Temporal Diffeomorphic Free-Form Deformation:  Application to for Motion and Deformation    Estimation from 3D Echocardiog...
Motion and Deformation Indexes    Motion    Quantify the motion    field over    the cardiac cycle                         ...
Motion and Deformation Indexes    Motion    Quantify the motion    field over    the cardiac cycle                         ...
Algorithmic framework! Extend diffeomorphic ! TDFFD  registration for joint Temporal Diffeomorphic  alignment of image    ...
Recent advances in diffeomorphism for  quantification of longitudinal changes  ! Durrleman et al.                (1)       ...
MethodTransformation model! Continuous velocity field in the 3D+t domain! The displacement field is obtained from the  displ...
MethodTransformation model! Numerical integration: Forward Euler integration                                              ...
MethodParametric Jacobian! Definitions! Eq. (1) can then be rewritten as                                                 (2...
MethodObjective function! The first frame is taken as reference! Gradient-based optimization (L-BFGS-B method)! Requires de...
Experiments on synthetic ultrasoundimages! Synthetic US 3D Sequence as used in [1]! It models the left ventricle a sa thic...
Synthetic displacement field:Habemus ground truth
Experiments on synthetic ultrasoundimages ( surface propagation )
Experiments on synthetic ultrasoundimages! Comparing error on displacement fields (magnitude of  difference between estima...
Experiments on synthetic ultrasoundimages! Comparing error on displacement fields (magnitude of  difference between estima...
Motion quantification in healthyvolunteers! Database of 8 healthy subjects (aged 31 +/- 6 years)! The average number of ima...
Motion quantification in healthyvolunteers                                  14
Volunteer 1        0.05   Long. strain volunteer 1          0       −0.05        −0.1       −0.15        −0.2       −0.25 ...
Volunteer 2        0.05   Long. strain volunteer 2          0       −0.05        −0.1       −0.15        −0.2       −0.25 ...
Volunteer 3        0.05   Long. strain volunteer 3          0       −0.05        −0.1       −0.15        −0.2       −0.25 ...
Volunteer 4        0.05   Long. strain volunteer 4          0       −0.05        −0.1       −0.15        −0.2       −0.25 ...
Volunteer 5        0.05   Long. strain volunteer 5          0       −0.05        −0.1       −0.15        −0.2       −0.25 ...
Volunteer 6        0.05   Long. strain volunteer 6          0       −0.05        −0.1       −0.15        −0.2       −0.25 ...
Volunteer 7        0.05   Long. strain volunteer 7          0       −0.05        −0.1       −0.15        −0.2       −0.25 ...
Volunteer 8        0.05   Long. strain volunteer 8          0       −0.05        −0.1       −0.15        −0.2       −0.25 ...
De Craene et al, FIMH09 2009 “LargeQuantification of Motion and           diffeomorphic FFD Registration for motion and    ...
Quantification of Motion andDeformation before and after CRT
Strain curves before and after CRT                                     25
Strain curves before and after CRT                                     26
Conclusions! Extension of diffeomorphic framework to handle  image sequences! Continuity of 4D velocity field enforced thro...
Thanks !           28
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Seminar CISTIB Sept 2010

