Displacement Field Estimation for Echocardiography Strain Imaging Using B-Spline Based Elastic Image Registration—Synthetic Data Study

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 519)


Strain imaging in echocardiography allows identification of myocardium dysfunctions. This paper describes the use of own implementation of elastic image registration, to calculate displacement field in two-dimensional echocardiographic data. Performance of the algorithm was examined on synthetic ultrasonic data. A series of tests examining the influence of algorithm parameters on the outcome was conducted. Displacement fields were compared with reference data from the finite element model used for generation of the synthetic data. Quality of image registration was evaluated using two error measures: mean absolute error and median absolute error. The displacement field errors obtained in the direction transverse to the ultrasound wave had an order of magnitude 10−5 m and errors in the direction along ultrasound wave: 10−6 m, which is close to the accuracy of two state-of-the-art methods for displacements estimation tested on the same input data.


Strain imaging Elastic image registration 


  1. 1.
    Gillum, R.F.: Epidemiology of heart failure in the United States. Am. Heart J. 126(4), 1042–1047 (1993)CrossRefGoogle Scholar
  2. 2.
    Folland, E.D., Parisi, A.F., Moynihan, P.F., Jones, D.R., Feldman, C.L., Tow, D.E.: Assessment of left ventricular ejection fraction and volumes by real-time, two-dimensional echocardiography. A comparison of cineangiographic and radionuclide techniques. Circulation 60(4), 760–766 (1979)CrossRefGoogle Scholar
  3. 3.
    Møller, J.E., Hillis, G.S., Oh, J.K., Reeder, G.S., Gersh, B.J., Pellikka, P.A.: Wall motion score index and ejection fraction for risk stratification after acute myocardial infarction. Am. Heart J. 151(2), 419–425 (2006)CrossRefGoogle Scholar
  4. 4.
    Heyde, B., Cygan, S., Choi, Hon Fai, Lesniak-Plewinska, B., Barbosa, D., Elen, A., Claus, P., Loeckx, D., Kaluzynski, K., D’hooge, J.: Regional cardiac motion and strain estimation in three-dimensional echocardiography: a validation study in thick-walled univentricular phantoms. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 59(4), 668–682 (2012)CrossRefGoogle Scholar
  5. 5.
    Gjesdal, O., Helle-Valle, T., Hopp, E., Lunde, K., Vartdal, T., Aakhus, S., Smith, H.J., Ihlen, H., Edvardsen, T.: Noninvasive Separation of Large, Medium, and Small Myocardial Infarcts in Survivors of Reperfused ST-Elevation Myocardial Infarction. Circ. Cardiovasc. Imaging 1(3), 189–196 (2008)CrossRefGoogle Scholar
  6. 6.
    Żmigrodzki, J., Cygan, S., Leśniak-Plewińska, B., Kałużyński, K.: Identification of subendocardial infarction—a feasibility study using synthetic ultrasonic image data of a left ventricular model. In: Computational Vision and Medical Image Processing V: Proceedings of the 5th Eccomas Thematic Conference on Computational Vision and Medical Image Processing, pp. 137–142 (2016)Google Scholar
  7. 7.
    Cygan, S., Żmigrodzki, J., Leśniak-Plewińska, B., Karny, M., Pakieła, Z., Kałużyński, K.: Influence of polivinylalcohol cryogel material model in FEM simulations on deformation of LV phantom. In: van Assen, H., Bovendeerd, P., Delhaas, T. (eds.) Functional Imaging and Modeling of the Heart, pp. 313–320. Springer International Publishing (2015)Google Scholar
  8. 8.
    Jensen, J.A.: FIELD: a program for simulating ultrasound systems. Presented at the 10th nordicbaltic conference on biomedical imaging, vol. 4, pp. 351–353 (1996)Google Scholar
  9. 9.
    Kybic, J., Unser, M.: Fast parametric elastic image registration. IEEE Trans. Image Process. 12(11), 1427–1442 (2003)CrossRefGoogle Scholar
  10. 10.
    Unser, M., Aldroubi, A., Eden, M.: Fast B-Spline transforms for continuous image representation and interpolation. IEEE Trans. Pattern Mach. Intell. 13(3), 227–285 (1991)Google Scholar
  11. 11.
    Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18(8), 712–721 (1999)CrossRefGoogle Scholar
  12. 12.
    Keys R.: Cubic convolution interpolation for digital image processing. IEEE Trans. Acoust. Speech Signal Proc. 29(6) (1981)Google Scholar
  13. 13.
    Rohlfing, T., Maurer, C.R. Jr., Bluemke, D.A., Jacobs, M.A.: Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint. IEEE Trans. Med. Imaging 22(6), 730–741 (2003) (str.)Google Scholar
  14. 14.
    Liu, Dong C., Nocedal, J.: On the limited memory BFGS method for large scale optimization. Math. Prog. 45(1), 503–528 (1989)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Metrology and Biomedical EngineeringWarsaw University of TechnologyWarsawPoland

Personalised recommendations