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Personalised Electromechanical Model of the Heart for the Prediction of the Acute Effects of Cardiac Resynchronisation Therapy M. Sermesant1,3, F. Billet1, R. Chabiniok2, T. Mansi1, P. Chinchapatnam4, P. Moireau2, J.-M. Peyrat1, K. Rhode3, M. Ginks3, P. Lambiase6, S. Arridge4, H. Delingette1, M. Sorine7, C.A. Rinaldi5, D. Chapelle2, R. Razavi3, and N. Ayache1 1 INRIA, Asclepios project, 2004 route des Lucioles, Sophia Antipolis, France 2 INRIA, Macs project, Le Chesnay, France 3 King's College London, Division of Imaging Sciences, London, UK 4 University College London, Centre for Medical Image Computing, London, UK 5 Department of Cardiology, St Thomas' Hospital, London, UK 6 The Heart Hospital, University College London Hospitals, London, UK 7 INRIA, Sysiphe project, Le Chesnay, France Personalisation: patient-specific parameter estimation Therapy planning anatomy Personalisation electro-physiology blood flow Diagnosis perfusion & metabolism Cardiac data solid mechanics Cardiac modeling Personalised and predictive medicine Clinical applications Cardiac Resynchronisation Therapy CRT has revolutionised the treatment of heart failure. However up to one third of patients receiving this CRT do not derive clinical improvement. The reasons for this are multifactorial, including: • heterogeneity of the heart failure population • inadequacy of techniques for patient selection • suboptimal positioning of the left ventricular lead • failure to optimise the device settings in order to enhance the hemodynamic response to treatment. Personalised Models for Cardiac Resynchronisation Therapy Data: Anatomical MRI Endocardial Mapping Cine MRI Pressure Catheter Method: Personalised Anatomy Personalised Electrophysiology Personalised Kinematics Personalised Mechanics Output: Geometry Fibres Result: Conductivity Isochrones Contours Motion Contractility Stress Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Clinical Data: XMR Suite Clinical case presented: Sixty year old woman with NYHA class III symptoms Dilated cardiomyopathy + nonviable areas consistent with previous infarction no flow-limiting disease LV Ejection fraction 30% on maximal tolerated medication Left bundle branch block (LBBB) XMR = hybrid X-ray/MR imaging Common sliding patient table Path to MR-guided intervention XMR System at King’s College London M1 Scanner Space 3D Image Space X-ray Table Space T 6 7 M2 R*P 4 1 5 2 3 X-ray C-arm space M3 8 2D Image Space • • • Registration: no inherent ability Overall registration transform: composed of a series of stages Calibration + tracking during intervention Overlay of MRI-derived left ventricular (LV) surface model (red) onto live X-ray fluoroscopy image (grey scale). This real-time overlay was used to guide the placement of catheters prior to the start of pacing. The catheters are: (1) St. Jude ESI balloon; (2) LV roving; (3) coronary sinus sheath; (4) coronary venous/epicardial; (5) pressure; (6) high right atrium; (7) His; and (8) right ventricle. K. Rhode, M. Sermesant, D. Brogan, S. Hegde, J. Hipwell, P. Lambiase, E. Rosenthal, C. Bucknall, S. Qureshi, J. Gill, R. Razavi, D. Hill. A system for realtime XMR guided cardiovascular intervention. IEEE Transactions on Medical Imaging, 24(11): 1428-40, 2005. Clinical MR images 3D+t Cine 3D Late Enhancement ms XMR Fusion of Clinical Data: Endocardial Mapping MRI Scars K. Rhode, M. Sermesant, D. Brogan, S. Hegde, J. Hipwell, P. Lambiase, E. Rosenthal, C. Bucknall, S. Qureshi, J. Gill, R. Razavi, D. Hill. A system for realtime XMR guided cardiovascular intervention. IEEE Transactions on Medical Imaging, 24(11): 1428-40, 2005. Application to Cardiac Resynchronisation Therapy Data: Anatomical MRI Endocardial Mapping Cine MRI Pressure Catheter Method: Personalised Anatomy Personalised Electrophysiology Personalised Kinematics Personalised Mechanics Output: Geometry Fibres Result: Conductivity Isochrones Contours Motion Contractility Stress Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Application to Cardiac Resynchronisation Therapy Data: Anatomical MRI Endocardial Mapping Cine MRI Pressure Catheter Method: Personalised Anatomy Personalised Electrophysiology Personalised Kinematics Personalised Mechanics Output: Geometry Fibres Result: Conductivity Isochrones Contours Motion Contractility Stress Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Personalised