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Seminar CISTIB Sept 2010

  1. 1. Temporal Diffeomorphic Free-Form Deformation: Application to for Motion and Deformation Estimation from 3D Echocardiography Mathieu De Craenea,b, Gemma Piellaa,b, Nicolas Duchateaua,b, Etel Silvad, Adelina Doltrad, Jan Dhoogee, Oscar Camaraa,b, Josep Brugadad, Marta Sitgesd, and Alejandro F. Frangia,b,c Center for Computational Imaging & Simulation Technologies in Biomedicine (CISTIB),a Information and Communication Technologies Department, Universitat Pompeu Fabra, Barcelona, Spain and b Networking Center on Biomedical Research - CIBER-BBN, Barcelona, Spain. c Institucio Catalana de Recerca i Estudis Avancats, Barcelona, Spain. d Hospital Clínic; IDIBAPS; Universitat de Barcelona, Spain. e Department of Cardiovascular Diseases, Cardiovascular Imaging and Dynamics, Katholieke Universiteit Leuven, Belgium.
  2. 2. Motion and Deformation Indexes Motion Quantify the motion field over the cardiac cycle DeformationStrain tensortensor Strain Compute spatial derivatives F of the motion field Longitudinal strain color plotted over time
  3. 3. Motion and Deformation Indexes Motion Quantify the motion field over the cardiac cycle DeformationStrain tensortensor Strain Compute spatial derivatives F of the motion field Longitudinal strain color plotted over time
  4. 4. Algorithmic framework! Extend diffeomorphic ! TDFFD registration for joint Temporal Diffeomorphic alignment of image registration using Free sequences Form Deformation ! Exploit temporal consistency in the dataset
  5. 5. Recent advances in diffeomorphism for quantification of longitudinal changes ! Durrleman et al. (1) ! Diffeomorphic framework for longitudinal regression and atlas building. Comparing the evolution of two populations ! Possible discontinuity at data time points ! Restricted to 2D/3D contours (skulls) ! Khan et al. (2) ! Dense non-rigid registration for diffeomorphic registration of longitudinal datasets ! 2D synthetic images, few time points ! Spatial regularization kernel (nothing done in time) ! Possible discontinuity at data time points(1) Durrleman et al. Spatiotemporal Atlas Estimation for Developmental Delay Detection in Longitudinal Datasets. MICCAI 09(2) Khan et al. Representation of time-varying shapes in the large deformation diffeomorphic framework. ISBI 08. 4
  6. 6. MethodTransformation model! Continuous velocity field in the 3D+t domain! The displacement field is obtained from the displacement field by solving the following ODE: Continuous time Velocity = Sum of 3D + t spatiotemporal kernels Material point in reference frame Transformation
  7. 7. MethodTransformation model! Numerical integration: Forward Euler integration (1) time t=0
  8. 8. MethodParametric Jacobian! Definitions! Eq. (1) can then be rewritten as (2)! We want to compute the derivative of the mapped coordinate of a given material point regarding the velocity parameters using (2)
  9. 9. MethodObjective function! The first frame is taken as reference! Gradient-based optimization (L-BFGS-B method)! Requires de derivative of w.r.t control point velocities (Parametric Jacobian)
  10. 10. Experiments on synthetic ultrasoundimages! Synthetic US 3D Sequence as used in [1]! It models the left ventricle a sa thick-walled ellipsoid with physiologically relevant end-diastolic dimensions! A simplified kinematic model with an ejection fraction of 60% gives an analytical expression of the displacement field.[1] A. Elen, H. Choi, D. Loeckx, H. Gao, P. Claus, P. Suetens, F. Maes, and J. D’hooge, “Three-dimensional cardiac strain estimation using spatio- temporal elastic registration of ultrasound images: a feasibility study.” IEEE Transactions on Medical Imaging, vol. 27, no. 11, pp. 1580 – 1591, 2008.
  11. 11. Synthetic displacement field:Habemus ground truth
  12. 12. Experiments on synthetic ultrasoundimages ( surface propagation )
  13. 13. Experiments on synthetic ultrasoundimages! Comparing error on displacement fields (magnitude of difference between estimated and ground truth motion) for pairwise registration and our algorithm! 2 Levels of noise: 20% and 70 % 12
  14. 14. Experiments on synthetic ultrasoundimages! Comparing error on displacement fields (magnitude of difference between estimated and ground truth motion) for pairwise registration and our algorithm! 2 Levels of noise: 20% and 70 % Median error for w=0.2 Median error for w=0.7 7 7 TDFFD TDFFD 6 FFD 6 FFD Error magnitude (mm) Error magnitude (mm) 5 5 4 4 3 3 2 2 1 1 0 0 0 5 10 15 20 0 5 10 15 20 time frame time frame 12
  15. 15. Motion quantification in healthyvolunteers! Database of 8 healthy subjects (aged 31 +/- 6 years)! The average number of images per cardiac cycle was of 17.8! The pixel spacing was on average of 0.9 x 0.6 x 0.9 mm3! Quantification of strain in mid and basal AHA segments! Segments either not totally included in the field of view of the 3D-US images or suffering from typical image artifacts were excluded from the analysis. 13
  16. 16. Motion quantification in healthyvolunteers 14
  17. 17. Volunteer 1 0.05 Long. strain volunteer 1 0 −0.05 −0.1 −0.15 −0.2 −0.25 0 0.5 1
  18. 18. Volunteer 2 0.05 Long. strain volunteer 2 0 −0.05 −0.1 −0.15 −0.2 −0.25 0 0.5 1
  19. 19. Volunteer 3 0.05 Long. strain volunteer 3 0 −0.05 −0.1 −0.15 −0.2 −0.25 0 0.5 1
  20. 20. Volunteer 4 0.05 Long. strain volunteer 4 0 −0.05 −0.1 −0.15 −0.2 −0.25 0 0.5 1
  21. 21. Volunteer 5 0.05 Long. strain volunteer 5 0 −0.05 −0.1 −0.15 −0.2 −0.25 0 0.5 1
  22. 22. Volunteer 6 0.05 Long. strain volunteer 6 0 −0.05 −0.1 −0.15 −0.2 −0.25 0 0.5 1
  23. 23. Volunteer 7 0.05 Long. strain volunteer 7 0 −0.05 −0.1 −0.15 −0.2 −0.25 0 0.5 1
  24. 24. Volunteer 8 0.05 Long. strain volunteer 8 0 −0.05 −0.1 −0.15 −0.2 −0.25 0 0.5 1
  25. 25. De Craene et al, FIMH09 2009 “LargeQuantification of Motion and diffeomorphic FFD Registration for motion and strain quantification from 3D US sequences ”Deformation before and after CRT before after Septal stretching
  26. 26. Quantification of Motion andDeformation before and after CRT
  27. 27. Strain curves before and after CRT 25
  28. 28. Strain curves before and after CRT 26
  29. 29. Conclusions! Extension of diffeomorphic framework to handle image sequences! Continuity of 4D velocity field enforced through radial basis functions! Coupling between time steps improved robustness to noise! Further questions ! Include incompressibility constraint ! Extension to arbitrary reference in the sequence and sequential metric ! Address unbiased sampling schemes. Symmetric registration. 27
  30. 30. Thanks ! 28

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