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Project ID : 715093

Funded under: H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)


Electrostructural Tomography - Towards Multiparametric Imaging of Cardiac Electrical Disorders

From 2017-02-01 to 2022-01-31, ongoing project


Project details

Total cost:
EUR 1 475 000

ERC-2016-STG - ERC Starting Grant

EU contribution:
EUR 1 475 000

Call for proposal:

Coordinated in:

Funding scheme:
ERC-STG – Starting Grant










Cardiac electrical diseases are directly responsible for sudden cardiac death, heart failure and stroke. They result from a complex interplay between myocardial electrical activation and structural heterogeneity. Current diagnostic strategy based on separate electrocardiographic and imaging assessment is unable to grasp both these aspects. Improvements in personalised diagnostics are urgently needed as existing curative or preventive therapies (catheter ablation, multisite pacing, and implantable defibrillators) cannot be offered until patients are correctly recognised.

Our aim is to achieve a major advance in the way cardiac electrical diseases are characterised and thus diagnosed and treated, through the development of a novel non-invasive modality (Electrostructural Tomography), combining magnetic resonance imaging (MRI) and non-invasive cardiac mapping (NIM) technologies.

The approach will consist of: (1) hybridising NIM and MRI technologies to enable the joint acquisition of magnetic resonance images of the heart and torso and of a large array of body surface potentials within a single environment; (2) personalising the inverse problem of electrocardiography based on MRI characteristics within the heart and torso, to enable accurate reconstruction of cardiac electrophysiological maps from body surface potentials within the 3D cardiac tissue; and (3) developing a novel disease characterisation framework based on registered non-invasive imaging and electrophysiological data, and propose novel diagnostic and prognostic markers.

This project will dramatically impact the tailored management of cardiac electrical disorders, with applications for diagnosis, risk stratification/patient selection and guidance of pacing and catheter ablation therapies. It will bridge two medical fields (cardiac electrophysiology and imaging), thereby creating a new research area and a novel semiology with the potential to modify the existing classification of cardiac electrical diseases.

Host Institution


Place Pey Berland 35
33000 Bordeaux
Activity type: Higher or Secondary Education Establishments

EU contribution: EUR 1 475 000








Global EU contribution: EUR 1 475 000

Université de Bordeaux

Place Pey Berland 35
33000 Bordeaux
Activity type: Higher or Secondary Education Establishments



Domaine de Voluceau Rocquencourt
78153 Le Chesnay Cedex
Activity type: Research Organization














Principal Investigator

Prof. Hubert Cochet, MD, PhD

Cardiology, Radiology
Head of Cardiac Imaging in Bordeaux University Hospital
Head of cardiac imaging and software applications within LIRYC Electrophysiology and Heart Modeling Institute

Maxime Sermesant, PhD

Research Scientist
Cardiac modelling, machine learning, medical imaging



  1. Takigawa M, Duchateau J, Sacher F, Martin R, Vlachos K, Kitamura T, Sermesant M, Cedilnik N, Cheniti G, Frontera A, Thompson N, Martin C, Massoullie G, Bourier F, Lam A, Wolf M, Escande W, André C, Pambrun T, Denis A, Derval N, Hocini M, Haissaguerre M, Cochet H, Jaïs P. Are wall thickness channels defined by computed tomography predictive of isthmuses of postinfarction ventricular tachycardia? Heart Rhythm. 2019 Jun 15. pii: S1547-5271(19)30557-0. doi: 10.1016/j.hrthm.2019.06.012. (Accepted manuscript)
  1. Cheniti G, Sridi S, Sacher F, Chaumeil A, Pillois X, Takigawa M, Frontera A, Vlachos K, Martin CA, Teijeira E, Kitamura T, Lam A, Bourier F, Puyo S, Duchateau J, Denis A, Pambrun T, Chauvel R, Derval N, Laurent F, Montaudon M, Hocini M, Haissaguerre M, Jais P, Cochet H. Post-Myocardial Infarction Scar With Fat Deposition Shows Specific Electrophysiological Properties and Worse Outcome After Ventricular Tachycardia Ablation. J Am Heart Assoc. 2019 Aug 6;8(15):e012482. doi: 10.1161/JAHA.119.012482.
  1. Alexia Hennig, Marjorie Salel , Frederic Sacher, Claudia Camaioni, Soumaya Sridi, Arnaud Denis, Michel Montaudon, François Laurent, Pierre Jais, and Hubert Cochet. High-resolution three-dimensional late gadolinium-enhanced cardiac magnetic resonance imaging to identify the underlying substrate of ventricular arrhythmia. Europace. 2018 Sep 1;20(FI2):f179-f191. doi: 10.1093/europace/eux278.
  1. Cochet H, Iriart X, Allain-Nicolaï A, Camaioni C, Sridi S, Nivet H, Fournier E, Dinet ML, Jalal Z, Laurent F, Montaudon M, Thambo JB. Focal scar and diffuse myocardial fibrosis are independent imaging markers in repaired tetralogy of Fallot. Eur Heart J Cardiovasc Imaging. 2019 Sep 1;20(9):990-1003. doi: 10.1093/ehjci/jez068.
  1. Jefairi NA, Camaioni C, Sridi S, Cheniti G, Takigawa M, Nivet H, Denis A, Derval N, Mathilde Merle, Laurent F, Montaudon M, Sacher F, Hocini M, Haissaguerre M, Jais P, Cochet H. Relationship between atrial scar on cardiac magnetic resonance and pulmonary vein reconnection after catheter ablation for paroxysmal atrial fibrillation. J Cardiovasc Electrophysiol. 2019 May;30(5):727-740. doi: 10.1111/jce.13908. (Accepted manuscript)
  1. Molléro R, Pennec X, Delingette H, Ayache N, Sermesant M. Population-based priors in cardiac model personalisation for consistent parameter estimation in heterogeneous databases. Int J Numer Method Biomed Eng. 2019 Feb;35(2):e3158. doi: 10.1002/cnm.3158.
  1. Boyle PM, Hakim JB, Zahid S, Franceschi WH, Murphy MJ, Vigmond EJ, Dubois R, Haïssaguerre M, Hocini M, Jaïs P, Trayanova NA, Cochet H. Comparing Reentrant Drivers Predicted by Image-Based Computational Modeling and Mapped by Electrocardiographic Imaging in Persistent Atrial Fibrillation. Front Physiol. 2018 Apr 19;9:414. doi: 10.3389/fphys.2018.00414.
  1. Cedilnik N, Duchateau J, Dubois R, Sacher F, Jaïs P, Cochet H, Sermesant M. Fast personalized electrophysiological models from computed tomography images for ventricular tachycardia ablation planning. Europace. 2018 Nov 1;20(suppl_3):iii94-iii101. doi: 10.1093/europace/euy228.
  1. Giffard-Roisin S, Delingette H, Jackson T, Webb J, Fovargue L, Lee J, Rinaldi CA, Razavi R, Ayache N, Sermesant M. Transfer Learning From Simulations on a Reference Anatomy for ECGI in Personalized Cardiac Resynchronization Therapy. IEEE Trans Biomed Eng. 2019 Feb;66(2):343-353. doi: 10.1109/TBME.2018.2839713.
  1. Tania Bacoyannis, Julian Krebs, Nicolas Cedilnik, Hubert Cochet, and Maxime Sermesant. Deep Learning Formulation of ECGI for Data-driven Integration of Spatiotemporal Correlations and Imaging Information. In FIMH 2019 - 10th International Conference on Functional Imaging and Modeling of the Heart, volume LNCS 11504, Bordeaux, France, pages 20-28, June 2019. Springer.