An optimal transport approach for solving dynamic inverse problems in spaces of measures
ESAIM: Mathematical Modelling and Numerical Analysis , Tome 54 (2020) no. 6, pp. 2351-2382.

In this paper we propose and study a novel optimal transport based regularization of linear dynamic inverse problems. The considered inverse problems aim at recovering a measure valued curve and are dynamic in the sense that (i) the measured data takes values in a time dependent family of Hilbert spaces, and (ii) the forward operators are time dependent and map, for each time, Radon measures into the corresponding data space. The variational regularization we propose is based on dynamic (un-)balanced optimal transport which means that the measure valued curves to recover (i) satisfy the continuity equation, i.e., the Radon measure at time t is advected by a velocity field v and varies with a growth rate g, and (ii) are penalized with the kinetic energy induced by v and a growth energy induced by g. We establish a functional-analytic framework for these regularized inverse problems, prove that minimizers exist and are unique in some cases, and study regularization properties. This framework is applied to dynamic image reconstruction in undersampled magnetic resonance imaging (MRI), modelling relevant examples of time varying acquisition strategies, as well as patient motion and presence of contrast agents.

Reçu le :
Accepté le :
Publié le :
DOI : 10.1051/m2an/2020056
Classification : 65J20, 49J20, 35F05, 46G12, 92C55
Mots-clés : Dynamic inverse problems, optimal transport regularization, continuity equation, time dependent Bochner spaces, dynamic image reconstruction, dynamic MRI
@article{M2AN_2020__54_6_2351_0,
     author = {Bredies, Kristian and Fanzon, Silvio},
     title = {An optimal transport approach for solving dynamic inverse problems in spaces of measures},
     journal = {ESAIM: Mathematical Modelling and Numerical Analysis },
     pages = {2351--2382},
     publisher = {EDP-Sciences},
     volume = {54},
     number = {6},
     year = {2020},
     doi = {10.1051/m2an/2020056},
     mrnumber = {4174417},
     language = {en},
     url = {http://www.numdam.org/articles/10.1051/m2an/2020056/}
}
TY  - JOUR
AU  - Bredies, Kristian
AU  - Fanzon, Silvio
TI  - An optimal transport approach for solving dynamic inverse problems in spaces of measures
JO  - ESAIM: Mathematical Modelling and Numerical Analysis 
PY  - 2020
SP  - 2351
EP  - 2382
VL  - 54
IS  - 6
PB  - EDP-Sciences
UR  - http://www.numdam.org/articles/10.1051/m2an/2020056/
DO  - 10.1051/m2an/2020056
LA  - en
ID  - M2AN_2020__54_6_2351_0
ER  - 
%0 Journal Article
%A Bredies, Kristian
%A Fanzon, Silvio
%T An optimal transport approach for solving dynamic inverse problems in spaces of measures
%J ESAIM: Mathematical Modelling and Numerical Analysis 
%D 2020
%P 2351-2382
%V 54
%N 6
%I EDP-Sciences
%U http://www.numdam.org/articles/10.1051/m2an/2020056/
%R 10.1051/m2an/2020056
%G en
%F M2AN_2020__54_6_2351_0
Bredies, Kristian; Fanzon, Silvio. An optimal transport approach for solving dynamic inverse problems in spaces of measures. ESAIM: Mathematical Modelling and Numerical Analysis , Tome 54 (2020) no. 6, pp. 2351-2382. doi : 10.1051/m2an/2020056. http://www.numdam.org/articles/10.1051/m2an/2020056/

M. Aharon, M. Elad and A. Bruckstein, K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54 (2006) 4311–4322. | DOI

G. Alberti, S. Bianchini and G. Crippa, A uniqueness result for the continuity equation in two dimensions. J. Eur. Math. Soc. 16 (2014) 201–234. | DOI | MR

