A. Suinesiaputra, P. Medrano-gracia, B. Cowan, and A. Young, Big Heart Data: Advancing Health Informatics Through Data Sharing in Cardiovascular Imaging, IEEE Journal of Biomedical and Health Informatics, vol.19, issue.4, pp.1283-90, 2015.
DOI : 10.1109/JBHI.2014.2370952
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4659497

V. Klinke, S. Muzzarelli, N. Lauriers, D. Locca, G. Vincenti et al., Quality assessment of cardiovascular magnetic resonance in the setting of the European CMR registry: description and validation of standardized criteria, Journal of Cardiovascular Magnetic Resonance, vol.15, issue.1, p.55, 2013.
DOI : 10.1016/j.ehj.2004.06.037

C. Fonseca, M. Backhaus, D. Bluemke, R. Britten, J. Chung et al., The Cardiac Atlas Project???an imaging database for computational modeling and statistical atlases of the heart, Bioinformatics, vol.27, issue.16, pp.2288-95, 2011.
DOI : 10.1093/bioinformatics/btr360

P. Lancellotti, Normal reference ranges for echocardiography: do we really need more?, European Heart Journal - Cardiovascular Imaging, vol.15, issue.3, pp.253-257, 2014.
DOI : 10.1093/ehjci/jet265
URL : http://orbi.ulg.ac.be/bitstream/2268/168282/1/Eur%20Heart%20J%20Cardiovasc%20Imaging-2014-Lancellotti-253-4.pdf

W. Bai, W. Shi, A. De-marvao, T. Dawes, D. O-'regan et al., A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion, Medical Image Analysis, vol.26, issue.1, pp.133-178, 2015.
DOI : 10.1016/j.media.2015.08.009

S. Petersen, P. Matthews, F. Bamberg, D. Bluemke, J. Francis et al., Imaging in population science: cardiovascular magnetic resonance in 100,000 participants of UK Biobank - rationale, challenges and approaches, Journal of Cardiovascular Magnetic Resonance, vol.15, issue.1, p.46, 2013.
DOI : 10.1186/1532-429X-12-69

A. Ganna and E. Ingelsson, 5 year mortality predictors in 498???103 UK Biobank participants: a prospective population-based study, The Lancet, vol.386, issue.9993, pp.533-573, 2015.
DOI : 10.1016/S0140-6736(15)60175-1

T. Glatard, C. Lartizien, B. Gibaud, R. Da-silva, G. Forestier et al., A Virtual Imaging Platform for Multi-Modality Medical Image Simulation, IEEE Transactions on Medical Imaging, vol.32, issue.1, pp.110-118, 2013.
DOI : 10.1109/TMI.2012.2220154
URL : https://hal.archives-ouvertes.fr/inserm-00762497

S. Tsaftaris, A. Gooya, A. Frangi, and J. Prince, Simulation and Synthesis in Medical Imaging, MICCAI-SASHIMI, LNCS 9968, 2016.

D. Ye, D. Zikic, B. Glocker, A. Criminisi, and E. Konukoglu, Modality Propagation: Coherent Synthesis of Subject-Specific Scans with Data-Driven Regularization, MICCAI, pp.606-619, 2013.
DOI : 10.1007/978-3-642-40811-3_76

N. Cordier, H. Delingette, M. Le, and N. Ayache, Extended Modality Propagation: Image Synthesis of Pathological Cases, IEEE Transactions on Medical Imaging, vol.35, issue.12, pp.2598-608, 2016.
DOI : 10.1109/TMI.2016.2589760
URL : https://hal.archives-ouvertes.fr/hal-01343233

M. De-craene, S. Marchesseau, B. Heyde, H. Gao, M. Alessandrini et al., 3D Strain Assessment in Ultrasound (Straus): A Synthetic Comparison of Five Tracking Methodologies, IEEE Transactions on Medical Imaging, vol.32, issue.9, pp.1632-1678, 2013.
DOI : 10.1109/TMI.2013.2261823
URL : https://hal.archives-ouvertes.fr/hal-00840039

A. Prakosa, M. Sermesant, H. Delingette, S. Marchesseau, E. Saloux et al., Generation of Synthetic but Visually Realistic Time Series of Cardiac Images Combining a Biophysical Model and Clinical Images, IEEE Transactions on Medical Imaging, vol.32, issue.1, pp.99-109, 2013.
DOI : 10.1109/TMI.2012.2220375
URL : https://hal.archives-ouvertes.fr/hal-00813861

