Advertisement

Towards Left Ventricular Scar Localisation Using Local Motion Descriptors

  • Devis Peressutti
  • Wenjia Bai
  • Wenzhe Shi
  • Catalina Tobon-Gomez
  • Thomas Jackson
  • Manav Sohal
  • Aldo Rinaldi
  • Daniel Rueckert
  • Andrew King
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9534)

Abstract

We propose a novel technique for the localisation of Left Ventricular (LV) scar based on local motion descriptors. Cardiac MR imaging is employed to construct a spatio-temporal motion atlas where the LV motion of different subjects can be directly compared. Local motion descriptors are derived from the motion atlas and dictionary learning is used for scar classification. Preliminary results on a cohort of 20 patients show a sensitivity and specificity of \(80\,\%\) and \(87\,\%\) in a binary classification setting.

Keywords

Cardiac Resynchronisation Therapy Sparse Code Dictionary Learning Orthogonal Match Pursuit Statistical Shape Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was funded by EPSRC Grants EP/K030310/1 and EP/K030523/1. We acknowledge financial support from the Department of Health via the NIHR comprehensive Biomedical Research Centre award to Guy’s & St Thomas’ NHS Foundation Trust with KCL and King’s College Hospital NHS Foundation Trust.

References

  1. 1.
    Bilchick, K.C., Kuruvilla, S., Hamirani, Y.S., Ramachandran, R., Clarke, S.A., Parker, K.M., Stukenborg, G.J., Mason, P., Ferguson, J.D., Moorman, J.R., Malhotra, R., Mangrum, J.M., Darby, A.E., DiMarco, J., Holmes, J.W., Salerno, M., Kramer, C.M., Epstein, F.H.: Impact of mechanical activation, scar, and electrical timing on cardiac resynchronization therapy response and clinical outcomes. J. Am. Coll. Cardiol. 63(16), 1657–1666 (2014)CrossRefGoogle Scholar
  2. 2.
    Cerqueira, M.D., Weissman, N.J., Dilsizian, V., Jacobs, A.K., Kaul, S., Laskey, W.K., Pennell, D.J., Rumberger, J.A., Ryan, T., Verani, M.S.: Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. Circulation 105(4), 539–542 (2002)CrossRefGoogle Scholar
  3. 3.
    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. In: Taylor, C.J., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 599–610. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    De Craene, M., Duchateau, N., Tobon-Gomez, C., Ghafaryasl, B., Piella, G., Rhode, K.S., Frange, A.: SPM to the heart: Mapping of 4D continuous velocities for motion abnormality quantification. In: Proc. of IEEE ISBI, pp. 454–457 (2012)Google Scholar
  5. 5.
    Guha, T., Ward, R.: Learning sparse representations for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 34(8), 1576–1588 (2012)CrossRefGoogle Scholar
  6. 6.
    Hoogendoorn, C., Duchateau, N., Sanchez-Quintana, D., Whitmarsh, T., Sukno, F., De Craene, M., Lekadir, K., Frangi, A.: 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
  7. 7.
    Maret, E., Todt, T., Brudin, L., Nylander, E., Swahn, E., Ohlsson, J., Engvall, J.: Functional measurements based on feature tracking of cine magnetic resonance images identify left ventricular segments with myocardial scar. Cardiovasc. Ultrasound 7(1), 53 (2009)CrossRefGoogle Scholar
  8. 8.
    Medrano-Gracia, P., Suinesiaputra, A., Cowan, B., Bluemke, D., Frangi, A., Lee, D., Lima, J., Young, A.: An atlas for cardiac MRI regional wall motion and infarct scoring. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2012. LNCS, vol. 7746, pp. 188–197. Springer, Heidelberg (2013)Google Scholar
  9. 9.
    Rao, A., Chandrashekara, R., Sanchez-Ortiz, G., Mohiaddin, R., Aljabar, P., Hajnal, J., Puri, B.K., Rueckert, D.: Spatial transformation of motion and deformation fields using nonrigid registration. IEEE Trans. Med. Imaging 23(9), 1065–1076 (2004)CrossRefGoogle Scholar
  10. 10.
    Shan, K., Constantine, G., Sivananthan, M., Flamm, S.D.: Role of cardiac magnetic resonance imaging in the assessment of myocardial viability. Circulation 109(11), 1328–1334 (2004)CrossRefGoogle Scholar
  11. 11.
    Shi, W., Zhuang, X., Wang, H., Duckett, S., Luong, D., Tobon-Gomez, C., Tung, K., Edwards, P., Rhode, K., Razavi, R., Ourselin, S., Rueckert, D.: A comprehensive cardiac motion estimation framework using both untagged and 3-D tagged MR images based on nonrigid registration. IEEE Trans. Med. Imaging 31(6), 1263–1275 (2012)CrossRefGoogle Scholar
  12. 12.
    Shi, W., Jantsch, M., Aljabar, P., Pizarro, L., Bai, W., Wang, H., O’Regan, D., Zhuang, X., Rueckert, D.: Temporal sparse free-form deformations. Med. Image Anal. 17(7), 779–789 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Devis Peressutti
    • 1
  • Wenjia Bai
    • 2
  • Wenzhe Shi
    • 2
  • Catalina Tobon-Gomez
    • 1
  • Thomas Jackson
    • 1
  • Manav Sohal
    • 1
  • Aldo Rinaldi
    • 1
  • Daniel Rueckert
    • 2
  • Andrew King
    • 1
  1. 1.Division of Imaging Sciences and Biomedical EngineeringKing’s College LondonLondonUK
  2. 2.Biomedical Image Analysis GroupImperial College LondonLondonUK

Personalised recommendations