Skip to main content

A Surface-Volume Matching Process Using a Markov Random Field Model for Cardiac Motion Extraction in MSCT Imaging

  • Conference paper
Functional Imaging and Modeling of the Heart (FIMH 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3504))

Included in the following conference series:

Abstract

Multislice Computed Tomography (MSCT) scanners offers new perspectives for cardiac kinetics evaluation with 3D time image sequences of high contrast and spatio-temporal resolutions. A new method is proposed for cardiac motion extraction in Multislice CT. Based on a 3D surface-volume matching process, it provides the detection of the heart left cavities along the acquired sequence and the estimation of their 3D surface velocity fields. A 3D segmentation step and surface reconstruction process are first applied on only one image of the sequence to obtain a 3D mesh representation for one t time. A Markov Random Field model is defined to find best correspondences between 3D mesh nodes at t time and voxels in the next volume at t + 1 time. A simulated annealing is used to perform a global optimization of the correspondences. First results obtained on simulated and real data show the good behaviour of this method.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

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.

References

  1. Prince, J.L., McWeigh, E.R.: Motion estimation from tagged MR image sequences. IEEE Transactions on Medical Imaging 11, 238–249 (1992)

    Article  Google Scholar 

  2. Clarysse, P., Han, M., Croisille, P., Magnin, I.E.: Exploratory analysis of the spatio-temporal deformation of the myocardium during systole from tagged MRI. IEEE Transactions on Medical Imaging 149, 1328–1339 (2002)

    Google Scholar 

  3. Mulet-Parada, M., Noble, J.A.: 2D+T acoustic boundery detection in echocardiography. Medical Image Analysis 4, 21–30 (2000)

    Article  Google Scholar 

  4. Papademetris, X., Sinusas, A.J., Dione, D.P., Duncan, J.S.: Estimation of 3D left ventricular deformation from echocardiography. Medical Image Analysis 5, 17–28 (2001)

    Article  Google Scholar 

  5. Gorce, J.M., Friboulet, D., Magnin, I.E.: Estimation of three-dimensional cardiac velocity fields: assessment of a differential method. Medical Image Analysis 1, 245–261 (1997)

    Article  Google Scholar 

  6. Eusemann, C.D., Ritman, E.L., Robb, R.A.: Parametric visualization methods for the quantitative assessment of myocardial motion. Acad Radiol. 10, 66–76 (2003)

    Article  Google Scholar 

  7. Schroeder, S., Kopp, A.F., Ohnesorge, B., Floh, T., Baumbach, A., Kuettner, A., Herdeg, C., Karsch, K., Claussen, C.D.: Accuracy and reliability of quantitative measurements in coronary arteries by multi-slice computed tomography: Experimental and initial clinical results. Clinical Radiology 56, 466–474 (2001)

    Article  Google Scholar 

  8. Larralde, A., Boldak, C., Garreau, M., Toumoulin, C., Boulmier, D., Rolland, Y.: Evaluation of a 3D Segmentation Software for the Coronary Characterization in Multi-slice Computed Tomography. In: Magnin, I.E., Montagnat, J., Clarysse, P., Nenonen, J., Katila, T. (eds.) FIMH 2003. LNCS, vol. 2674, pp. 39–51. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  9. Garreau, M., Simon, A., Boulmier, D., Guillaume, H.: Cardiac Motion Extraction in Multislice Computed Tomography by using a 3D Hierarchical Surface Matching process. In: IEEE Computers in Cardiology Conference, Chicago, USA (2004)

    Google Scholar 

  10. Frangi, A.F., Niessen, W.J., Viergever, M.A.: Three-Dimensionnal Modeling for Functionnal Analysis of Cardiac Images: A Review. IEEE Transactions on Medical Imaging 20, 2–25 (2001)

    Article  Google Scholar 

  11. Bardinet, E., Cohen, L.D., Ayache, N.: Tracking and motion analysis of the left ventricle with deformable superquadrics. Medical Image Analysis 1, 129–149 (1996)

    Article  Google Scholar 

  12. Chen, C.W., Huang, T.S., Arrott, M.: Modeling, analysis and visualization of left ventricle shape and motion by hierarchical decomposition. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 342–356 (1994)

    Article  Google Scholar 

  13. Choi, S.-M., Kim, M.-H.: Motion visualization of human left ventricle with a time-varying deformable model for cardiac diagnosis. The journal of visualization and computer animation 12, 55–66 (2001)

    Article  MATH  Google Scholar 

  14. Benayoun, S., Ayache, N.: Dense non-rigid motion estimation in sequences of medical images using differential constraints. Int. Journal of Computer Vision 26, 25–40 (1998)

    Article  Google Scholar 

  15. Song, S.M., Leahy, R.M.: Computation of 3-D velocity fields from 3-D cine ct images of the human heart. IEEE Transactions on Medical Imaging 10, 295–306 (1991)

    Article  Google Scholar 

  16. Amini, A.A., Duncan, J.S.: Bending and stretching models for LV wall motion analysis from curves and surfaces. Image and Vision Computing 10, 418–430 (1992)

    Article  Google Scholar 

  17. Kambhamettu, C., Goldgof, D., He, M., Laskov, P.: 3D nonrigid motion analysis under small deformations. Image and Vision Computing 21, 229–245 (2003)

    Article  Google Scholar 

  18. Shi, P., Sinusas, A.J., Constable, R.T., Ritman, E., Duncan, J.S.: Point-tracked quantitative analysis of left ventricular motion from 3D image sequences. IEEE Transactions on Medical Imaging 19, 36–50 (2000)

    Article  Google Scholar 

  19. Heitz, F., Bouthemy, P.: Multimodal Estimation of Discontinuous Optical Flow Using Markov Random Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence 15, 1217–1232 (1993)

    Article  Google Scholar 

  20. Lim, K.P., Das, A., Chong, M.N.: Estimation of Occlusion and Dense Motion Fields in a Bidirectional Bayesian Framework. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 712–718 (2002)

    Article  Google Scholar 

  21. Guillaume, H., Garreau, M.: Segmentation de cavités cardiaques en imagerie scanner multi-barettes. Forum des Jeunes Chercheurs en Génie Biologique et Médical (2003)

    Google Scholar 

  22. Besag, J.: Spatial interaction and the Statistical Analysis of Lattice Systems. Journal of the Royal Statistical Society 36, 192–236 (1974)

    MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Simon, A., Garreau, M., Boulmier, D., Coatrieux, JL., Le Breton, H. (2005). A Surface-Volume Matching Process Using a Markov Random Field Model for Cardiac Motion Extraction in MSCT Imaging. In: Frangi, A.F., Radeva, P.I., Santos, A., Hernandez, M. (eds) Functional Imaging and Modeling of the Heart. FIMH 2005. Lecture Notes in Computer Science, vol 3504. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494621_45

Download citation

  • DOI: https://doi.org/10.1007/11494621_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26161-2

  • Online ISBN: 978-3-540-32081-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics