A. P. James and B. V. Dasarathy, Medical image fusion: A survey of the state of the art, Information Fusion, vol.384, pp.4-19, 2014.

G. Kiss, A. Thorstensen, B. Amundsen, P. Claus, J. D'hooge et al., Fusion of 3d 386 echocardiographic and cardiac magnetic resonance volumes, IEEE International, vol.387, 2012.

, Ultrasonics Symposium, p.388, 2012.

S. Klein, J. P. Pluim, M. Staring, and M. A. Viergever, Adaptive stochastic gradient descent 389 optimisation for image registration, International journal of computer vision, vol.81, issue.3, p.227

Y. L. Ma, G. P. Penney, C. A. Rinaldi, M. Cooklin, R. Razavi et al., Echocar-392 diography to magnetic resonance image registration for use in image-guided cardiac 393 catheterization procedures, Physics in medicine and biology, vol.54, issue.16, p.394, 2009.

Y. Mizuguchi, Y. Oishi, H. Miyoshi, A. Iuchi, N. Nagase et al., The functional role 395 of longitudinal, circumferential, and radial myocardial deformation for regulating the early 396 impairment of left ventricular contraction and relaxation in patients with cardiovascular 397 risk factors: a study with two-dimensional strain imaging, Journal of the American Society, vol.398, issue.10, pp.1138-1144, 2008.

S. F. Nagueh, S. M. Chang, F. Nabi, D. J. Shah, and J. D. Estep, Imaging to diagnose and 400 manage patients in heart failure with reduced ejection fraction, Circulation: Cardiovascular 401 Imaging, vol.10, issue.4, p.5615, 2017.

F. J. Olsen, L. Bertelsen, M. C. De-knegt, T. E. Christensen, and N. Vejlstrup, , p.403

J. H. Jensen, J. S. Biering-sorensen, and T. , Multimodality cardiac imaging for the assessment 404 of left atrial function and the association with atrial arrhythmias, Circulation: Cardiovas-405 cular Imaging, vol.9, issue.10, p.4947, 2016.

D. Perperidis, R. H. Mohiaddin, and D. Rueckert, Spatio-temporal free-form registration 407 of cardiac mr image sequences, Medical image analysis, vol.9, issue.5, p.408, 2005.

E. Puyol-anton, M. Sinclair, B. Gerber, M. S. Amzulescu, H. Langet et al., , p.409

P. Aljabar, P. Piro, and A. P. King, A multimodal spatiotemporal cardiac motion atlas from 410 mr and ultrasound data, Medical image analysis, vol.40, p.411, 2017.

O. Szasz, On products of summability methods, Proceedings of the American Mathe-412 matical Society, vol.3, issue.2, pp.257-263, 1952.

S. Thorpe, D. Fize, and C. Marlot, Speed of processing in the human visual system, nature, vol.414, issue.6582, p.520, 1996.

C. Tobon-gomez, M. De-craene, K. Mcleod, L. Tautz, W. Shi et al., , p.416

A. Prakosa, H. Wang, G. Carr-white, and S. Kapetanakis, Benchmarking framework for 417 myocardial tracking and deformation algorithms: An open access database, Medical image, vol.418, issue.6, pp.632-648, 2013.

E. R. Valsangiacomo-buechel and L. L. Mertens, Imaging the right heart: the use of inte-420 grated multimodality imaging, European heart journal, vol.33, issue.8, p.421, 2012.

P. A. Yushkevich, J. Piven, H. C. Hazlett, R. G. Smith, S. Ho et al., , p.422

, User-guided 3d active contour segmentation of anatomical structures: significantly im-423 proved efficiency and reliability, Neuroimage, vol.31, issue.3, p.424, 2006.

Y. Zhai and M. Shah, Visual attention detection in video sequences using spatiotemporal 425 cues, Proceedings of the 14th ACM international conference on Multimedia, vol.824, pp.815-426, 2006.

W. Zhang, J. A. Noble, and J. M. Brady, Adaptive non-rigid registration of real time 3d 431 ultrasound to cardiovascular mr images, Biennial International Conference on Infor-432 mation Processing in Medical Imaging, p.433, 2007.

N. Zhao, A. Basarab, D. Kouamé, and J. Y. Tourneret, Joint segmentation and decon-434 volution of ultrasound images using a hierarchical Bayesian model based on generalized 435

, Gaussian priors. IEEE transactions on Image Processing, vol.25, pp.3736-3750, 2016.

A. Atehortúa, M. Garreau, and E. Romero, Fusion of 4D echocardiography and cine 437 cardiac magnetic resonance volumes using a salient spatio-temporal analysis, 13th In-438 ternational Conference on Medical Information Processing and Analysis, vol.10572, p.440, 2017.

S. Klein, M. Staring, K. Murphy, . Viergever, and J. Ma-&-pluim, elastix: A toolbox for 441 intensity-based medical image registration, IEEE Trans Med Imag, p.442, 2010.

T. Kanai, N. Kadoya, K. Ito, Y. Onozato, S. Y. Cho et al., Eval-443 uation of accuracy of B-spline transformation-based deformable image registration with 444 different parameter settings for thoracic images, Journal of radiation research, vol.55, issue.6, 2014.

Y. Ou, H. Akbari, M. Bilello, X. Da, and C. Davatzikos, Comparative evaluation of 447 registration algorithms in different brain databases with varying difficulty: results and 448 insights, IEEE transactions on medical imaging, vol.33, issue.10, p.449, 2014.

S. Klein, M. Staring, and J. P. Pluim, Evaluation of optimization methods for nonrigid 450 medical image registration using mutual information and B-splines, IEEE transactions on 451 image processing, vol.16, pp.2879-2890, 2007.

D. Rueckert, L. I. Sonoda, C. Hayes, D. L. Hill, M. O. Leach et al.,

, Nonrigid registration using free-form deformations: application to breast MR images, IEEE 454 transactions on medical imaging, vol.18, issue.8, pp.712-721, 1999.