Decraene spie-09

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Presentation by M. De Craene at SPIE Medical Imaging 2009

Presentation by M. De Craene at SPIE Medical Imaging 2009

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  • 1. Non-stationary Diffeomorphic Registration: Application toEndo-Vascular TreatmentMonitoring
    M. De Craene2,1,O. Camara1,2, B.H. Bijnens1,2,3, A.F. Frangi1,2,3
    Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Barcelona Spain.
    1. Information and Communication Technologies Department, Universitat Pompeu Fabra, Barcelona, Spain
    2. Networking Center on Biomedical Research - Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN
    3. Catalan Institution for Research and Advanced Studies (ICREA).
  • 2. Context.Aneurysm Recurrence after Coiling
    Causes
    Compaction of the coil mass
    Aneurysm growth
    Related factors
    Packing [Johnston,Kai]
    Ratio between the volume of inserted coil and the aneurysm volume
    Shown to be a strong predictor of aneurysm recurrence
    Others [Cottier]
    Size
    Treatment during the acute phase
    Rupture status
    2
    Example of “coil compaction” : DSA image [Steinman]
    Cottier et al. Neuroradiology45, pp. 818–824, 2003.
    Johnston et al. Stroke 39(1), pp. 120–125, 2008.
    Kai et al. Neurosurgery 56, pp. 785–791, 2005.
    Steinman et al. American Journal of Neuroradiology24, pp. 559–566, 2003.
  • 3. Image-based quantification of aneurysm recurrence and evolution over time
    Objectives
    Visualize several time points in a common frame of coordinates
    Compute coil and aneurysm volume curves over time
    Local deformation maps
    3
    t0 pre
    t1 post
    t2 pre
    t1 pre
    t0 post
    t3 post
    t4 pre
    t4 post
    t5 pre
  • 4. Characterize evolution using non-rigid registration
    Local deformation maps
    Where is the aneurysm growing?
    4
  • 5. Challenges
    Accuracy for detecting small volume changes and retreat if necessary
    Flexibility for detecting small and large volume changes
    Depends on time follow-up interval, aneurysm location, …
    Invertibilityof the non-rigid mapping to ensure correctness of the volume change estimate
    5
    Patient 1
    Patient 2
    Patient 3
  • 6. Non-rigid registration and diffeomorphisms
    Popular pairwise diffeomorphic registration schemes
    Mainly optimize a dense velocity field
    • Higher computational cost, no implicit regularization as offered by FFD (except [Rueckert])
    • 7. Simple optimization scheme based on first derivatives (except [Hernandez])
    • 8. )
    6
    Beg et al. Int. J. Comput. Vis. 61 (2), pp. 139–157, 2005.
    Hernandez et al. MMBIA’07 , 2007.
    Rueckert et al. MICCAI’06, LNCS 4191, pp. 702–709, 2006.
    Vercauteren et al. MICCAI’07, LNCS 4792, pp. 319–326, 2007.
  • 9. LDFFD diffeomorphic non-rigid registration
    Transformation = Concatenation of FFD transformations
    Strong coupling between phases: the first transformation influences all subsequent time steps
    Mutual information metric: ITK, Mattes´ implementation
    LBFGS optimizer: ITK
    7
    v(x;t0)
    v(x;t1)
    v(x;t2)
    v(x;t3)
    u(x;t2)
    time
    For k=1:M (number of time steps)
  • 10. LDFFD diffeomorphic non-rigid registration
    8
    ∆u(x;t2)
    ∆v(x;t0)
    v(x;t1)
    v(x;t2)
    ∆ metric  ∆ intensity  ∆ mapped coordinate  ∆ transformation parameter
    Parametric Jacobian
    Similar expression can be found in LDDMM registration [Beg] when computing variational derivative
    Parametric Jacobian of mth transformation
    Jacobian of all transformations posterior to m
    Beg et al. Int. J. Comput. Vis. 61 (2), pp. 139–157, 2005.
  • 11. LDFFD diffeomorphic non-rigid registration
    9
    u(x;t2)
    v(x;t0)
    Multi-resolution scheme in the temporal dimension
    Initiate algorithm with 2 time steps
    In the event that any of these parameters reaches a given threshold (0.4 the spacing between control points, as proposed by [Rueckert])
    Interrupt optimization
    Restore last set of valid parameters
    Break the problematic time steps using square root computation [Arsigny]
    with
    Told
    Tnew
    Tnew
    Arsigny et al. MICCAI’ 06, LNCS 4190, pp. 924-931, 2006.
    Rueckert et al. MICCAI’06, LNCS 4191, pp. 702–709, 2006.
  • 12. LDFFD at work
    10
  • 13. Results: aneurysm volume changes measured by non-rigid registration
    11
    Rueckert et al. IEEE Transactions on Medical Imaging 18(8), pp. 712-721, 1999.
    Rueckert et al. MICCAI’06, LNCS 4191, pp. 702–709, 2006.
  • 14. Results: patient 1, second time point
    12
    t1
    t2
    LDFFD
    [Verc06]
    [Rueck99]
    [Rueck06]
    Rueckert et al. IEEE Transactions on Medical Imaging 18(8), pp. 712-721, 1999.
    Rueckert et al. MICCAI’06, LNCS 4191, pp. 702–709, 2006.
    Vercauteren et al. MICCAI’07, LNCS 4792, pp. 319–326, 2007.
  • 15. Results: patient 1, second time point
    13
    t1
    t2
    LDFFD
    [Verc06]
    [Rueck99]
    [Rueck06]
    Rueckert et al. IEEE Transactions on Medical Imaging 18(8), pp. 712-721, 1999.
    Rueckert et al. MICCAI’06, LNCS 4191, pp. 702–709, 2006.
    Vercauteren et al. MICCAI’07, LNCS 4792, pp. 319–326, 2007.
  • 16. 14
    Results: Jacobian distributions
    FFD
    LDFFD
    Diff FFD
    Diff. Demons
  • 17. Results: displacement fields
    15
    [Rueck99]
    LDFFD
    [Rueck06]
    [Verc06]
    Rueckert et al. IEEE Transactions on Medical Imaging 18(8), pp. 712-721, 1999.
    Rueckert et al. MICCAI’06, LNCS 4191, pp. 702–709, 2006.
    Vercauteren et al. MICCAI’07, LNCS 4792, pp. 319–326, 2007.
  • 18. Conclusions
    LDFFD: non-stationary non-rigid registration algorithm
    Dynamically finds the optimal number of time steps
    Transformation invertibility
    Keep the dimension of the optimization problem reasonably low
    Applicable to quantify post interventional volume changes over subsequent follow-ups
    Future work,
    Exploit Jacobian-based local growth maps
    Comparison to other coil compaction predictors published in the literature
    Extension to motion and deformation estimation from image sequences: FIMH 09, Nice.
    16
  • 19. Acknowledgements
    This research has been partially funded by the Industrial and Technological Development Centre (CDTI) under the CENIT Programme (CDTEAM Project) and the Integrated Project @neurIST(IST-2005-027703), which is cofinanced by the European Commission.
    The authors wish to acknowledge ElioVivasfor the acquisition of the intra-cranial aneurysm imaging data using 3D rotational angiography at Hospital General de Catalunya, San Cugat del Valles, Spain.
    17