smoluchowski Coagulation Equation,” J. Comput. Phys. 316, 164-179 (2016).
  19. L. Beilina and M. V. Klibanov, Approximate Global Convergence and Adaptivity for Coefficient Inverse Problems (Springer, New York, 2012).
  20. A. V. Kuzhuget, L. Beilina, M. V. Klibanov, et al., “Blind Backscattering Experimental Data Collected in the Field and an Approximately Globally Convergent Inverse Algorithm,” Inverse Probl. 28 (9) (2012).
    doi 10.1088/0266-5611/28/9/095007
  21. A. V. Goncharsky, S. Y. Romanov, and S. Y. Seryozhnikov, Supercomputer Technologies in Development of Diagnostic Tomography Facilities (Politekh. Univ., St. Petersburg, 2016) [in Russian].
  22. S. Y. Romanov, Development of Algorithms for Solving Direct and Inverse Tomography Problems in Scalar Wave Models , Doctoral Dissertation in Mathematics and Physics (Moscow State Univ., Moscow, 2016).
  23. A. V. Goncharsky and S. Yu. Romanov, “Supercomputer Technologies in the Development of Methods for Solving Inverse Problems in Ultrasound Tomography,” Vychisl. Metody Programm. 13, 235-238 (2012).
  24. A. V. Goncharsky, S. Y. Romanov, and S. Y. Seryozhnikov, “Inverse Problems of 3D Ultrasonic Tomography with Complete and Incomplete Range Data,” Wave Motion 51 (3), 389-404 (2014).
  25. A. V. Goncharsky and S. Y. Romanov, “Inverse Problems of Ultrasound Tomography in Models with Attenuation,” Phys. Med. Biol. 59 (8), 1979-2004 (2014).
  26. A. V. Goncharsky, S. Yu. Romanov, and S. Yu. Seryozhnikov, “Problems of Limited-Data Wave Tomography,” Vychisl. Metody Programm. 15, 274-285 (2014).
  27. Numerical Analysis Library, Moscow State University.
    http://num-anal.srcc.msu.ru/lib_na/libnal.htm . Cited September 19, 2017.
  28. A. N. Tikhonov, “Solution of Incorrectly Formulated Problems and the Regularization Method,” Dokl. Akad. Nauk SSSR 151 (3), 501-504 (1963) [Sov. Math. Dokl. 5 (4), 1035-1038 (1963)].
  29. A. N. Tikhonov, A. V. Goncharsky, V. V. Stepanov, and A. G. Yagola, Numerical Methods for the Solution of Ill-Posed Problems (Springer, Dordrecht, 1995).
  30. S. Bilbao, Numerical Sound Synthesis: Finite Difference Schemes and Simulation in Musical Acoustics (Wiley, Chichester, 2009).
  31. M. A. Ilgamov and A. N. Gilmanov, Non-Reflecting Boundary Conditions for Computational Domains (Fizmatlit, Moscow, 2003) [in Russian].
  32. W. R. Hendee and E. R. Ritenour, Medical Imaging Physics (Wiley, New York, 2002).
  33. V. Sadovnichy, A. Tikhonravov, V. Voevodin, and V. Opanasenko, “’Lomonosov’: Supercomputing at Moscow State University,” in Contemporary High Performance Computing: From Petascale toward Exascale (CRC Press, Boca Raton, 2013), pp. 283-308.
  34. S. Schmidt, N. Duric, C. Li, et al., “Modification of Kirchhoff Migration with Variable Sound Speed and Attenuation for Acoustic Imaging of Media and Application to Tomographic Imaging of the Breast,” Med. Phys. 38, 998-1007 (2011).
  35. R. K. Saha and S. K. Sharma, “Validity of a Modified Born Approximation for a Pulsed Plane Wave in Acoustic Scattering Problems,” Phys. Med. Biol. 50 (2005).
    doi 10.1088/0031-9155/50/12/007
  36. B. Zeqiri, C. Baker, G. Alosa, et al., “Quantitative Ultrasonic Computed Tomography Using Phase-Insensitive Pyroelectric Detectors,” Phys. Med. Biol. 58 (2013).
    doi 10.1088/0031-9155/58/15/5237
  37. A. E. Bazulin, E. G. Bazulin, A. Kh. Vopilkin, et al., “Application of 3D Coherent Processing in Ultrasonic Testing,” Defektoskopiya 50 (2), 46-65 (2014) [Russ. J. Nondestruct. Test. 50 (2), 92-108 (2014)].
  38. E. G. Bazulin, “On the Possibility of Using the Maximum Entropy Method in Ultrasonic Nondestructive Testing for Scatterer Visualization from a Set of Echo Signals,” Akust. Zh. 59 (2), 235-254 (2013)