MRI-Based Heart and Torso Personalization for Computer Modeling and Simulation of Cardiac Electrophysiology

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10549)


In the last decade, electrophysiological models for in-silico simulations of cardiac electrophysiology have gained much attention in the research field. However, to translate them to clinical uses, the models need personalization based on recordings from the patient. In this work, we explore methodologies for the patient-specific personalization of torso and heart geometric models based on standard clinical cardiac magnetic resonance acquisitions to enable simulations. The inclusion of the torso and its internal structures allows simulations of the human ventricular electrophysiological activity from the ionic level to the body surface potentials and to the electrocardiogram.


Cardiac Electrophysiology Body Surface Potential (BSPs) EP Models Left Endocardium Inverse Problem Setting 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



EZ acknowledges the Marie Sklodowska-Curie Individual Fellowship from the H2020 EU Framework Programme for Research and Innovation (Proposal No: 655020-DTI4micro-MSCA-IF-EF-ST). AM and BR are supported by BR’s Wellcome Trust Senior Research Fellowship in Basic Biomedical Sciences, the CompBiomed project (grant agreement No 675451) and the NC3R Infrastructure for Impact award (NC/P001076/1). BV acknowledges the support of the RCUK Digital Economy Programme grant number EP/G036861/1 (Oxford Centre for Doctoral Training in Healthcare Innovation). VC was supported by ERACoSysMed through a grant to the project SysAFib - Systems medicine for diagnosis and stratification of atrial fibrillation. RA is supported by a British Heart Foundation Clinical Research Training Fellowship. VG is supported by a BBSRC grant (BB/I012117/1), an EPSRC grant (EP/J013250/1), by BHF New Horizon Grant NH/13/30238 and by the CompBiomed project (grant agreement No 675451).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Engineering Science, Institute of Biomedical EngineeringUniversity of OxfordOxfordUK
  2. 2.Department of Computer ScienceUniversity of OxfordOxfordUK
  3. 3.Division of Cardiovascular MedicineUniversity of Oxford Centre for Clinical Magnetic Resonance ResearchOxfordUK
  4. 4.Simula Research LaboratoryBærumNorway

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