[Formulation en transformation finie du principe d’écart d’équilibre discret : application à la régularisation mécanique pour le suivi de mouvement]
Le principe de l’écart d’équilibre offre un bon compromis entre la robustesse et la précision pour la régularisation du suivi du mouvement, car il impose simplement que le mouvement suivi corresponde à celui d’un corps se déformant sous l’effet de charges arbitraires. Cet article présente une extension du principe de l’écart d’équilibre dans le cadre des grandes déformations, un nouveau terme de régularisation pour contrôler les tractions de surface, les deux dans le contexte du suivi de mouvement par éléments finis, et une reformulation « problème inverse » cohérente du problème de suivi de mouvement avec régularisation mécanique. Les performances de suivi de la méthode proposée, avec une résolution de déplacement allant jusqu’à la taille du pixel de l’image, sont démontrées sur des images synthétiques représentant divers mouvements avec différents rapports signal-sur-bruit.
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The equilibrium gap principle offers a good trade-off between robustness and accuracy for regularizing motion tracking, as it simply enforces that the tracked motion corresponds to a body deforming under arbitrary loadings. This paper introduces an extension of the equilibrium gap principle in the large deformation setting, a novel regularization term to control surface tractions, both in the context of finite element motion tracking, and an inverse problem consistent reformulation of the tracking problem. Tracking performance of the proposed method, with displacement resolution down to the pixel size, is demonstrated on synthetic images representing various motions with various signal-to-noise ratios.
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Mot clés : Suivi de mouvement, Régularisation mécanique, Principe de l’écart d’équilibre, Méthode des éléments finis, Problèmes inverses
Martin Genet 1, 2
@article{CRMECA_2023__351_G2_429_0, author = {Martin Genet}, title = {Finite strain formulation of the discrete equilibrium gap principle: application to mechanically consistent regularization for large motion tracking}, journal = {Comptes Rendus. M\'ecanique}, pages = {429--458}, publisher = {Acad\'emie des sciences, Paris}, volume = {351}, year = {2023}, doi = {10.5802/crmeca.228}, language = {en}, }
TY - JOUR AU - Martin Genet TI - Finite strain formulation of the discrete equilibrium gap principle: application to mechanically consistent regularization for large motion tracking JO - Comptes Rendus. Mécanique PY - 2023 SP - 429 EP - 458 VL - 351 PB - Académie des sciences, Paris DO - 10.5802/crmeca.228 LA - en ID - CRMECA_2023__351_G2_429_0 ER -
%0 Journal Article %A Martin Genet %T Finite strain formulation of the discrete equilibrium gap principle: application to mechanically consistent regularization for large motion tracking %J Comptes Rendus. Mécanique %D 2023 %P 429-458 %V 351 %I Académie des sciences, Paris %R 10.5802/crmeca.228 %G en %F CRMECA_2023__351_G2_429_0
Martin Genet. Finite strain formulation of the discrete equilibrium gap principle: application to mechanically consistent regularization for large motion tracking. Comptes Rendus. Mécanique, Volume 351 (2023), pp. 429-458. doi : 10.5802/crmeca.228. https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.5802/crmeca.228/
[1] Applications of digital-image-correlation techniques to experimental mechanics, Exp. Mech., Volume 25 (1985) no. 3, pp. 232-244 | DOI
[2] Digital image correlation: from displacement measurement to identification of elastic properties – a review, Strain, Volume 42 (2006) no. 2, pp. 69-80 | DOI
[3] Volumetric digital image correlation applied to X-ray microtomography images from triaxial compression tests on argillaceous rock, Strain, Volume 43 (2007) no. 3, pp. 193-205 | DOI
[4] Microstructural deformation observed by Mueller polarimetry during traction assay on myocardium samples, Sci. Rep., Volume 10 (2020) no. 1, 20531 | DOI
[5] Fast determination of regional myocardial strain fields from tagged cardiac images using harmonic phase MRI, Circulation, Volume 101 (2000) no. 9, pp. 981-988 | DOI