M. Alessandrini, M. De-craene, O. Bernard, S. Giffard-roisin, P. Allain et al., A Pipeline for the Generation of Realistic 3D Synthetic Echocardiographic Sequences: Methodology and Open-Access Database, IEEE Transactions on Medical Imaging, vol.34, issue.7, pp.1436-51, 2015.
DOI : 10.1109/TMI.2015.2396632
URL : https://hal.archives-ouvertes.fr/hal-01117490

Y. Zhou, M. De-craene, O. Somphone, M. Sermesant, and O. Bernard, Generation of Realistic 4D Synthetic CSPAMM Tagged MR Sequences for Benchmarking Cardiac Motion Tracking Algorithms, MICCAI- SASHIMI, pp.108-117, 2016.
DOI : 10.1007/978-3-319-46630-9_11

Q. Duan, P. Moireau, E. Angelini, D. Chapelle, and A. Laine, Simulation of 3D Ultrasound with a Realistic Electro-mechanical Model of the Heart, FIMH, pp.463-73, 2007.
DOI : 10.1007/978-3-540-72907-5_47

H. Gao, H. Choi, P. Claus, S. Boonen, S. Jaecques et al., A fast convolutionbased methodology to simulate 2-D/3-D cardiac ultrasound images, IEEE Trans Ultrason Ferroelectr Freq Control, vol.56, pp.404-413, 2009.

O. Shakil, F. Mahmood, and R. Matyal, Simulation in Echocardiography: An Ever-Expanding Frontier, Journal of Cardiothoracic and Vascular Anesthesia, vol.26, issue.3, pp.476-85, 2012.
DOI : 10.1053/j.jvca.2012.01.019

W. Segars, M. Mahesh, T. Beck, E. Frey, and B. Tsui, Realistic CT simulation using the 4D XCAT phantom, Medical Physics, vol.44, issue.8, pp.3800-3808, 2008.
DOI : 10.1016/0020-708X(82)90248-4
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2809711

P. G. Kwan, R. Evans, and A. , MRI simulation-based evaluation of image-processing and classification methods, IEEE Transactions on Medical Imaging, vol.18, issue.11, pp.1085-97, 1999.
DOI : 10.1109/42.816072

C. Tobon-gomez, F. Sukno, B. Bijnens, M. Huguet, and A. Frangi, Realistic simulation of cardiac magnetic resonance studies modeling anatomical variability, trabeculae, and papillary muscles, Magnetic Resonance in Medicine, vol.22, issue.Suppl 1, pp.280-288, 2011.
DOI : 10.1002/mrm.22621

F. Li, J. White, M. Rajchl, A. Goela, and T. Peters, Generation of Synthetic 4D Cardiac CT Images by Deformation from Cardiac Ultrasound, MICCAI-AECAI, pp.132-173, 2013.
DOI : 10.1007/978-3-642-38085-3_13

P. Clarysse, J. Tafazzoli, P. Delachartre, and P. Croisille, Simulation based evaluation of cardiac motion estimation methods in tagged-MR Image sequences, Journal of Cardiovascular Magnetic Resonance, vol.13, issue.Suppl 1, p.360, 2011.
DOI : 10.1186/1532-429X-13-S1-P360

M. Nichols, N. Townsend, P. Scarborough, and M. Rayner, European cardiovascular disease statistics. European Society of Cardiology, European Heart Network and British Heart Foundation, 2012.

J. Bax and V. Delgado, Detection of viable myocardium and scar tissue: Table 1, European Heart Journal ??? Cardiovascular Imaging, vol.16, issue.10, pp.1062-1066, 2015.
DOI : 10.1093/ehjci/jev200

B. Bijnens, P. Claus, F. Weidemann, J. Strotmann, and G. Sutherland, Investigating Cardiac Function Using Motion and Deformation Analysis in the Setting of Coronary Artery Disease, Circulation, vol.116, issue.21, pp.2453-64, 2007.
DOI : 10.1161/CIRCULATIONAHA.106.684357

M. Konstam, D. Kramer, A. Patel, M. Maron, and J. Udelson, Left Ventricular Remodeling in Heart Failure, JACC: Cardiovascular Imaging, vol.4, issue.1, pp.98-108, 2011.
DOI : 10.1016/j.jcmg.2010.10.008
URL : http://doi.org/10.1016/j.jcmg.2010.10.008

V. Sudarshan, U. Acharya, E. Ng, C. Meng, R. Tan et al., Automated Identification of Infarcted Myocardium Tissue Characterization Using Ultrasound Images: A Review, IEEE Reviews in Biomedical Engineering, vol.8, pp.86-97, 2015.
DOI : 10.1109/RBME.2014.2319854

A. Suinesiaputra, X. Zhang, P. Ablin, X. Alba, M. Alessandrini et al., Statistical shape modelling of the left ventricle: myocardial infarct classification challenge, IEEE J Biomed Health Inform, 2017.

