Decraene fimh09
Upcoming SlideShare
Loading in...5
×

Like this? Share it with your network

Share

Decraene fimh09

  • 1,037 views
Uploaded on

Presentation by M. De Craene at FIMH 2009

Presentation by M. De Craene at FIMH 2009

More in: Technology
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
No Downloads

Views

Total Views
1,037
On Slideshare
708
From Embeds
329
Number of Embeds
3

Actions

Shares
Downloads
2
Comments
0
Likes
2

Embeds 329

http://www.dtic.upf.edu 182
http://mj89sp3sau2k7lj1eg3k40hkeppguj6j-a-sites-opensocial.googleusercontent.com 136
http://cilab2.upf.edu 11

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide
  • Définir les termes pasforcémentévidentspourl´auditeur

Transcript

  • 1. Large Diffeomorphic FFD Registration forMotion and Strain Quantification from 3D-USSequences
    Mathieu De Craene1,2, Oscar Camara2,1, Bart H. Bijnens3,2,1,
    and Alejandro F. Frangi2,1,3
    Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB)
    1 Networking Biomedical Research Center on Bioengineering, Biomaterials and Nanomedicine,
    (CIBER-BBN), Barcelona, Spain
    2UniversitatPompeuFabra, Barcelona, Spain
    3 InstitucióCatalana de RecercaiEstudisAvançats (ICREA)
  • 2. Context (1/2)3D Ultrasound challenges
    Motion and deformation estimation from Ultra-sound image sequences
    Patient friendly
    Low cost
    Acquisition noise  challenging for image processing (segmentation and registration)
    Exploit temporal consistency
    Extend diffeomorphic registration sequences for joint alignment of image sequences
  • 3. Context (2/2) Motion and deformation
    Displacement (mm)
    Long strain (%)
    Point 2
    Point 1
    % cardiaccycle
    % cardiac cycle
  • 4. State of the art (1/2)
    Diffeomorphicpairwise registration: invertible mapping with smooth inverse
    Mainly optimize a dense non-parametric velocity field
    • Higher computational cost, no implicit regularization as offered by FFD (except [3])
    • 5. Simple optimization scheme based on first derivatives (except [2])
    [1] Beg et al. “Computing large deformation metric mappings via geodesic flows of diffeomorphisms.” Int. J. Comput. Vis. 61 (2) (2005) pp.139–157.
    [2] Hernandez et al. “Registration of anatomical images using geodesic paths of diffeomorphisms parameterized with stationary vector fields”. MMBIA’07 (2007).
    [3] Rueckert et al. “Diffeomorphic Registration using B-Splines”. MICCAI’06, LNCS 4191(2006), pp. 702–709.
    [4] Vercauteren et al. “Diffeomorphic image registration with the demons algorithm”. MICCAI’07, LNCS 4792 (2007), pp. 319–326.
  • 6. State of the art (2/2)
    Extension of diffeomorphic registration to handle temporal data
    Framework for point sets (landmarks, curves and surfaces) encoding within-subject shape changes in a global template via parallel transport technique [1]
    Dense deformation field for measuring longitudinal changes over follow-up (interval of several months) [2]
    Advantages
    Invertible mapping with smooth inverse
    Use of velocity fields to enforce temporal consistency
    [1] Qiu et al. “Time sequence diffeomorphic metric mapping and parallel transport track time-dependent shape changes”. NeuroImage. 45(1) Supl. 1 (2009), pp. S51-S60
    [2] Khan et al. Representation of time-varying shapes in the large deformation diffeomorphic framework. ISBI 2008, pp.1521-1524
  • 7. Method (1/4)Transformation model
    Concatenation of FFD transformations
    Strong coupling between phases
    The first transformation influences all subsequent time steps
    v(x;t0)
    v(x;t1)
    v(x;t2)
    v(x;t3)
    u(x;t2)
    time
  • 8. Method (2/4)
    Metric
    Average of the joint histograms between images at t0 and ti
    Mutual information computed from the average joint histogram
    Optimization method: LBFGS
    Limited-memory quasi-Newton method for unconstrained optimization
    ∆ metric  ∆ intensity  ∆ mapped coordinate  ∆ transformation parameter
    Parametric Jacobian at time step M regarding a parameter a time step m<M
    ∆u(x;t2)
    ∆v(x;t0)
    v(x;t1)
    v(x;t2)
    Parametric Jacobian of mth transformation
    Jacobian of all transformations posterior to m: Account for volume changes
  • 9. Method (3/4)
    First image segmented using an ASM segmentation technique [1]
    The segmentation is deformed using the result of the registration
    [1] Butakoffet al. “Simulated 3D ultrasound LV cardiac images for active shape model training”. Proc SPIE Med Imag (SPIE’07):Image Processing (2007) 6512:U5123.
  • 10. Undeformed mesh
    Method (4/4)
    Non-rigid transformation used to propagate surface mesh in the first frame
    On each triangle, strain is computed by using the first derivatives F of the displacement field
    Strain computed in the reference space of coordinates of the first frame (end-diastolic)
    F is approximated using linear shape functions
    Deformed mesh
  • 11. Results. Longitudinal strain in healthy subject 1 as color map
    Longitudinal strain color plotted over time
  • 12. Results. Longitudinal strain curves in healthy subject 2 over 17
    1
    7
    2
    6
    13
    8
    12
    17
    16
    14
    Long strain (%)
    11
    3
    9
    15
    5
    10
    4
    % cardiac cycle
  • 13. Results. Longitudinal strain curves in healthy subject 1 over 17 regions
    1
    7
    2
    6
    13
    8
    12
    17
    16
    14
    Long strain (%)
    11
    3
    9
    15
    5
    10
    4
    % cardiac cycle
  • 14. Results. Tracking before and after CRT
    after
    before
  • 15. Results. Application to CRT case
  • 16. Results. Strain before and after CRT
    Septal stretching
    after
    before
  • 17. Results. Strain before and after CRT
    Septal stretching
    1
    7
    2
    6
    13
    8
    12
    17
    16
    14
    11
    3
    9
    15
    5
    10
    4
    before CRT
    after CRT
    normal
  • 18. Results. Strain before and after CRT
    1
    7
    2
    6
    13
    8
    12
    17
    16
    14
    11
    3
    9
    15
    5
    10
    4
    before CRT
    after CRT
    normal
  • 19. Conclusions
    Diffeomorphic registration framework suited for handling motion and deformation estimation problems
    The technique can be generalized to other cardiac imaging modalities and to other organs imaged dynamically
    Include temporal consistency in the representation of the transformation
    Strong coupling between time points
    Current drawbacks
    High computation time  Parallelize
    Dimensionality proportional to the number of images in the sequence  temporal windowing
  • 20. Future work
    Deal with basal fibrous valve ring separately
    More flexible application-specific regions
    Confidence based on image SNR or distance to transducer
    Replace FFD chain by continuous velocity field defined over space and time
    Increases complexity
    Modeling velocity instead of incremental displacements
    Add physical constraints
    Incompressibility
    Incorporate in the velocity estimate information coming from modalities that directly estimate velocities
    Tissue Doppler Imaging
  • 21. Acknowledgments. Funding agencies
    European Community’s 7th framework programme (FP7/2007-2013) under grant agreement n. 224495: euHeart project
    CENIT-CDTEAM grant funded by the Spanish Ministry of Science and Innovation (MICINN-CDTI)