C.D. Aliprantis and K. Border, Infinite Dimensional Analysis. Springer-Verlag, Berlin, Heidelberg (2006). | MR

A. Alphonse, C. Elliott and B. Stinner, An abstract framework for parabolic PDEs on evolving spaces. Port. Math. 72 (2015) 1–46. | DOI | MR

L. Ambrosio, N. Fusco and D. Pallara, Functions of Bounded Variation and Free Discontinuity Problems. Oxford Science Publications. Clarendon Press (2000). | DOI | MR

L. Ambrosio, N. Gigli and G. Savaré, Gradient Flows: In Metric Spaces and in the Space of Probability Measures. Lectures in Mathematics. ETH Zürich. Birkhäuser, Basel (2006). | MR

J.-D. Benamou, Numerical resolution of an “unbalanced” mass transport problem. ESAIM: M2AN 37 (2003) 851–868. | DOI | Numdam | MR | Zbl

J.-D. Benamou and Y. Brenier, A computational fluid mechanics solution to the Monge-Kantorovich mass transfer problem. Numer. Math. 84 (2000) 375–393. | DOI | MR

C. Boyer, A. Chambolle, Y. De Castro, V. Duval, F. De Gournay and P. Weiss, On representer theorems and convex regularization. SIAM J. Optim. 29 (2019) 1260–1281. | DOI | MR

K. Bredies and M. Carioni, Sparsity of solutions for variational inverse problems with finite-dimensional data. Calc. Var. Part. Differ. Equ. 59 (2019) 14. | DOI | MR

K. Bredies, M. Carioni and S. Fanzon, A superposition principle for the inhomogeneous continuity equation with Hellinger–Kantorovich-regular coefficients. Preprint arXiv:2007.06964 (2020). | MR

K. Bredies, M. Carioni, S. Fanzon and F. Romero, On the extremal points of the ball of the Benamou-Brenier energy. Preprint arXiv:1907.11589 (2019). | MR

K. Bredies and M. Holler, Regularization of linear inverse problems with total generalized variation. J. Inverse Ill-Posed Prob. 22 (2014) 871–913. | DOI | MR

K. Bredies, K. Kunisch and T. Pock, Total generalized variation. SIAM J. Imaging Sci. 3 (2010) 492–526. | DOI | MR

K. Bredies, D.A. Lorenz and P. Maass, A generalized conditional gradient method and its connection to an iterative shrinkage method. Comput. Optim. Appl. 42 (2009) 173–193. | DOI | MR

K. Bredies and H.K. Pikkarainen, Inverse problems in spaces of measures. ESAIM: COCV 19 (2013) 190–218. | Numdam | MR | Zbl

E.J. Candès and C. Fernandez-Granda, Towards a mathematical theory of super-resolution. Commun. Pure Appl. Math. 67 (2014) 906–956. | DOI | MR

E.J. Candès, J.K. Romberg and T. Tao, Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math. 59 (2006) 1207–1223. | DOI | MR

A. Chambolle and P.-L. Lions, Image recovery via total variation minimization and related problems. Numer. Math. 76 (1997) 167–188. | DOI | MR

L. Chizat, G. Peyré, B. Schmitzer and F.-X. Vialard, Unbalanced optimal transport: dynamic and Kantorovich formulations. J. Funct. Anal. 274 (2018) 3090–3123. | DOI | MR

L. Chizat, G. Peyré, B. Schmitzer and F.-X. Vialard, An interpolating distance between optimal transport and Fisher-Rao metrics. Found. Comput. Math. 18 (2018) 1–44. | DOI | MR

M. Cuturi and G. Peyré, Computational optimal transport. Found. Trends Mach. Learn. 11 (2019) 355–607. | DOI

G. Dal Maso and R. Toader, On the Cauchy problem for the wave equation on time-dependent domains. J. Differ. Equ. 266 (2019) 3209–3246. | DOI | MR

