Skip to main content

An Image Registration Framework to Estimate 3D Myocardial Strains from Cine Cardiac MRI in Mice

  • Conference paper
  • First Online:
Functional Imaging and Modeling of the Heart (FIMH 2021)

Abstract

Accurate and efficient quantification of cardiac motion offers promising biomarkers for non-invasive diagnosis and prognosis of structural heart diseases. Cine cardiac magnetic resonance imaging remains one of the most advanced imaging tools to provide image acquisitions needed to assess and quantify in-vivo heart kinematics. The majority of cardiac motion studies are focused on human data, and there remains a need to develop and implement an image-registration pipeline to quantify full three-dimensional (3D) cardiac motion in mice where ideal image acquisition is challenged by the subject size and heart rate and the possibility of traditional tagged imaging is hampered. In this study, we used diffeomorphic image registration to estimate strains in the left ventricular wall in two wild-type mice and one diabetic mouse. Our pipeline resulted in a continuous and fully 3D strain map over one cardiac cycle. The estimation of 3D regional and transmural variations of strains is a critical step towards identifying mechanistic biomarkers for improved diagnosis and phenotyping of structural left heart diseases including heart failure with reduced or preserved ejection fraction.

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

Access this chapter

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

Similar content being viewed by others

References

  1. Abhayaratna, W.P., Marwick, T.H., Smith, W.T., Becker, N.G.: Characteristics of left ventricular diastolic dysfunction in the community: an echocardiographic survey. Heart 92(9), 1259–1264 (2006)

    Article  Google Scholar 

  2. Amzulescu, M.S., et al.: Myocardial strain imaging: review of general principles, validation, and sources of discrepancies. Eur. Heart J. Cardiovascular Imaging 20(6), 605–619 (2019)

    Article  Google Scholar 

  3. Benjamin, E.J., et al.: Heart disease and stroke statistics–2019 update: a report from the American heart association. Circulation 139(10), e56–e528 (2019)

    Article  Google Scholar 

  4. Bistoquet, A., Oshinski, J., Škrinjar, O.: Myocardial deformation recovery from cine MRI using a nearly incompressible biventricular model. Med. Image Anal. 12(1), 69–85 (2008)

    Article  Google Scholar 

  5. Coleman, D.L.: Obese and diabetes: two mutant genes causing diabetes-obesity syndromes in mice. Diabetologia 14(3), 141–148 (1978)

    Article  Google Scholar 

  6. De Craene, M., et al.: 3d strain assessment in ultrasound (straus): a synthetic comparison of five tracking methodologies. IEEE Trans. Med. Imaging 32(9), 1632–1646 (2013)

    Article  Google Scholar 

  7. Garot, J., et al.: Fast determination of regional myocardial strain fields from tagged cardiac images using harmonic phase MRI. Circulation 101(9), 981–988 (2000)

    Article  Google Scholar 

  8. Geyer, H., et al.: Assessment of myocardial mechanics using speckle tracking echocardiography: fundamentals and clinical applications. J. Am. Soc. Echocardiogr. 23(4), 351–369 (2010)

    Article  Google Scholar 

  9. Konstam, M.A., Abboud, F.M.: Ejection fraction: misunderstood and overrated (changing the paradigm in categorizing heart failure). Circulation 135(8), 717–719 (2017)

    Article  Google Scholar 

  10. Lima, J.A., et al.: Accurate systolic wall thickening by nuclear magnetic resonance imaging with tissue tagging: correlation with sonomicrometers in normal and ischemic myocardium. J. Am. Coll. Cardiol. 21(7), 1741–1751 (1993)

    Article  Google Scholar 

  11. Mansi, T., Pennec, X., Sermesant, M., Delingette, H., Ayache, N.: ilogdemons: a demons-based registration algorithm for tracking incompressible elastic biological tissues. Int. J. Comput. Vision 92(1), 92–111 (2011)

    Article  Google Scholar 

  12. Mansi, T., et al.: Physically-constrained diffeomorphic demons for the estimation of 3D myocardium strain from cine-MRI. In: Ayache, N., Delingette, H., Sermesant, M. (eds.) FIMH 2009. LNCS, vol. 5528, pp. 201–210. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01932-6_22

    Chapter  Google Scholar 

  13. Mor-Avi, V., et al.: Current and evolving echocardiographic techniques for the quantitative evaluation of cardiac mechanics: ASE/EAE consensus statement on methodology and indications endorsed by the Japanese society of echocardiography. Eur. J. Echocardiogr. 12(3), 167–205 (2011)

    Article  Google Scholar 

  14. Perk, G., Tunick, P.A., Kronzon, I.: Non-doppler two-dimensional strain imaging by echocardiography-from technical considerations to clinical applications. J. Am. Soc. Echocardiogr. 20(3), 234–243 (2007)

    Article  Google Scholar 

  15. Schuster, A., Hor, K.N., Kowallick, J.T., Beerbaum, P., Kutty, S.: Cardiovascular magnetic resonance myocardial feature tracking: concepts and clinical applications. Circulation: Cardiovascular Imaging 9(4), e004077 (2016)

    Google Scholar 

  16. 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 

  17. Thomas, D., et al.: Quantitative assessment of regional myocardial function in a rat model of myocardial infarction using tagged MRI. Magn. Reson. Mater. Phys., Biol. Med. 17(3–6), 179–187 (2004)

    Article  Google Scholar 

  18. Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: efficient non-parametric image registration. Neuroimage 45(1), S61–S72 (2009)

    Article  Google Scholar 

  19. Veress, A.I., Phatak, N., Weiss, J.A.: Deformable image registration with hyperelastic warping. In: Suri, J.S., Wilson, D.L., Laxminarayan, S. (eds.) Handbook of Biomedical Image Analysis, pp. 487–533. Springer, Boston (2005). https://doi.org/10.1007/0-306-48608-3_12

    Chapter  Google Scholar 

  20. Veress, A.I., et al.: Measuring regional changes in the diastolic deformation of the left ventricle of SHR rats using micropet technology and hyperelastic warping. Ann. Biomed. Eng. 36(7), 1104–1117 (2008)

    Article  Google Scholar 

  21. Voigt, J.U., et al.: Definitions for a common standard for 2D speckle tracking echocardiography: consensus document of the EACVI/ASE/industry task force to standardize deformation imaging. Eur. Heart J. Cardiovascular Imaging 16(1), 1–11 (2015)

    Article  MathSciNet  Google Scholar 

  22. 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. Radiology 169(1), 59–63 (1988)

    Article  Google Scholar 

  23. Zou, H., et al.: Three-dimensional biventricular strains in pulmonary arterial hypertension patients using hyperelastic warping. Comput. Methods Programs Biomed. 189, 105345 (2020)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National Institutes of Health R00HL138288 to R.A. Dr. Sadayappan has received support from National Institutes of Health grants R01 HL130356, R01 HL105826, R01 AR078001, and R01 HL143490; American Heart Association 2019 Institutional Undergraduate Student (19UFEL34380251) and transformation (19TPA34830084) awards.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Reza Avazmohammadi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Keshavarzian, M. et al. (2021). An Image Registration Framework to Estimate 3D Myocardial Strains from Cine Cardiac MRI in Mice. In: Ennis, D.B., Perotti, L.E., Wang, V.Y. (eds) Functional Imaging and Modeling of the Heart. FIMH 2021. Lecture Notes in Computer Science(), vol 12738. Springer, Cham. https://doi.org/10.1007/978-3-030-78710-3_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-78710-3_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78709-7

  • Online ISBN: 978-3-030-78710-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics