title: Estimation of volumetric myocardial apparent conductivity from endocardial electro-anatomical mapping.
abstract: Estimating patient-specific electrical tissue parameters is of considerable benefit towards personalization of cardiac biophysical models. In this paper, an adaptive inverse parameter estimation algorithm is proposed to estimate the myocardial apparent conductivity from endocardial electrical potential measurements. The forward electrophysiology problem is posed as an Eikonal model and is solved using an anisotropic fast marching method. The conductivity estimation algorithm is validated on patient data obtained using hybrid X-ray/magnetic resonance imaging. Future directions towards improving such estimation procedures are also indicated.
authors: Centre for Medical Image Computing, University College London, Gower Street, London WC1E 6BT, UK. p.chinchapatnam@cs.ucl.ac.uk
authors: Phani
authors: P
authors: Chinchapatnam
authors: Kawal S
authors: KS
authors: Rhode
authors: Matthew
authors: M
authors: Ginks
authors: Tommaso
authors: T
authors: Mansi
authors: Jean-Marc
authors: JM
authors: Peyrat
authors: Pier
authors: P
authors: Lambiase
authors: C
authors: C
authors: Rinaldi
authors: Reza
authors: R
authors: Razavi
authors: Simon
authors: S
authors: Arridge
authors: Maxime
authors: M
authors: Sermesant
mesh_terms: D000465:Algorithms; D001698:Biomedical Engineering / Q000379:methods; D001703:Biophysics / Q000379:methods; D003198:Computer Simulation; D004058:Diffusion; D004594:Electrophysiology / Q000379:methods; D004699:Endocardium / Q000473:pathology*; D006321:Heart / Q000502:physiology; D006801:Humans; D008953:Models, Anatomic; D009200:Myocardial Contraction; D012815:Signal Processing, Computer-Assisted