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Restoration of Phase-Contrast Cardiovascular MRI for the Construction of Cardiac Contractility Atlases

  • Christina KoutsoumpaEmail author
  • Robin Simpson
  • Jennifer Keegan
  • David Firmin
  • Guang-Zhong Yang
Conference paper
  • 1.9k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8896)

Abstract

Cardiac Atlases are promising tools for the interpretation of functional and anatomical structures of the heart. Myocardial viability is reflected by both global and regional contractile abnormalities. Atlases incorporating contractility information of a population can assist the diagnosis of myocardial disease and myocardial infarction. For the analysis of myocardial contractility phase-contrast MRI (PC-MRI) is emerging as a valuable clinical tool. The myocardial velocity distribution depicted by PC-MRI provides important insights into the intrinsic mechanics of the heart. As with many imaging techniques, there is an inherent trade-off between imaging resolution and noise. The main purpose of this study is to reduce the noise exhibited in phase-contrast MRI by applying a total variation restoration algorithm. The restoration algorithm has been evaluated on a spiral phase-contrast MRI sequence from a group of normal subjects. The results have shown that the proposed method is able to restore the myocardial velocity distribution whilst preserving the fidelity of the underlying contractile behavior.

Keywords

Restoration Total Variation Contractor Atlas Cardiac Atlas Phase Contrast MRI Myocardial Contractility 

