Coupled personalisation of electrophysiology models for simulation of induced ischemic ventricular tachycardia. Relan, J., Chinchapatnam, P., Sermesant, M., Rhode, K., Delingette, H., Razavi, R., & Ayache, N. *Med Image Comput Comput Assist Interv*, 13(Pt 2):420--428, 2010. bibtex @Article{RSM:Rel2010,
author = "J. Relan and P. Chinchapatnam and M. Sermesant and K.
Rhode and H. Delingette and R. Razavi and N. Ayache",
title = "Coupled personalisation of electrophysiology models for
simulation of induced ischemic ventricular tachycardia.",
journal = "Med Image Comput Comput Assist Interv",
year = "2010",
volume = "13",
number = "Pt 2",
pages = "420--428",
robnote = "Despite recent efforts in cardiac electrophysiology
modelling, there is still a strong need to make
macroscopic models usable in planning and assistance of
the clinical procedures. This requires model
personalisation i.e. estimation of patient-specific model
parameters and computations compatible with clinical
constraints. Fast macroscopic models allow a quick
estimation of the tissue conductivity, but are often
unreliable in prediction of arrhythmias. On the other
side, complex biophysical models are quite expensive for
the tissue conductivity estimation, but are well suited
for arrhythmia predictions. Here we present a coupled
personalisation framework, which combines the benefits of
the two models. A fast Eikonal (EK) model is used to
estimate the conductivity parameters, which are then used
to set the parameters of a biophysical model, the
Mitchell-Schaeffer (MS) model. Additional parameters
related to Action Potential Duration (APD) and APD
restitution curves for the tissue are estimated for the MS
model. This framework is applied to a clinical dataset
provided with an hybrid X-Ray/MR imaging on an ischemic
patient. This personalised MS Model is then used for in
silico simulation of clinical Ventricular Tachycardia (VT)
stimulation protocol to predict the induction of VT. This
proof of concept opens up possibilities of using VT
induction modelling directly in the intervention room, in
order to plan the radio-frequency ablation lines. INRIA",
bibdate = "Sun Dec 4 15:51:50 2011",
}

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This requires model personalisation i.e. estimation of patient-specific model parameters and computations compatible with clinical constraints. Fast macroscopic models allow a quick estimation of the tissue conductivity, but are often unreliable in prediction of arrhythmias. On the other side, complex biophysical models are quite expensive for the tissue conductivity estimation, but are well suited for arrhythmia predictions. Here we present a coupled personalisation framework, which combines the benefits of the two models. A fast Eikonal (EK) model is used to estimate the conductivity parameters, which are then used to set the parameters of a biophysical model, the Mitchell-Schaeffer (MS) model. Additional parameters related to Action Potential Duration (APD) and APD restitution curves for the tissue are estimated for the MS model. This framework is applied to a clinical dataset provided with an hybrid X-Ray/MR imaging on an ischemic patient. This personalised MS Model is then used for in silico simulation of clinical Ventricular Tachycardia (VT) stimulation protocol to predict the induction of VT. This proof of concept opens up possibilities of using VT induction modelling directly in the intervention room, in order to plan the radio-frequency ablation lines. INRIA","bibdate":"Sun Dec 4 15:51:50 2011","bibtex":"@Article{RSM:Rel2010,\n author = \"J. Relan and P. Chinchapatnam and M. Sermesant and K.\n Rhode and H. Delingette and R. Razavi and N. Ayache\",\n title = \"Coupled personalisation of electrophysiology models for\n simulation of induced ischemic ventricular tachycardia.\",\n journal = \"Med Image Comput Comput Assist Interv\",\n year = \"2010\",\n volume = \"13\",\n number = \"Pt 2\",\n pages = \"420--428\",\n robnote = \"Despite recent efforts in cardiac electrophysiology\n modelling, there is still a strong need to make\n macroscopic models usable in planning and assistance of\n the clinical procedures. This requires model\n personalisation i.e. estimation of patient-specific model\n parameters and computations compatible with clinical\n constraints. Fast macroscopic models allow a quick\n estimation of the tissue conductivity, but are often\n unreliable in prediction of arrhythmias. On the other\n side, complex biophysical models are quite expensive for\n the tissue conductivity estimation, but are well suited\n for arrhythmia predictions. Here we present a coupled\n personalisation framework, which combines the benefits of\n the two models. A fast Eikonal (EK) model is used to\n estimate the conductivity parameters, which are then used\n to set the parameters of a biophysical model, the\n Mitchell-Schaeffer (MS) model. Additional parameters\n related to Action Potential Duration (APD) and APD\n restitution curves for the tissue are estimated for the MS\n model. This framework is applied to a clinical dataset\n provided with an hybrid X-Ray/MR imaging on an ischemic\n patient. This personalised MS Model is then used for in\n silico simulation of clinical Ventricular Tachycardia (VT)\n stimulation protocol to predict the induction of VT. This\n proof of concept opens up possibilities of using VT\n induction modelling directly in the intervention room, in\n order to plan the radio-frequency ablation lines. 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