N. Duchateau, M. De-craene, P. Allain, E. Saloux, and M. Sermesant, Infarct Localization From Myocardial Deformation: Prediction and Uncertainty Quantification by Regression From a Low-Dimensional Space, IEEE Transactions on Medical Imaging, vol.35, issue.10, pp.2340-52, 2016.
DOI : 10.1109/TMI.2016.2562181
URL : https://hal.archives-ouvertes.fr/hal-01314767

A. Prakosa, M. Sermesant, P. Allain, N. Villain, C. Rinaldi et al., Cardiac Electrophysiological Activation Pattern Estimation From Images Using a Patient-Specific Database of Synthetic Image Sequences, IEEE Transactions on Biomedical Engineering, vol.61, issue.2, pp.235-280, 2014.
DOI : 10.1109/TBME.2013.2281619
URL : https://hal.archives-ouvertes.fr/hal-00858891

C. Tobon-gomez, M. De-craene, K. Mcleod, L. Tautz, W. Shi et al., Benchmarking framework for myocardial tracking and deformation algorithms: An open access database, Personalization of a cardiac electromechanical model using reduced order unscented Kalman filtering from regional volumes Med Image Anal, pp.632-680, 2013.
DOI : 10.1016/j.media.2013.03.008
URL : https://hal.archives-ouvertes.fr/hal-00855928

S. Marchesseau, H. Delingette, M. Sermesant, M. Sorine, K. Rhode et al., Preliminary specificity study of the Bestel???Cl??ment???Sorine electromechanical model of the heart using parameter calibration from medical images, Journal of the Mechanical Behavior of Biomedical Materials, vol.20, pp.259-71, 2013.
DOI : 10.1016/j.jmbbm.2012.11.021

R. Mollero, X. Pennec, H. Delingette, N. Ayache, and M. Sermesant, A Multiscale Cardiac Model for Fast Personalisation and Exploitation, MICCAI, 2016.
DOI : 10.1007/978-3-319-46726-9_21
URL : https://hal.archives-ouvertes.fr/hal-01360908

S. Giffard-roisin, T. Jackson, L. Fovargue, J. Lee, H. Delingette et al., Non-Invasive Personalisation of a Cardiac Electrophysiology Model from Body Surface Potential Mapping, IEEE Transactions on Biomedical Engineering, pp.292-301, 2016.
DOI : 10.1109/TBME.2016.2629849
URL : https://hal.archives-ouvertes.fr/hal-01397393

D. Neumann, T. Mansi, L. Itu, B. Georgescu, E. Kayvanpour et al., A self-taught artificial agent for multi-physics computational model personalization, Medical Image Analysis, vol.34, pp.52-64, 2016.
DOI : 10.1016/j.media.2016.04.003

J. Ortiz-perez, J. Rodriguez, S. Meyers, D. Lee, C. Davidson et al., Correspondence Between the 17-Segment Model and Coronary Arterial Anatomy Using Contrast-Enhanced Cardiac Magnetic Resonance Imaging, JACC: Cardiovascular Imaging, vol.1, issue.3, pp.282-92, 2008.
DOI : 10.1016/j.jcmg.2008.01.014

M. Lorenzi, N. Ayache, G. Frisoni, X. Pennec, and A. D. , LCC-Demons: A robust and accurate symmetric diffeomorphic registration algorithm, NeuroImage, vol.81, pp.470-83, 2013.
DOI : 10.1016/j.neuroimage.2013.04.114
URL : https://hal.archives-ouvertes.fr/hal-00819895

C. Hoogendoorn, N. Duchateau, D. Sanchez-quintana, T. Whitmarsh, F. Sukno et al., A High-Resolution Atlas and Statistical Model of the Human Heart From Multislice CT, IEEE Transactions on Medical Imaging, vol.32, issue.1, pp.28-44, 2013.
DOI : 10.1109/TMI.2012.2230015

T. Heimann, P. Mountney, M. John, and R. Ionasec, Real-time ultrasound transducer localization in fluoroscopy images by transfer learning from synthetic training data, Medical Image Analysis, vol.18, issue.8, pp.1320-1328, 2014.
DOI : 10.1016/j.media.2014.04.007