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Source and metric estimation in the eikonal equation using optimization on a manifold

  • *Corresponding author: Jérôme Fehrenbach

    *Corresponding author: Jérôme Fehrenbach 
Abstract / Introduction Full Text(HTML) Figure(8) / Table(3) Related Papers Cited by
  • We address the estimation of the source(s) location in the eikonal equation on a Riemann surface, as well as the determination of the metric when it depends on a few parameters. The available observations are the arrival times or are obtained indirectly from the arrival times by an observation operator, this frame is intended to describe electro-cardiographic imaging. The sensitivity of the arrival times is computed from $ {{{\rm{Log}}}}_x $ the log map wrt to the source $ x $ on the surface. The $ {{{\rm{Log}}}}_x $ map is approximated by solving an elliptic vectorial equation, using the Vector Heat Method. The $ L^2 $-error function between the model predictions and the observations is minimized using Gauss-Newton optimization on the Riemann surface. This allows to obtain fast convergence. We present numerical results, where coefficients describing the metric are also recovered like anisotropy and global orientation.

    Mathematics Subject Classification: Primary: 35F21, 49Q12; Secondary: 35R30.

    Citation:

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  • Figure 1.  Some triangle $ ABC $ of the mesh and the associated angles

    Figure 2.  Top : location of the successive iterates $ x^k $ (red) and the true source point $ x^* $ (blue), the 3rd iterate $ x^3 $ coincides with $ x^* $. Bottom left: evolution of the cost function $ J $ for cases 1a and 1b. Bottom right: evolution of the distance to the solution $ \|x^k-x^*\| $ in both cases, note that the iterates are the same for cases 1a and 1b

    Figure 3.  Top: location of the successive iterates $ x^k $ before splitting (red) and after splitting (green) and the true source points (blue). Bottom left: evolution of the cost function $ J $ for cases 2a and 2b. Bottom right: evolution of the distance to the solution in both cases

    Figure 4.  Left: evolution of the cost function $ J $ for cases 3a and 3b. Right: evolution of the distance to the solution $ \|x^k-x^*\| $ in both cases

    Figure 5.  Left: location of the successive iterates $ x^k $ (red) and the true source point $ x^* $ (blue). Right: evolution of the parameters of the metric (dashed: case 3a, line: case 3b). Note that it is not optimized during the first 4 steps

    Figure 6.  Left: configuration of the torso (electrodes in red) and the heart surface (visible by transparency). Right: observations at the electrodes (mimicking an ECG) obtained with noise-level 1%

    Figure 7.  Top: location of the successive iterates $ x^k $ (red) and the true source point $ x^* $ (blue). Bottom left: evolution of the cost function $ J $. Bottom right: evolution of the distance to the solution $ \|x^k-x^*\| $ in both cases

    Figure 8.  Time necessary to solve the localization problem of Test case 1 on meshes with increasing size

    Table 1.  Reference and estimated activation times for test case 1

    True activation time $ \tau^\star $ retrieved $ \tau $ for case 1a retrieved $ \tau $ for case 1b
    0.2 0.229 0.217
     | Show Table
    DownLoad: CSV

    Table 2.  Reference and estimated activation times for test case 2

    True activation times $ \tau^\star_1 $/$ \tau^\star_2 $ retrieved $ \tau $ for case 2a retrieved $ \tau $ for case 2b
    0/0.2 0.031/0.228 0.017/0.226
     | Show Table
    DownLoad: CSV

