Estimation of volumetric myocardial apparent conductivity from endocardial electro-anatomical mapping.
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.
Chinchapatnam
Phani
P
Centre for Medical Image Computing, University College London, Gower Street, London WC1E 6BT, UK. p.chinchapatnam@cs.ucl.ac.uk
Rhode
Kawal S
KS
Ginks
Matthew
M
Mansi
Tommaso
T
Peyrat
Jean-Marc
JM
Lambiase
Pier
P
Rinaldi
C
C
Razavi
Reza
R
Arridge
Simon
S
Sermesant
Maxime
M
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