Skip to main content
Springer Nature Link
Log in

Strain Rate Tensor Estimation in Cine Cardiac MRI Based on Elastic Image Registration

  • Chapter
Tensors in Image Processing and Computer Vision

Part of the book series: Advances in Pattern Recognition ((ACVPR))

  • 2856 Accesses

Abstract

In this work we propose an alternative method to estimate and visualize the Strain Rate Tensor (SRT) in Magnetic Resonance Images (MRI) when Phase Contrast MRI (PCMRI) and Tagged MRI (TMRI) are not available. This alternative is based on image processing techniques. Concretely, image registration algorithms are used to estimate the movement of the myocardium at each point. Additionally, a consistency checking method is presented to validate the accuracy of the estimates when no golden standard is available. Results prove that the consistency checking method provides an upper bound of the mean squared error of the estimate. Our experiments with real data show that the registration algorithm provides a useful deformation field to estimate the SRT fields. A classification between regional normal and dysfunctional contraction patterns, as compared with experts diagnosis, points out that the parameters extracted from the estimated SRT can represent these patterns. Additionally, a scheme for visualizing and analyzing the local behavior of the SRT field is presented.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Aja-Fernández, S., Alberola-López, C., Westin, C.F.: Noise and signal estimation in magnitude MRI and Rician distributed images: A LMMSE approach. IEEE Tran. Image Proc. 17(8), 1383–1398 (2008)

    Article  Google Scholar 

  2. Aja-Fernández, S., San-José-Estépar, R., Alberola-López, C., Westin, C.F.: Image quality assessment based on local variance. In: Proc. IEEE EMBS, pp. 4815–4818. New York, NY, USA (2006)

    Google Scholar 

  3. Amini, A.A., Chen, Y., Elayyadi, M.: Tag surface reconstruction and tracking of myocardial beads from SPAMM-MRI with parametric B-spline surfaces. IEEE Tran. Med. Imaging 20, 94–103 (2001)

    Article  Google Scholar 

  4. Axel, L., A., M., Kim, D.: Tagged magnetic resonance imaging of the heart: a survey. Med. Image Anal. 9, 376–393 (2005)

    Article  Google Scholar 

  5. Axel, L., Dougherty, L.: MR imaging of motion with spatial modulation of magnetization. Radiol. 171, 841–845 (1989)

    Google Scholar 

  6. Axel, L., Morton, D.: Correction of phase wrapping in magnetic resonance imaging. Med. Phys. 16, 284–287 (1989)

    Article  Google Scholar 

  7. Cachier, P., Pennec, X.: 3D non-rigid registration by gradient descent on a Gaussian-windowed similarity measure using convolutions. In: Proc. MMBIA, pp. 182–189. Hilton Head Island, SC, USA (2000)

    Google Scholar 

  8. Chandrashekara, R., Mohiaddin, R.H., Rueckert, D.: Analysis of 3-d myocardial motion in tagged mr images using nonrigid image registration. IEEE Tran. Med. Imaging 23, 1245–1250 (2004)

    Article  Google Scholar 

  9. Chandrashekara, R., Mohiaddin, R.H., Rueckert, D.: Cardiac motion tracking in tagged MR images using a 4D B-spline motion model and nonrigid image registration. In: Proc. IEEE Biomed. Imaging: Nano to Macro, pp. 468–471. Arlington, VA, USA (2004)

    Google Scholar 

  10. Chandrashekara, R., Mohiaddin, R.H., Rueckert, D.: Comparison of cardiac motion fields from tagged and untagged MR images using nonrigid registration. In: 3rd Int. Workshop FIMH, LNCS, vol. 3504, pp. 425–433. Barcelona, Spain (2005)

    Google Scholar 

  11. Chavez, S., Xiang, Q.S., An, L.: Understanding phase maps in MRI: A new cutline phase unwrapping method. IEEE Tran. Med. Imaging 21, 966–977 (2002)

    Article  Google Scholar 

  12. Chen, Y., Amini, A.A.: A map framework for tag line detection in spamm data using markov random fields on the b-spline solid. IEEE Tran. Med. Imaging 21, 1110–1122 (2002)

    Article  Google Scholar 

  13. Fischer, S.E., McKinnon, G.C., Maier, S.E., Boesiger, P.: Improved myocardial tagging contrast. Magn. Reson. Med. 30, 191–200 (1993)

