@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. INRIA\",\n bibdate = \"Sun Dec 4 15:51:50 2011\",\n}\n\n","author_short":["Relan, J.","Chinchapatnam, P.","Sermesant, M.","Rhode, K.","Delingette, H.","Razavi, R.","Ayache, N."],"key":"RSM:Rel2010","id":"RSM:Rel2010","bibbaseid":"relan-chinchapatnam-sermesant-rhode-delingette-razavi-ayache-coupledpersonalisationofelectrophysiologymodelsforsimulationofinducedischemicventriculartachycardia-2010","role":"author","urls":{},"downloads":0,"html":""},"search_terms":["coupled","personalisation","electrophysiology","models","simulation","induced","ischemic","ventricular","tachycardia","relan","chinchapatnam","sermesant","rhode","delingette","razavi","ayache"],"keywords":[],"authorIDs":[],"dataSources":["5HG3Kp8zRwDd7FotB"]}