M. Dannhauer, E. Lämmel, C.H. Wolters and T.R. Knösche, Spatio-temporal regularization in linear distributed source reconstruction from EEG/MEG: a critical evaluation. Brain Topogr. 26 (2013) 229–246. | DOI

I. Daubechies, M. Defrise and C. De Mol, An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Commun. Pure Appl. Math. 57 (2004) 1413–1457. | DOI | MR

J. Diestel, Sequences and Series in Banach Spaces. Springer-Verlag, New York (1984). | DOI | MR

J. Diestel and J. Uhl, Vector Measures. American Mathematical Society (1977). | DOI | MR

D.L. Donoho, Compressed sensing. IEEE Trans. Inform. Theory 52 (2006) 1289–1306. | DOI | MR

V. Duval and G. Peyré, Exact support recovery for sparse spikes deconvolution. Found. Comput. Math. 15 (2015) 1315–1355. | DOI | MR

H.W. Engl, M. Hanke and A. Neubauer, Regularization of Inverse Problems. Springer (2000).

L.C. Evans and R.F. Gariepy, Measure Theory and Fine Properties of Functions CRC Press (2015). | DOI | MR

A. Figalli, The optimal partial transport problem. Arch. Ratio. Mech. Anal. 195 (2010) 533–560. | DOI | MR

A. Figalli and N. Gigli, A new transportation distance between non-negative measures, with applications to gradients flows with Dirichlet boundary conditions. J. Math. Pures Appl. 94 (2010) 107–130. | DOI | MR

M. Frank and P. Wolfe, An algorithm for quadratic programming. Nav. Res. Logist. Q. 3 (1956) 95–110. | DOI | MR

J. Frikel, Sparse regularization in limited angle tomography. Appl. Comput. Harmon. Anal. 34 (2013) 117–141. | DOI | MR

W. Gangbo, W. Li, S. Osher and M. Puthawala, Unnormalized optimal transport. J. Comput. Phys. 399 (2019) 108940. | DOI | MR

M. Holler and K. Kunisch, On infimal convolution of TV-type functionals and applications to video and image reconstruction. SIAM J. Imaging Sci. 7 (2014) 2258–2300. | DOI | MR

R. Hug, E. Maitre and N. Papadakis, Multi-physics optimal transportation and image interpolation. ESAIM: M2AN 49 (2015) 1671–1692. | DOI | Numdam | MR | Zbl

J. Karlsson and A. Ringh, Generalized Sinkhorn iterations for regularizing inverse problems using optimal mass transport. SIAM J. Imaging Sci. 10 (2017) 1935–1962. | DOI | MR

F. Knoll, K. Bredies, T. Pock and R. Stollberger, Second order total generalized variation (TGV) for MRI. Magn. Reson. Med. 65 (2011) 480–491. | DOI

F. Knoll, C. Clason, K. Bredies, M. Uecker and, R. Stollberger, Parallel imaging with nonlinear reconstruction using variational penalties. Magn. Reson. Med. 67 (2012) 34–41. | DOI

S. Kondratyev, L. Monsaingeon and, D. Vorotnikov, A new optimal transport distance on the space of finite Radon measures. Adv. Differ. Equ. 21 (2016) 1117–1164. | MR

M. Liero, A. Mielke and G. Savaré, Optimal transport in competition with reaction: the Hellinger-Kantorovich distance and geodesic curves. SIAM J. Math. Anal. 48 (2016) 2869–2911. | DOI | MR

M. Liero, A. Mielke and G. Savaré, Optimal Entropy-Transport problems and a new Hellinger-Kantorovich distance between positive measures. Invent. Math. 211 (2018) 969–1117. | DOI | MR

S.G. Lingala, Y. Hu, E. Dibella and M. Jacob, Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR. IEEE T. Med. Imaging 30 (2011) 1042–1054. | DOI

D. Lombardi and E. Maitre, Eulerian models and algorithms for unbalanced optimal transport. ESAIM: M2AN 49 (2015) 1717–1744. | DOI | Numdam | MR