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References

  1. 1.
    Gault, J.H., Ross, J., Braunwald, E.: Contractile state of the left ventricle in man: instantaneous tension-velocity-length relations in patients with and without disease of the left ventricular myocardium. Circ. Res. 22(4), 451–463 (1968)CrossRefGoogle Scholar
  2. 2.
    Young, A.A., Frangi, A.F.: Computational cardiac atlases: from patient to population and back. Exp. Physiol. 94(5), 578–596 (2009)CrossRefGoogle Scholar
  3. 3.
    Ordas, S., Oubel, S., Sebastian, R., Frangi, A.F.: Computational anatomy atlas of the heart. In: Proceedings of the 5th ISPA 2007, vol. 8, pp. 338–342 (2007)Google Scholar
  4. 4.
    Fonseca, C.G., Backhaus, M., Bluemke, D.A., Britten, R.D., Do Chung, J., Cowan, B.R., et al.: The Cardiac Atlas Project–an imaging database for computational modeling and statistical atlases of the heart. Bioinformatics 27(16), 2288–2295 (2011)CrossRefGoogle Scholar
  5. 5.
    Hoogendoorn, C., Duchateau, N., Sánchez-Quintana, D., Whitmarsh, T., Sukno, F.M., De Craene, M., Lekadir, K., Frangi, A.F.: A high-resolution atlas and statistical model of the human heart from multislice CT. IEEE Trans. Med. Imaging 32(1), 28–44 (2013)CrossRefGoogle Scholar
  6. 6.
    Chandrashekara, R., Rao, A., Sanchez-Ortiz, G.I., Mohiaddin, R.H., Rueckert, D.: Construction of a statistical model for cardiac motion analysis using nonrigid image registration. Inf. Process. Med. Imaging 18, 599–610 (2003)CrossRefGoogle Scholar
  7. 7.
    Duchateau, N., De Craene, M., Piella, G., Silva, E., Doltra, A., Sitges, M., et al.: A spatiotemporal statistical atlas of motion for the quantification of abnormal myocardial tissue velocities. Med. Image Anal. 15(3), 316–328 (2011)CrossRefGoogle Scholar
  8. 8.
    Rougon, N.F., Petitjean, C., Prêteux, F.J.: Building a 4D atlas of the cardiac anatomy and motion using MR imaging. SPIE Medical Imaging 2, 253–264 (2004)Google Scholar
  9. 9.
    Simpson, R.M., Keegan, J., Firmin, D.N.: MR assessment of regional myocardial mechanics. J. Magn. Reson. Imaging 37(3), 576–599 (2013)CrossRefGoogle Scholar
  10. 10.
    Masood, S., Gao, J., Yang, G.-Z.: Virtual tagging: numerical considerations and phantom validation. IEEE Trans. Med. Imaging 21(9), 1123–1131 (2002)CrossRefGoogle Scholar
  11. 11.
    Wünsche, B., Young, A.A.: The visualization and measurement of left ventricular deformation using finite element models. J. Vis. Lang. Comput. 14(4), 299–326 (2003)CrossRefGoogle Scholar
  12. 12.
    Codreanu, I., Robson, M.D., Golding, S.J., Jung, B.A., Clarke, K., Holloway, C.J.: Longitudinally and circumferentially directed movements of the left ventricle studied by cardiovascular magnetic resonance phase contrast velocity mapping. J. Cardiovasc. Magn. Reson. 12, 48 (2010)CrossRefGoogle Scholar
  13. 13.
    Torrent-Guasp, F., Ballester, M., Buckberg, G.D., Carreras, F., Flotats, A., Carrió, I., et al.: Spatial orientation of the ventricular muscle band: physiologic contribution and surgical implications. J. Thorac. Cardiovasc. Surg. 122(2), 389–392 (2001)CrossRefGoogle Scholar
  14. 14.
    Sengupta, P.P., Korinek, J., Belohlavek, M., Narula, J., Vannan, M.A., Jahangir, A., Khandheria, B.K.: Left ventricular structure and function: basic science for cardiac imaging. J. Am. Coll. Cardiol. 48(10), 1988–2001 (2006)CrossRefGoogle Scholar
  15. 15.
    Veeraraghavan, R., Gourdie, R.G., Poelzing, S.: Mechanisms of cardiac conduction: a history of revisions. Am. J. Physiol. Heart Circ. Physiol. 306(5), H619–H627 (2014)CrossRefGoogle Scholar
  16. 16.
    Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D Nonlinear Phenom. 60, 259–268 (1992)CrossRefzbMATHGoogle Scholar
  17. 17.
    Blomgren, P., Chan, T.F.: Color TV: total variation methods for restoration of vector-valued images. IEEE Trans. Image Process. 7(3), 304–309 (1998)CrossRefGoogle Scholar
  18. 18.
    Shen, J., Chan, T.: Variational restoration of nonflat image features: Models and algorithms. SIAM J. Appl. Math. 61(4), 1338–1361 (2001)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Chan, T.F., Kang, S.H., Shen, J.: Total Variation Denoising and Enhancement of Color Images Based on the CB and HSV Color Models. J. Vis. Commun. Image Represent. 12(4), 422–435 (2001)CrossRefGoogle Scholar
  20. 20.
    Chan, T.F., Osher, S., Shen, J.: The digital TV filter and nonlinear denoising. IEEE Trans. Image Process. 10(2), 231–241 (2001)CrossRefzbMATHGoogle Scholar
  21. 21.
    Ng, Y., Yang, G.: Vector-valued image restoration with applications to magnetic resonance velocity imaging. In: WSCG, vol. 37(2), pp. 58–61 (February 2003) Google Scholar
  22. 22.
    Huntbatch, A., Lee, S.-L., Firmin, D., Yang, G.-Z.: Bayesian motion recovery framework for myocardial phase-contrast velocity MRI. Med. Image Comput. Comput. Assist. Interv. 11(Pt 2), 79–86 (2008)Google Scholar
  23. 23.
    Simpson, R., Keegan, J., Gatehouse, P., Hansen, M., Firmin, D.: Spiral tissue phase velocity mapping in a breath-hold with non-cartesian SENSE. Magn. Reson. Med. (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Christina Koutsoumpa
    • 1
    Email author
  • Robin Simpson
    • 2
  • Jennifer Keegan
    • 3
  • David Firmin
    • 3
  • Guang-Zhong Yang
    • 1
  1. 1.The Hamlyn Centre for Robotic SurgeryImperial College LondonLondonUK
  2. 2.Radiological PhysicsUniversity of FreiburgFreiburgGermany
  3. 3.Cardiovascular Biomedical Research UnitRoyal Brompton HospitalLondonUK

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