    Table 3.  Reference and estimated activation times for test case 3

    True activation times $ \tau^\star $ retrieved $ \tau $ for case 3a retrieved $ \tau $ for case 3b
    0/0.2 0.019/0.214 0.011/0.0215
     | Show Table
    DownLoad: CSV
  • [1] P.-A. Absil, R. Mahony and R. Sepulchre, Optimization Algorithms on Matrix Manifolds, Princeton University Press, 2009. doi: 10.1515/9781400830244.
    [2] M. Bardi and I. Capuzzo-Dolcetta, Optimal Control and Viscosity Solutions of Hamilton-Jacobi-Bellman Equations, volume 12., Springer, 1997. doi: 10.1007/978-0-8176-4755-1.
    [3] G. Barles, Solutions de Viscosité des Équations de Hamilton-Jacobi, volume 17., Springer, 1994.
    [4] A. I. Bobenko and B. A. Springborn, A discrete laplace–beltrami operator for simplicial surfaces, Discrete & Computational Geometry, 38 (2007), 740-756.  doi: 10.1007/s00454-007-9006-1.
    [5] F. Bornemann and C. Rasch, Finite-element discretization of static hamilton-jacobi equations based on a local variational principle, Computing and Visualization in Science, 9 (2006), 57-69.  doi: 10.1007/s00791-006-0016-y.
    [6] Y. BourgaultY. Coudière and C. Pierre, Existence and uniqueness of the solution for the bidomain model used in cardiac electrophysiology, Nonlinear Analysis: Real World Applications, 10 (2009), 458-482.  doi: 10.1016/j.nonrwa.2007.10.007.
    [7] P. P. Chinchapatnam, K. S. Rhode, A. King, G. Gao, Y. Ma, T. Schaeffter, D. J. Hawkes, R. S. Razavi, D. L. G. Hill, S. Arridge and M. Sermesant, Anisotropic wave propagation and apparent conductivity estimation in a fast electrophysiological model: Application to xmr interventional imaging, Med Image Comput Comput Assist Interv., 2007,575-583. doi: 10.1007/978-3-540-75757-3_70.
    [8] M. CluitmansD. H. BrooksR. MacLeodO. DosselM. S. GuillemP. M. van DamJ. SvehlikovaB. HeJ. SappL. Wang and L. Bear, Validation and opportunities of electrocardiographic imaging: From technical achievements to clinical applications, Front Physiol., 9 (2018), 1305.  doi: 10.3389/fphys.2018.01305.
    [9] P. Colli Franzone and L. Guerri, Spreading of excitation in 3-d models of the anisotropic cardiac tissue. i. validation of the eikonal model, Mathematical Biosciences, 113 (1993), 145-209.  doi: 10.1016/0025-5564(93)90001-Q.
    [10] D. Colton and R. Kress, Inverse Acoustic and Electromagnetic Scattering Theory, volume 93., Springer, 1998. doi: 10.1007/978-3-662-03537-5.
    [11] C. CorradoJ.-F. Gerbeau and P. Moireau, Identification of weakly coupled multiphysics problems. application to the inverse problem of electrocardiography, J. Comput. Phys., 283 (2015), 271-298.  doi: 10.1016/j.jcp.2014.11.041.
    [12] M. G. CrandallH. Ishii and P.-L. Lions, User's guide to viscosity solutions of second order partial differential equations, Bulletin of the American Mathematical Society, 27 (1992), 1-67.  doi: 10.1090/S0273-0979-1992-00266-5.
    [13] K. CraneC. Weischedel and M. Wardetzky, Geodesics in heat: A new approach to computing distance based on heat flow, ACM Transactions on Graphics (TOG), 32 (2013), 1-11.  doi: 10.1145/2516971.2516977.
    [14] B. Delaunay, Sur la sphere vide, Izv. Akad. Nauk SSSR, Otdelenie Matematicheskii i Estestvennyka Nauk, 7 (1934), 1-2. 
    [15] S. Gallot, D. Hulin and J. Lafontaine, Riemannian Geometry, volume 2., Springer, 1990. doi: 10.1007/978-3-642-97242-3.
    [16] C. Geuzaine and J.-F. Remacle, Gmsh: A 3-d finite element mesh generator with built-in pre-and post-processing facilities, International Journal for Numerical Methods in Engineering, 79 (2009), 1309-1331.  doi: 10.1002/nme.2579.
    [17] T. Grandits, A. Effland, T. Pock, R. Krause, G. Plank and S. Pezzuto, Geasi: Geodesic-based earliest activation sites identification in cardiac models, International Journal for Numerical Methods in Biomedical Engineering, 37 (2021), e3505. doi: 10.1002/cnm.3505.
    [18] T. Grandits, S. Pezzuto, J. M. Lubrecht, T. Pock, G. Plank and R. Krause, Piemap: Personalized inverse eikonal model from cardiac electro-anatomical maps, In International Workshop on Statistical Atlases and Computational Models of the Heart, Springer, 2020, 76-86. doi: 10.1007/978-3-030-68107-4_8.
    [19] M. JiangL. XiaG. ShouQ. WeiF. Liu and S. Crozier, Effect of cardiac motion on solution of the electrocardiography inverse problem, IEEE Trans. Biomed. Eng., 56 (2009), 923-931.  doi: 10.1109/TBME.2008.2005967.
    [20] S. Kallhovd, M. M. Maleckar and M. E. Rognes, Inverse estimation of cardiac activation times via gradient-based optimization, International Journal for Numerical Methods in Biomedical Engineering, 34 (2018), e2919. doi: 10.1002/cnm.2919.