J. Maas, M. Rumpf, C. Schönlieb and S. Simon, A generalized model for optimal transport of images including dissipation and density modulation. ESAIM: M2AN 49 (2015) 1745–1769. | DOI | Numdam | MR | Zbl

L. Métivier, R. Brossier, Q. Mérigot, E. Oudet and J. Virieux, An optimal transport approach for seismic tomography: application to 3D full waveform inversion. Inverse Prob. 32 (2016) 115008. | DOI | MR

R. Otazo, E. Candès And D.K. Sodickson, Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components, Magn. Reson. Med. 73 (2015) 1125–1136. | DOI

N. Papadakis, G. Peyré and E. Oudet, Optimal transport with proximal splitting. SIAM J. Imaging Sci. 7 (2014) 212–238. | DOI | MR

B. Piccoli and F. Rossi, Generalized Wasserstein distance and its application to transport equations with source. Arch. Ratio. Mech. Anal. 211 (2014) 335–358. | DOI | MR | Zbl

B. Piccoli and F. Rossi, On properties of the generalized Wasserstein distance. Arch. Ratio. Mech. Anal. 222 (2016) 1339–1365. | DOI | MR | Zbl

K. Pieper and D. Walter, Linear convergence of accelerated conditional gradient algorithms in spaces of measures. Preprint arXiv:1904.09218 (2019).

K.P. Pruessmann, Encoding and reconstruction in parallel MRI. NMR Biomed. 19 (2006) 288–299. | DOI

L.I. Rudin, S. Osher and E. Fatemi, Nonlinear total variation based noise removal algorithms. Phys. D 60 (1992) 259–268. | DOI | MR

F. Santambrogio, Optimal Transport for Applied Mathematicians. Birkhäuser, Basel (2015). | DOI | MR

O. Scherzer, M. Grasmair, H. Grossauer, M. Haltmeier and F. Lenzen, Variational Methods in Imaging. Springer (2009). | MR

M. Schloegl, M. Holler, A. Schwarzl, K. Bredies and R. Stollberger, Infimal convolution of total generalized variation functionals for dynamic MRI. Magn. Reson. Med. 78 (2017) 142–155. | DOI

U. Schmitt and A.K. Louis, Efficient algorithms for the regularization of dynamic inverse problems: I. Theory. Inverse Prob. 18 (2002) 645. | DOI | MR

B. Schmitzer, K.P. Schäfers and B. Wirth, Dynamic cell imaging in PET with optimal transport regularization. IEEE T. Med. Imaging 39 (2019) 1626–1635. | DOI

T. Schuster, B. Hahn and M. Burger, Dynamic inverse problems: modelling–regularization–numerics. Inverse Prob. 34 (2018) 040301. | DOI | MR | Zbl

T. Schuster, B. Kaltenbacher, B. Hofmann and K.S. Kazimierski, Regularization Methods in Banach Spaces. Walter de Gruyter (2012). | DOI | MR

A.N. Tikhonov and V. Arsenin, Solutions of Ill-Posed Problems. Wiley (1977). | MR

A.N. Tikhonov, A.S. Leonov and A.G. Yagola, Nonlinear Ill-Posed Problems. Chapman & Hall (1998). | MR

B. Vandeghinste, B. Goossens, R.V. Holen, C. Vanhove, A. Pižurica, S. Vandenberghe and S. Staelens, Iterative CT reconstruction using shearlet-based regularization. IEEE Trans. Nucl. Sci. 60 (2013) 3305–3317. | DOI

C. Villani, Optimal Transport: Old and New. Springer, Berlin, Heidelberg (2008).

J. Weickert and C. Schnörr, Variational optic flow computation with a spatio-temporal smoothness constraint. J. Math. Imaging Vis. 14 (2001) 245–255. | DOI

Cité par Sources :