[6] Quantification of biventricular strains in heart failure with preserved ejection fraction patient using hyperelastic warping method, Front. Physiol., Volume 9 (2018), 1295 | DOI
[7] euHeart: personalized and integrated cardiac care using patient-specific cardiovascular modelling, Interface Focus, Volume 1 (2011) no. 3, pp. 349-364 | DOI
[8] Estimation of regional pulmonary compliance in idiopathic pulmonary fibrosis based on personalized lung poromechanical modeling, J. Biomech. Eng., Volume 144 (2022) no. 9, 091008 | DOI
[9] Assessment of digital image correlation measurement errors: methodology and results, Exp. Mech., Volume 49 (2009) no. 3, pp. 353-370 | DOI
[10] Comparison of local and global approaches to digital image correlation, Exp. Mech., Volume 52 (2012) no. 9, pp. 1503-1519 | DOI
[11] Deformable medical image registration: a survey, IEEE Trans. Med. Imaging, Volume 32 (2013) no. 7, pp. 1153-1190 | DOI
[12] Benchmarking framework for myocardial tracking and deformation algorithms: an open access database, Med. Image Anal., Volume 17 (2013) no. 6, pp. 632-648 | DOI
[13] High resolution digital image correlation using proper generalized decomposition: PGD-DIC, Int. J. Numer. Methods Eng., Volume 92 (2012) no. 6, pp. 531-550 | DOI | MR | Zbl
[14] Deformable templates using large deformation kinematics, IEEE Trans. Image Process.: A Publ. IEEE Signal Process. Soc., Volume 5 (1996) no. 10, pp. 1435-1447 | DOI
[15] iLogDemons: a demons-based registration algorithm for tracking incompressible elastic biological tissues, Int. J. Comput. Vis., Volume 92 (2011) no. 1, pp. 92-111 | DOI
[16] Measurement of strain in the left ventricle during diastole with cine-MRI and deformable image registration, J. Biomech. Eng., Volume 127 (2005) no. 7, pp. 1195-1207 | DOI
[17] Equilibrated warping: finite element image registration with finite strain equilibrium gap regularization, Med. Image Anal., Volume 50 (2018), pp. 1-22 | DOI
[18] A finite element formulation to identify damage fields: the equilibrium gap method, Int. J. Numer. Methods Eng., Volume 61 (2004) no. 2, pp. 189-208 | DOI | Zbl
[19] Voxel-scale digital volume correlation, Exp. Mech., Volume 51 (2010) no. 4, pp. 479-490 | DOI
[20] Validation of equilibrated warping—image registration with mechanical regularization—on 3D ultrasound images, Functional Imaging and Modeling of the Heart (FIMH) (Y. Coudière; V. Ozenne; E. Vigmond; N. Zemzemi, eds.), Volume 11504, Springer International Publishing, Cham, 2019, pp. 334-341 | DOI
[21] Validation of finite element image registration-based cardiac strain estimation from magnetic resonance images, Proc. Appl. Math. Mech., Volume 19 (2019) no. 1, e201900418 | DOI
[22] Patient-specific computational analysis of ventricular mechanics in pulmonary arterial hypertension, J. Biomech. Eng., Volume 138 (2016) no. 11, 111001
[23] Three-dimensional biventricular strains in pulmonary arterial hypertension patients using hyperelastic warping, Comput. Methods Programs Biomed., Volume 189 (2020), 105345
[24] Left ventricular torsion obtained using equilibrated warping in patients with repaired tetralogy of fallot, Pediatr. Cardiol., Volume 42 (2021), pp. 1275-1283 | DOI
[25] Mathematical textbook of deformable neuroanatomies, Proc. Natl. Acad. Sci. USA, Volume 90 (1993) no. 24, pp. 11944-11948 | DOI | Zbl
[26] An extended and integrated digital image correlation technique applied to the analysis of fractured samples: The equilibrium gap method as a mechanical filter, Eur. J. Comput. Mech., Volume 18 (2009) no. 3–4, pp. 285-306 | DOI | Zbl
[27] Mastering Calculations in Linear and Nonlinear Mechanics, Mechanical Engineering Series, Springer Science, New York, 2005
[28] Ch. 15—Errors, recovery processes, and error estimates, The Finite Element Method: its Basis and Fundamentals, Elsevier, Amsterdam, 2013, pp. 493-543
[29] Complete mechanical regularization applied to digital image and volume correlation, Comput. Methods Appl. Mech. Eng., Volume 355 (2019), pp. 27-43 | DOI | MR | Zbl
[30] Finite element discretization methods for velocity-pressure and stream function formulations of surface Stokes Equations, SIAM J. Sci. Comput., Volume 44 (2022) no. 4, p. A1807-A1832 | DOI | MR | Zbl
