Advertisement

Integrated Dynamic Shape Tracking and RF Speckle Tracking for Cardiac Motion Analysis

  • Nripesh ParajuliEmail author
  • Allen Lu
  • John C. Stendahl
  • Maria Zontak
  • Nabil Boutagy
  • Melissa Eberle
  • Imran Alkhalil
  • Matthew O’Donnell
  • Albert J. Sinusas
  • James S. Duncan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9900)

Abstract

We present a novel dynamic shape tracking (DST) method that solves for Lagrangian motion trajectories originating at the left ventricle (LV) boundary surfaces using a graphical structure and Dijkstra’s shortest path algorithm.

These trajectories, which are temporally regularized and accrue minimal drift, are augmented with radio-frequency (RF) speckle tracking based mid-wall displacements and dense myocardial deformation fields and strains are calculated.

We used this method on 4D Echocardiography (4DE) images acquired from 7 canine subjects and validated the strains using a cuboidal array of 16 sonomicrometric crystals that were implanted on the LV wall. The 4DE based strains correlated well with the crystal based strains. We also created an ischemia on the LV wall and evaluated how strain values change across ischemic, non-ischemic remote and border regions (with the crystals planted accordingly) during baseline, severe occlusion and severe occlusion with dobutamine stress conditions. We were able to observe some interesting strain patterns for the different physiological conditions, which were in good agreement with the crystal based strains.

Keywords

Shape Descriptor Speckle Tracking Left Ventricle Wall Left Anterior Descend Occlusion Severe Occlusion 
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

Acknowledgment

Several members of Dr. Albert Sinusas’s lab, including Christi Hawley and James Bennett, were involved in the image acquisitions. Dr. Xiaojie Huang provided code for image segmentation. We would like to sincerely thank everyone for their contributions. This work was supported in part by the National Institute of Health (NIH) grant number 5R01HL121226.

References

  1. 1.
    Craene, M., Piella, G., Camara, O., Duchateau, N., Silva, E., Doltra, A., Dhooge, J., Brugada, J., Sitges, M., Frangi, A.F.: Temporal diffeomorphic free-form deformation: application to motion and strain estimation from 3D echocardiography. Med. Image Anal. 16(2), 427–450 (2012)CrossRefGoogle Scholar
  2. 2.
    Ledesma-Carbayo, M.J., Kybic, J., Desco, M., Santos, A., Sühling, M., Hunziker, P., Unser, M.: Spatio-temporal nonrigid registration for ultrasound cardiac motion estimation. IEEE Trans. Med. Imaging 24(9), 1113–1126 (2005)CrossRefGoogle Scholar
  3. 3.
    Compas, C.B., Wong, E.Y., Huang, X., Sampath, S., Lin, B.A., Pal, P., Papademetris, X., Thiele, K., Dione, D.P., Stacy, M., et al.: Radial basis functions for combining shape and speckle tracking in 4D echocardiography. IEEE Trans. Med. Imaging 33(6), 1275–1289 (2014)CrossRefGoogle Scholar
  4. 4.
    Parajuli, N., Compas, C.B., Lin, B.A., Sampath, S., ODonnell, M., Sinusas, A.J., Duncan, J.S.: Sparsity and biomechanics inspired integration of shape and speckle tracking for cardiac deformation analysis. In: van Assen, H., Bovendeerd, P., Delhaas, T. (eds.) FIMH 2015. LNCS, vol. 9126, pp. 57–64. Springer, Heidelberg (2015)Google Scholar
  5. 5.
    Huang, X., Dione, D.P., Compas, C.B., Papademetris, X., Lin, B.A., Bregasi, A., Sinusas, A.J., Staib, L.H., Duncan, J.S.: Contour tracking in echocardiographic sequences via sparse representation and dictionary learning. Med. Image Anal. 18(2), 253–271 (2014)CrossRefGoogle Scholar
  6. 6.
    Belongie, S., Malik, J., Puzicha, J.: Shape context: a new descriptor for shape matching and object recognition. In: Leen, T.K., Dietterich, T.G., Tresp, V. (eds.) Advances in Neural Information Processing Systems, pp. 831–837. MIT Press, Cambridge (2001)Google Scholar
  7. 7.
    Shafique, K., Shah, M.: A noniterative greedy algorithm for multiframe point correspondence. IEEE Trans. Pattern Anal. Mach. Intell. 27(1), 51–65 (2005)CrossRefGoogle Scholar
  8. 8.
    Chen, X., Xie, H., Erkamp, R., Kim, K., Jia, C., Rubin, J., O’Donnell, M.: 3-D correlation-based speckle tracking. Ultrason. Imaging 27(1), 21–36 (2005)CrossRefGoogle Scholar
  9. 9.
    Dione, D., Shi, P., Smith, W., DeMan, P., Soares, J., Duncan, J., Sinusas, A.: Three-dimensional regional left ventricular deformation from digital sonomicrometry. In: Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 2, pp. 848–851. IEEE (1997)Google Scholar
  10. 10.
    Waldman, L.K., Fung, Y., Covell, J.W.: Transmural myocardial deformation in the canine left ventricle. Normal in vivo three-dimensional finite strains. Circ. Res. 57(1), 152–163 (1985)CrossRefGoogle Scholar
  11. 11.
    Lin, N., Duncan, J.S.: Generalized robust point matching using an extended free-form deformation model: application to cardiac images. In: 2004 IEEE International Symposium on Biomedical Imaging: Nano to Macro, pp. 320–323. IEEE (2004)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Nripesh Parajuli
    • 1
    Email author
  • Allen Lu
    • 2
  • John C. Stendahl
    • 3
  • Maria Zontak
    • 4
  • Nabil Boutagy
    • 3
  • Melissa Eberle
    • 3
  • Imran Alkhalil
    • 3
  • Matthew O’Donnell
    • 4
  • Albert J. Sinusas
    • 3
    • 5
  • James S. Duncan
    • 1
    • 2
    • 5
  1. 1.Departments of Electrical EngineeringYale UniversityNew HavenUSA
  2. 2.Biomedical EngineeringYale UniversityNew HavenUSA
  3. 3.Internal MedicineYale UniversityNew HavenUSA
  4. 4.Department of BioengineeringUniversity of WashingtonSeattleUSA
  5. 5.Radiology and Biomedical ImagingYale UniversityNew HavenUSA

Personalised recommendations