    Article  Google Scholar 

  14. Gatehouse, P.D., Keegan, J., Crowe, L.A., Masood, S., Mohiaddin, R.H., Kreitner, K.F., Firmin, D.N.: Applications of phase-contrast flow and velocity imaging in cardiovascular MRI. Eur. Radiol. 15, 2172–2184 (2005)

    Article  Google Scholar 

  15. Guttman, M.A., Zerhouni, E.A., McVeigh, E.R.: Analysis of cardiac function from MR images. IEEE Comput. Graphics Appl. 17(1), 30–38 (1997)

    Article  Google Scholar 

  16. Haykin, S.: Neural Networks: A Comprehensive Foundation (2nd Edition). Prentice Hall (1998)

    Google Scholar 

  17. Jehenson, P., Westphal, M., Schuff, N.: Analytical method for compensation of eddy-current effects induced by pulsed magnetic field gradients. Med. Phys. 16, 284–287 (1989)

    Article  Google Scholar 

  18. Ledesma-Carbayo, M.J., Bajo, A., Santa-Marta, C., Perez-David, E., Garcia-Fernandez, M.A., M. Desco, A.S.: Fully automatic cardiac motion estimation from tagged MRI using non-rigid registration techniques. Com. Card. 33, 305–308 (2006)

    Google Scholar 

  19. Lee, S., Wolberg, G., Shin, S.: Scattered data interpolation with multilevel B-splines. IEEE Tran. Vis. Comput. Graphics 3(3), 228–244 (1997)

    Article  Google Scholar 

  20. Moore, C.C., Lugo-Olivieri, C.H., McVeigh, E.R., Zerhouni, E.A.: Three dimensional systolic strain patterns in the normal human left ventricle: characterization with tagged mr imaging. Radiol. 214, 453–466 (2000)

    Google Scholar 

  21. Mosher, T.J., Smith, M.B.: A DANTE tagging sequence for the evaluation of translational sample motion. Magn. Reson. Med. 15, 334–339 (1990)

    Article  Google Scholar 

  22. Netsch, T., van Muiswinkel, A.: Quantitative evaluation of image-based distortion correction in diffusion tensor imaging. IEEE Tran. Med. Imaging 23(7), 789–798 (2004)

    Article  Google Scholar 

  23. Osman, N.F., Kerwin, W.S., McVeigh, E.R., Prince, J.L.: Cardiac motion tracking using cine harmonic phase (HARP) magnetic resonance imaging. Magn. Reson. Med. 42, 1048–1060 (1999)

    Article  Google Scholar 

  24. Ozturk, C., Derbyshire, J., McVeigh, E.: Estimating motion from MRI data. Proc. IEEE 91, 1627–1648 (2003)

    Article  Google Scholar 

  25. Pelc, N.J., Sommer, F.G., Li, K.C., Brosnan, T.J., Herfkens, R.J., Enzmann, D.R.: Quantitative magnetic resonance flow imaging. Magn. Reson. Q. 10, 125–147 (1994)

    Google Scholar 

  26. Rogers Jr., W.J., Shapiro, E.P., Weiss, J.L., Buchalter, M.B., Rademakers, F.E., Weisfeldt, M.L., Zerhouni, E.A.: Quantification of and correction for left ventricular systolic long-axis shortening by magnetic resonance tissue tagging and slice isolation. Circul. 84, 721–731 (1991)

    Google Scholar 

  27. Ryf, S., Spiegel, M.A., Gerber, M., Boesiger, P.: Myocardial tagging with 3d-cspamm. J. Magn. Reson. Imaging 16, 320–325 (2002)

    Article  Google Scholar 

  28. Schiller, N.B., Shah, P.M., Crawford, M., DeMaria, A., Devereux, R., Feigenbaum, H., Gutgesell, H., Reichek, N., Sahn, D., Schnittger, I.: Recommendations for quantitation of the left ventricle by two-dimensional echocardiography. J. Am. Soc. Echocardiogr. 2, 358–367 (1989)

    Google Scholar 

  29. Selskog, P., Heiberg, E., Ebbers, T., Wigström, L., Karlsson, M.: Kinematics of the heart: strain-rate imaging from time-resolved tree-dimensional phase contrast MRI. IEEE Tran. Med. Imaging 21(9), 1105–1109 (2002)

