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Oral presentation at STACOM10 (Invited talk)

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M. De Craene, F.M. Sukno, C. Tobon-Gomez, C. Butakoff, R.M. Figueras i Ventura, C. Hoogendoorn, G. Piella, N. Duchateau, E. Muñoz-Moreno, R. Sebastián, O. Camara, and A.F. Frangi. Atlas construction and image analysis using statistical cardiac models. In Statistical Atlases and Computational Models of the Heart (STACOM). MICCAI Workshop., 2010.

http://www.dtic.upf.edu/~mde/pdf/stacom10/DeCraeneStacom10.pdf

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Oral presentation at STACOM10 (Invited talk)

  1. 1. !Atlas construction and image analysis using statistical cardiac modelsM. De Craene, F.M. Sukno, C. Tobon-Gomez, C. Butakoff, R.M. Figueras i Ventura, C.Hoogendoorn, G. Piella, N. Duchateau, E. Muñoz-Moreno, R. Sebastián, O. Camara, and A.F. Frangi Center for Computational Imaging & Simulation Technologies in Biomedicine Universitat Pompeu Fabra, Barcelona, Spain Networking Center on Biomedical Research – Bioengineering, Biomaterials and Nanomedicine mathieu.decraene@upf.edu www.cilab.upf.edu
  2. 2. 1WHY DO WE NEED ATLASESOF THE HEART? 2
  3. 3. Why do we need atlases of the heart?1.Integrated image-basedbiomarkersLooking at multiple levels Global shape Local shape Motion / Deformation 3
  4. 4. Why do we need atlases of the heart?1.Integrated image-basedbiomarkers patient d1 d2 d1 <?> d2Probabilistic biomarkers atlas Encode normality P-value of abnormality Duchateau et al, STACOM (2010) 4
  5. 5. Why do we need atlases the heart?2.Integrated multimodal information forpatient-specific modeling 5
  6. 6. 2 EVOLUTIONS AND CHALLENGES IN HEART ATLAS CONSTRUCTION 6
  7. 7. From single subject to population atlases … … Affine + nonrigid diffeomorphic registration Average non rigid Reference transformation Apply average non rigid Apply inverse transform transforms Average up to affine transform: The atlas image Segment TriangulateOrdas et al . Proc. SPIE Medical Imaging (2007)
  8. 8. From monomodal to multimodal atlases POINT DISTRIBUTION STATISTICAL INTENSITY MODEL TO IMAGE MODEL MODEL ADAPTATION/MATCHING Create automatically by image simulationTobon- Gomez et al. IEEE Trans onMedical Imaging, 27(11):1655-1667(2008) 8
  9. 9. From spatial to spatiotemporal atlases ! Two parameterizations ! a and b, subject and cardiac phase ! Each in their own space with orthogonal basisHoogendoorn et al. Int J Comput Vis 85(3):237-252 (2009). 9
  10. 10. From spatial to spatiotemporal atlases ! Two parameterizations ! a and b, subject and cardiac phase ! Each in their own space with orthogonal basisHoogendoorn et al. Int J Comput Vis 85(3):237-252 (2009). 9
  11. 11. From single object to multi-objects atlases ! Multiple anatomical levels and topologies ! 4 chambers ! Tissue properties ! Muscle & Purkinje fibers ! Coronaries 10
  12. 12. From scalar objects to vectors & tensors! DTI-based fiber orientation In vivo Human 3D Cardiac DTI Reconstruction 7 Muñoz-Moreno,& Frangi ICIP (2010, in press) Anderson et al. Clinical Anatomy 22:64-76(2009) 11 Toussaint et al. Miccai (2010, in press) Fig. 5. Top: Joint histograms of the elevation (or helix) angle and the normalized
  13. 13. 3 CONCLUSIONS & PERSPECTIVES 12
  14. 14. Shape is not enough 13
  15. 15. Motion is not enough Source: http://jcmr-online.com/imedia/1712877943433156/supp1.mpg Erikson et al. JCMR, 12(9), (2010) 14
  16. 16. Perspectives! On biomarkers ! Towards complex indexes ! Integrate shape (local & global), electrical activation, motion/deformation, and flow ! Towards new probabilistic biomarkers ! Distance to populations/manifolds! On data integration ! Multiple-layers visualization of heart function ! Multi-level patient specific models 15
  17. 17. Patient-specific simulation and virtual populations Geometrical Functional FEM Model Model ModelFEM Model Electrical Multiscale Simulation ModelingElectrophysiology Extracellular Membrane Extramyocardial Intracellular Romero et al. ABME, (2010) Hoogendoorn et al. STACOM, (2010)
  18. 18. Thanks 17

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