Presentation by M. De Craene at FIMH 2009

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

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)