Anatomy Segmentation done with Scars Interactive Surface Generator Labelled Myocardial Volumetric Mesh Personalised Anatomy dtMRI Statistical Analysis Mean Structure J.M. Peyrat, M. Sermesant, X. Pennec, H. Delingette, C. Xu, E. McVeigh, N. A. A Computational Framework for the Statistical Analysis of Cardiac Diffusion Tensors: Application to a Small Database of Canine Hearts. IEEE Transactions on Medical Imaging, 26(11):1500-1514, November 2007 Personalised Anatomy Statistical atlas of cardiac fibre architecture registered to patient anatomy Application to Cardiac Resynchronisation Therapy Data: Anatomical MRI Endocardial Mapping Cine MRI Pressure Catheter Method: Personalised Anatomy Personalised Electrophysiology Personalised Kinematics Personalised Mechanics Output: Geometry Fibres Result: Conductivity Isochrones Contours Motion Contractility Stress Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Application to Cardiac Resynchronisation Therapy Data: Anatomical MRI Endocardial Mapping Cine MRI Pressure Catheter Method: Personalised Anatomy Personalised Electrophysiology Personalised Kinematics Personalised Mechanics Output: Geometry Fibres Result: Conductivity Isochrones Contours Motion Contractility Stress Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Cardiac Cell Models Three Main classes Biophysical Ionic Models Noble, Luo-Rudy, Beeler-Reuter, Fenton-Karma,... Phenomenological Models Fitzhugh-Nagumo, Aliev-Panfilov,... Eikonal Models Keener, Colli-Franzone, .. For CRT, the main electrophysiology feature is the activation time, the model is chosen accordingly Eikonal-Diffusion Model c0 k T t DT div DT T: Depolarisation time c0, k, D: speed parameters Fast Electrophysiology Models Fast-Marching Method: solves very efficiently Eikonal equation: c T 1 • Anisotropic Propagation • • Add curvature effect to correct equation second term • • new algorithm even for high anisotropy fixed-point algorithm Implementation on unstructured grids • tetrahedral meshes Introduce repolarisation with an additional time scheme and discrete state representation of cell behaviour resting / depolarised / refractory / resting Extension of the fast-marching method E. Konukoglu, M. Sermesant, O. Clatz, J.-M. Peyrat, H. Delingette, N. Ayache. A Recursive Anisotropic Fast Marching Approach to Reaction Diffusion Equation: Application to Tumor Growth Modeling. IPMI 2007. M. Sermesant, E. Konukoglu, H. Delingette, Y. Coudière, P. Chinchapatnam, K. Rhode, R. Razavi, N. Ayache: An Anisotropic Multi-front Fast Marching Method for Real-Time Simulation of Cardiac Electrophysiology. FIMH 2007: 160-169 Electrophysiology Personalisation • Endocardial surface data to adjust myocardium volume conductivity • Onset location not in the data: LBBB Minimise combined criterion: on endocardial times to adjust sub-endocardial conductivity, with recursive domain decomposition on QRS duration to adjust mid-wall and sub-epicardial global ventricular conductivities J 1 1 T T QRS QRS i i 2 endocardium 2 regions P. Chinchapatnam, K. Rhode, M. Ginks, C.A. Rinaldi, P. Lambiase, R. Razavi, S. Arridge, M. Sermesant. Model-based Imaging of Cardiac Apparent Conductivity and Local Conduction Velocity for Diagnosis and Planning of Therapy. IEEE Transactions on Medical Imaging, 27(11):1631-1642, 2008. Baseline Electrophysiology Personalisation Measured Endocardial Isochrones Adjusted Volumetric Isochrones Endocardial Isochrones Error (QRS error = 12 ms) Personalised Electrophysiology Final Parameter Map Application to Cardiac Resynchronisation Therapy Data: Anatomical MRI Endocardial Mapping Cine MRI Pressure Catheter Method: Personalised Anatomy Personalised Electrophysiology Personalised Kinematics Personalised Mechanics Output: Geometry Fibres Result: Conductivity Isochrones Contours Motion Contractility Stress Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Application to Cardiac Resynchronisation Therapy Data: Anatomical MRI Endocardial Mapping Cine MRI Pressure Catheter Method: Personalised Anatomy Personalised Electrophysiology Personalised Kinematics Personalised Mechanics Output: Geometry Fibres Result: Conductivity Isochrones Contours Motion Contractility Stress Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings 3D Electromechanical Model Contraction forces Law of dynamics: acceleration mass velocity damping position stiffness Controlled by u Blood pressure forces Boundary