    [21] R. Kimmel and J. A. Sethian, Computing geodesic paths on manifolds, Proceedings of The National Academy of Sciences, 95 (1998), 8431-8435.  doi: 10.1073/pnas.95.15.8431.
    [22] F. KnöppelK. CraneU. Pinkall and P. Schröder, Globally optimal direction fields, ACM Transactions on Graphics (ToG), 32 (2013), 1-10.  doi: 10.1145/2461912.2462005.
    [23] E. KonukogluJ. RelanU. CilingirB. H. MenzeP. ChinchapatnamA. JadidiH. CochetM. HociniH. DelingetteP. JaisM. HaissaguerreN. Ayache and M. Sermesant, Efficient probabilistic model personalization integrating uncertainty on data and parameters: Application to eikonal-diffusion models in cardiac electrophysiology, Progress in Biophysics and Molecular Biology, 107 (2011), 134-146.  doi: 10.1016/j.pbiomolbio.2011.07.002.
    [24] K. KunischA. NeicG. Plank and P. Trautmann, Inverse localization of earliest cardiac activation sites from activation maps based on the viscous eikonal equation, Journal of Mathematical Biology, 79 (2019), 2033-2068.  doi: 10.1007/s00285-019-01419-3.
    [25] A. NeicF. O. CamposA. J. PrasslS. A. NiedererM. J. BishopE. J. Vigmond and G. Plank, Efficient computation of electrograms and ecgs in human whole heart simulations using a reaction-eikonal model, Journal of Computational Physics, 346 (2017), 191-211.  doi: 10.1016/j.jcp.2017.06.020.
    [26] B. O'NeillSemi-Riemannian Geometry with Applications to Relativity, Academic press, 1983. 
    [27] H. S. Oster and Y. Rudy, The use of temporal information in the regularization of the inverse problem of electrocardiography, In [1990] Proceedings of the Twelfth Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1990,599-600. doi: 10.1109/IEMBS.1990.691234.
    [28] S. PalamaraC. VergaraD. CatanzaritiE. FaggianoC. PangrazziM. CentonzeF. NobileM. Maines and A. Quarteroni, Computational generation of the purkinje network driven by clinical measurements: The case of pathological propagations, Int. J. Numer. Method. Biomed. Eng., 30 (2014), 1558-77.  doi: 10.1002/cnm.2689.
    [29] S. PezzutoF. W. PrinzenM. PotseF. MaffessantiF. RegoliM. L. CaputoG. ConteR. Krause and A. Auricchio, Reconstruction of three-dimensional biventricular activation based on the 12-lead electrocardiogram via patient-specific modelling, EP Europace, 23 (2021), 640-647.  doi: 10.1093/europace/euaa330.
    [30] M. PotseA. VinetT. Opthof and R. Coronel, Validation of a simple model for the morphology of the \t wave in unipolar electrograms, Am. J. Physiol. Heart Circ. Physiol., 297 (2009), 792-801.  doi: 10.1152/ajpheart.00064.2009.
    [31] G. RavonY. CoudièreM. Potse and R. Dubois, Impact of the endocardium in a parameter optimization to solve the inverse problem of electrocardiography, Front. Physiol., 22 (2019), 1946.  doi: 10.3389/fphys.2018.01946.
    [32] G. Ravon, R. Dubois, Y. Coudière and M. Potse, A parameter optimization to solve the inverse problem in electrocardiography, In Mihaela Pop and Graham A Wright, editors, Functional Imaging and Modelling of the Heart, Springer, 2017,219-229. doi: 10.1007/978-3-319-59448-4_21.
    [33] M. Sermesant, E. Konukoglu, H. Delingette, Y. Coudière, P. Chinchapatnam, K. Rhode, R. Razavi and N. Ayache, An anisotropic multi-front fast marching method for real-time simulation of cardiac electrophysiology, In Frank B. Sachse and Gunnar Seemann, editors, Functional Imaging and Modeling of the Heart, 160-169, Berlin, Heidelberg, 2007. Springer Berlin Heidelberg. doi: 10.1007/978-3-540-72907-5_17.
    [34] N. SharpY. Soliman and K. Crane, The vector heat method, ACM Transactions on Graphics (TOG), 38 (2019), 1-19.  doi: 10.1145/3243651.
    [35] S. R. S. Varadhan, On the behavior of the fundamental solution of the heat equation with variable coefficients, Communications on Pure and Applied Mathematics, 20 (1967), 431-455.  doi: 10.1002/cpa.3160200210.
    [36] C. VergaraS. PalamaraD. CatanzaritiF. NobileE. FaggianoC. PangrazziM. CentonzeM. MainesA. Quarteroni and G. Vergara, Patient-specific generation of the purkinje network driven by clinical measurements of a normal propagation, Med. Biol. Eng. Comput., 52 (2014), 813-826.  doi: 10.1007/s11517-014-1183-5.
    [37] M. WallmanN. S. Smith and B. Rodriguez, A comparative study of graph-based, eikonal, and monodomain simulations for the estimation of cardiac activation times, IEEE Trans Biomed Eng., 59 (2012), 1739-1748.  doi: 10.1109/TBME.2012.2193398.
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