    Article  Google Scholar 

  30. Thirion, J.P.: Image matching as a diffusion process: an analogy with maxwell’s demons. Med. Image Anal. 2(3), 243–260 (1998)

    Article  Google Scholar 

  31. Thunberg, P.: Accuracy and reproducibility in phase contrast magnetic resonance imaging. Ph.D. thesis, Linköping University, Linköping, Sweden (2004). Dissertation No. 899, ISBN 91-85295-41-8

    Google Scholar 

  32. Tristán, A., Arribas, J.I.: A fast B-spline pseudo-inversion algorithm for consistent image registration. In: Comput. Anal. Images Patterns, Lecture Notes in Computer Science, vol. 4673, pp. 768–775 (2007)

    Google Scholar 

  33. Tristán-Vega, A., Vegas-Sánchez-Ferrero, G., Aja-Fernández, S.: Local similarity measures for demons-like registration algorithms. In: IEEE Int. Symp. Biomed. Imaging. Nano to Macro., pp. 1087–1090. Paris, France (2008)

    Google Scholar 

  34. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: form error visibility to structural similarity. IEEE Tran. Image Proc. 13(4), 600–612 (2004)

    Article  Google Scholar 

  35. Wuensche, B., Lobb, R.: A toolkit for the visualization of stress and strain tensor fields in biological tissue. In: Proc. VIP, pp. 6–15. Sydney, Australia (1999)

    Google Scholar 

  36. Xiang, Q.S.: Temporal phase unwrapping for CINE velocity imaging. Magn. Reson. Imaging 5, 529–534 (1995)

    Article  Google Scholar 

  37. Xiao, G., Brady, J., Noble, J., Burcher, M., English, R.: Nonrigid registration of 3-D free-hand ultrasound images of the breast. IEEE Tran. Med. Imaging 21, 405–412 (2002)

    Article  Google Scholar 

  38. Young, A.A., Axel, L.: Three-dimensional motion and deformation of the heart wall: estimation with spatial modulation of magnetization –a model-based approach. Radiol. 185, 241–247 (1992)

    Google Scholar 

  39. Zerhouni, E.A., Parish, D.M., Rogers, W.J., Yang, A., Shapiro, E.P.: Human heart: tagging with MR imaging–a method for noninvasive assessment of myocardial motion. Radiol. 169, 59–63 (1988)

    Google Scholar 

  40. Zitová, B., Flusser, J.: Image registration methods: a survey. Im. Vis. Comp. 21, 977–1000 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LPI, Universidad de Valladolid, Italy

    Gonzalo Vegas Sánchez-Ferrero, Antonio Tristán Vega, Lucilio Cordero Grande, Pablo Casaseca de la Higuera, Santiago Aja Fernández, Marcos Martín Fernández & Carlos Alberola López

Authors
  1. Gonzalo Vegas Sánchez-Ferrero
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Antonio Tristán Vega
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lucilio Cordero Grande
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pablo Casaseca de la Higuera
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Santiago Aja Fernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Marcos Martín Fernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Carlos Alberola López
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gonzalo Vegas Sánchez-Ferrero .

Editor information

Editors and Affiliations

  1. Escuela Técnica Superior de Ingenieros de Telecomunicacíon, Universidad de Valladolid, Campus Miguel Delibes, 47011 Valladolid, Spain

    Santiago Aja-Fernández  & Rodrigo de Luis García  & 

  2. Dept. Computing, HongKong Polytechnic University, Hong Kong

    Dacheng Tao

  3. University of London Birkbeck College School of Computer Science & Information Systems, Malet Street, London, WC1E 7HX, UK

    Xuelong Li

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag London Limited

About this chapter

Cite this chapter

Sánchez-Ferrero, G.V. et al. (2009). Strain Rate Tensor Estimation in Cine Cardiac MRI Based on Elastic Image Registration. In: Aja-Fernández, S., de Luis García, R., Tao, D., Li, X. (eds) Tensors in Image Processing and Computer Vision. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84882-299-3_17

Download citation

  • DOI: https://doi.org/10.1007/978-1-84882-299-3_17

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84882-298-6

  • Online ISBN: 978-1-84882-299-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics