Decraene fimh09


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Presentation by M. De Craene at FIMH 2009

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  • Decraene fimh09

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