forces boundary pressures State Vector θ = model parameters u=electric control (related to action potential) How to adjust the Electromechanical Model motion to the patient motion? Pro-Active Deformable Model MY CY KY F u, Kimg (Y Yimg ) Internal Force External Force Personalised Kinematics Colour encodes the contraction force intensity F. Billet, M. Sermesant, H. Delingette, and N. Ayache. Cardiac Motion Recovery by Coupling an Electromechanical Model and Cine-MRI Data: First Steps. In Proc. of the Workshop on Computational Biomechanics for Medicine III. (Workshop MICCAI-2008), September 2008. Application to Cardiac Resynchronisation Therapy Data: Anatomical MRI Endocardial Mapping Cine MRI Pressure Catheter Method: Personalised Anatomy Personalised Electrophysiology Personalised Kinematics Personalised Mechanics Output: Geometry Fibres Result: Conductivity Isochrones Contours Motion Contractility Stress Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Application to Cardiac Resynchronisation Therapy Data: Anatomical MRI Endocardial Mapping Cine MRI Pressure Catheter Method: Personalised Anatomy Personalised Electrophysiology Personalised Kinematics Personalised Mechanics Output: Geometry Fibres Result: Conductivity Isochrones Contours Motion Contractility Stress Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Modelling Cardiac Electromechanics Active nonlinear viscoelastic anisotropic and incompressible material Bestel-Clément-Sorine constitutive law ES series element Ep parallel element Ec contractile element Manual adjustment of mechanical parameters Bestel J, Clément F, Sorine M. A biomechanical model of muscle contraction. In Medical Image Computing and Computer-Assisted Intervention (MICCAI 2001), volume 2208 of LNCS, Springer. J. Sainte-Marie, D. Chapelle, R. Cimrman and M. Sorine. Modeling and estimation of the cardiac electromechanical activity. Computers & Structures, 84:1743-1759, 2006 Personalised Mechanics Measured (solid red) and simulated (dashed blue) pressure curves in sinus rhythm. Measured (solid red) and simulated (dashed blue) dP/dt curves in sinus rhythm. Personalised electromechanical model reproduces pressure characteristics (dP/dt)max Application to Cardiac Resynchronisation Therapy Data: Anatomical MRI Endocardial Mapping Cine MRI Pressure Catheter Method: Personalised Anatomy Personalised Electrophysiology Personalised Kinematics Personalised Mechanics Output: Geometry Fibres Result: Conductivity Isochrones Contours Motion Contractility Stress Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Application to Cardiac Resynchronisation Therapy Data: Anatomical MRI Endocardial Mapping Cine MRI Pressure Catheter Method: Personalised Anatomy Personalised Electrophysiology Personalised Kinematics Personalised Mechanics Output: Geometry Fibres Result: Conductivity Isochrones Contours Motion Contractility Stress Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings P1TRIV Electrophysiology Personalisation LBBB Coronary sinus catheter RV catheter Endocardial catheter Measured Baseline Endocardial Isochrones Measured Pacing Endocardial Isochrones Coronary sinus catheter RV catheter Endocardial catheter Adjusted Volumetric Isochrones Prediction of the Acute Effects of Pacing Baseline dP/dt Pacing dP/dt Prediction of the Acute Effects of Pacing 1400 1200 1000 800 meas ured 600 s imulated 400 200 0 B as eline A trial Personalisation RV B iV P re L V P 1 B iV s im P 1TR IV (A NTL A T) Predictions Perspectives • Validate on a small cohort of patients • Automatic segmentation of the myocardium in MRI • in vivo DTI for patient-specific fibre architecture • Integrate functional blocks in electrophysiology model • Validation of kinematic prediction with 3D echo • Automatic adjustment of mechanical parameters • Remodelling for chronic effects of CRT • Optimisation of pacing leads position and delays On Cardiac Modelling «The notion of a single and ultimate (cardiac) model is as useful as the idea of a universal mechanical tool for all possible repairs and servicing requirements in daily life. The ideal model will be as simple as possible and as complex as necessary for the particular question raised. » Garny, Noble, Kohl, Dimensionality in cardiac modelling, Progress in Biophysics and Molecular Biology, Volume 87, Issue1 January 2005, Pages 47-66 Biophysics of Excitable Tissues http://tinyurl.com/ci2bm09 Early bird before 1st August