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
1. !Atlas construction and image analysis
using statistical cardiac models
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
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
3. Why do we need atlases of the heart?
1.Integrated image-based
biomarkers
Looking at multiple levels
Global shape
Local shape
Motion / Deformation
3
4. Why do we need atlases of the heart?
1.Integrated image-based
biomarkers patient
d1 d2 d1 <?> d2
Probabilistic biomarkers atlas
Encode normality
P-value of abnormality
Duchateau et al, STACOM (2010)
4
5. Why do we need atlases the heart?
2.Integrated multimodal information for
patient-specific modeling
5
6. 2 EVOLUTIONS AND
CHALLENGES
IN HEART ATLAS CONSTRUCTION
6
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 Triangulate
Ordas et al . Proc. SPIE Medical Imaging (2007)
8. From monomodal to multimodal atlases
POINT DISTRIBUTION STATISTICAL INTENSITY MODEL TO IMAGE
MODEL MODEL ADAPTATION/MATCHING
Create automatically by image simulation
Tobon- Gomez et al. IEEE Trans on
Medical Imaging, 27(11):1655-1667
(2008)
8
9. From spatial to spatiotemporal atlases
! Two parameterizations
! a and b, subject and
cardiac phase
! Each in their own space
with orthogonal basis
Hoogendoorn et al. Int J Comput Vis 85(3):
237-252 (2009).
9
10. From spatial to spatiotemporal atlases
! Two parameterizations
! a and b, subject and
cardiac phase
! Each in their own space
with orthogonal basis
Hoogendoorn et al. Int J Comput Vis 85(3):
237-252 (2009).
9
11. From single object to multi-objects atlases
! Multiple anatomical levels and
topologies
! 4 chambers
! Tissue properties
! Muscle & Purkinje fibers
! Coronaries
10
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
15. Motion is not enough
Source: http://jcmr-online.com/imedia/1712877943433156/supp1.mpg
Erikson et al. JCMR, 12(9), (2010)
14
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. Patient-specific simulation and virtual populations
Geometrical Functional FEM
Model Model Model
FEM Model
Electrical Multiscale Simulation
Modeling
Electrophysiology
Extracellular
Membrane Extramyocardial
Intracellular
Romero et al. ABME, (2010)
Hoogendoorn et al. STACOM, (2010)