[31] Conception Optimale Des Structures, Mathématiques & Applications, 58, Springer, Berlin, 2007
[32] Classic and inverse compositional Gauss–Newton in global DIC, Int. J. Numer. Methods Eng., Volume 119 (2019) no. 6, pp. 453-468 | DOI | MR
[33] Large deformation isotropic elasticity: on the correlation of theory and experiment for compressible rubberlike solids, Proc. R. Soc. Lond. A. Math. Phys. Sci., Volume 328 (1972) no. 1575, pp. 567-583 | Zbl
[34] Sur Les Lois de Comportement En Élasticité Non-Linéaire Compressible, C. R. Acad. Sci. Sér. II, Volume 295 (1982), pp. 423-426 | Zbl
[35] Numerical methods for nonlinear elasticity, Handbook of Numerical Analysis, Volume 3, Elsevier, 1994, pp. 465-622 | DOI | Zbl
[36] A relaxed growth modeling framework for controlling growth-induced residual stresses, Clin. Biomech., Volume 70 (2019), pp. 270-277 | DOI
[37] Numerical Recipes: The Art of Scientific Computing, Cambridge University Press, Cambridge, NY, 2007
[38] Global 2D digital image correlation for motion estimation in a finite element framework: a variational formulation and a regularized, pyramidal, multi-grid implementation, Int. J. Numer. Methods Eng., Volume 96 (2013) no. 12, pp. 739-762 | DOI | Zbl
[39] Dolfin_warp, 2023 (Zenodo) | DOI
[40] Automated Solution of Differential Equations by the Finite Element Method: The FEniCS Book (A. Logg; K.-A. Mardal; G. Wells, eds.), Lecture Notes in Computational Science and Engineering, 84, Springer, Heidelberg, 2012 | DOI | Zbl
[41] The FEniCS project version 1.5, Arch. Numer. Softw., Volume 3, 2015 no. 100, pp. 9-23 | DOI
[42] The Visualization Toolkit: An Object-Oriented Approach to 3D Graphics, Kitware, Inc., Clifton Park, NY, 2006
[43] N-DEG-paper-demos, 2023 (Zenodo) | DOI
[44] Quantification of left ventricular strain and torsion by joint analysis of 3D tagging and cine MR images, Med. Image Anal., Volume 82 (2022), 102598 | DOI
[45] Accelerated whole-heart 3D CSPAMM for myocardial motion quantification, Magn. Reson. Med., Volume 59 (2008) no. 4, pp. 755-763 | DOI
[46] Distribution of normal human left ventricular myofiber stress at end diastole and end systole: a target for in silico design of heart failure treatments, J. Appl. Phys., Volume 117 (2014), pp. 142-152 | DOI
[47] Computational quantification of patient-specific changes in ventricular dynamics associated with pulmonary hypertension, Am. J. Physiol. Heart Circ. Physiol., Volume 317 (2019) no. 6, p. H1363-H1375 | DOI
[48] Comparison of optimization parametrizations for regional lung compliance estimation using personalized pulmonary poromechanical modeling, Biomech. Model. Mechanobiol., Volume 22 (2023), pp. 1541-1554 | DOI
[49] Estimation of elastoplastic parameters via weighted FEMU and integrated-DIC, Exp. Mech., Volume 55 (2015) no. 1, pp. 105-119 | DOI
[50] Overview of identification methods of mechanical parameters based on full-field measurements, Exp. Mech., Volume 48 (2008) no. 4, pp. 381-402 | DOI
[51] Machine learning in cardiovascular magnetic resonance: basic concepts and applications, J. Cardiovasc. Magn. Resonan., Volume 21 (2019) no. 1, pp. 1-14
[52] Applications of artificial intelligence/machine learning approaches in cardiovascular medicine: a systematic review with recommendations, Eur. Heart J. - Digital Health, Volume 2 (2021) no. 3, pp. 424-436 | DOI
[53] Self-supervised motion descriptor for cardiac phase detection in 4D CMR based on discrete vector field estimations, Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers (O. Camara; E. Puyol-Antón; C. Qin; M. Sermesant; A. Suinesiaputra; S. Wang; A. Young, eds.), Volume 13593, Springer Nature, Switzerland, 2022, pp. 65-78 | DOI
[54] WarpPINN: Cine-MR image registration with physics-informed neural networks, Med. Image Anal., Volume 89 (2023), 102925 | DOI
[55] In-silico study of accuracy and precision of left-ventricular strain quantification from 3D tagged MRI, PLoS One, Volume 16 (2021) no. 11, e0258965 | DOI
[56] Mechanical and imaging models-based image registration, VipIMAGE 2019 (J. M. R. S. Tavares; R. M. Natal Jorge, eds.), Volume 34, Springer International Publishing, Cham, 2019, pp. 77-85 | DOI
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