1. Personalised Electromechanical Model of the Heart for the Prediction of the Acute Effects of Cardiac Resynchronisation Therapy M. Sermesant 1,3, F. Billet 1, R. Chabiniok 2, T. Mansi 1, P. Chinchapatnam 4, P. Moireau 2, J.-M. Peyrat 1, K. Rhode 3, M. Ginks 3, P. Lambiase 6, S. Arridge 4, H. Delingette 1, M. Sorine 7, C.A. Rinaldi 5, D. Chapelle 2, R. Razavi 3, and N. Ayache 1 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

2. Cardiac data Personalisation electro-physiology Cardiac modeling solid mechanics Clinical applications Diagnosis Therapy planning blood flow perfusion & metabolism anatomy Personalised and predictive medicine Personalisation: patient-specific parameter estimation

3. 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.

4. Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Cine MRI Cine MRI Endocardial Mapping Endocardial Mapping Anatomical MRI Anatomical MRI Personalised Electrophysiology Personalised Anatomy Personalised Anatomy Personalised Kinematics Personalised Mechanics Data: Pressure Catheter Pressure Catheter  Geometry  Fibres  Geometry  Fibres  Conductivity  Isochrones  Conductivity  Isochrones  Contours  Motion  Contours  Motion  Contractility  Stress  Contractility  Stress Output: Method: Result: Personalised Models for Cardiac Resynchronisation Therapy

5. Clinical Data: XMR Suite Clinical case presented:  Sixty year old woman with NYHA class III symptoms  Dilated cardiomyopathy + non- viable 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

6. XMR System at King’s College London T M1M1 M2M2 M3M3 3D Image Space X-ray Table Space X-ray C-arm space 2D Image Space Scanner Space R*P Registration: no inherent ability Overall registration transform: composed of a series of stages Calibration + tracking during intervention 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 real- time XMR guided cardiovascular intervention. IEEE Transactions on Medical Imaging, 24(11): 1428-40, 2005. 1 3 2 5 4 7 6 8 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.

7. Clinical MR images 3D+t Cine3D Late Enhancement

8. XMR Fusion of Clinical Data: 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 real- time XMR guided cardiovascular intervention. IEEE Transactions on Medical Imaging, 24(11): 1428-40, 2005. Endocardial Mapping Scars MRI ms

9. Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Cine MRI Cine MRI Endocardial Mapping Endocardial Mapping Anatomical MRI Anatomical MRI Personalised Electrophysiology Personalised Anatomy Personalised Anatomy Personalised Kinematics Personalised Mechanics Data: Pressure Catheter Pressure Catheter  Geometry  Fibres  Geometry  Fibres  Conductivity  Isochrones  Conductivity  Isochrones  Contours  Motion  Contours  Motion  Contractility  Stress  Contractility  Stress Output: Method: Result: Application to Cardiac Resynchronisation Therapy

10. Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Cine MRI Cine MRI Endocardial Mapping Endocardial Mapping Anatomical MRI Anatomical MRI Personalised Electrophysiology Personalised Anatomy Personalised Anatomy Personalised Kinematics Personalised Mechanics Data: Pressure Catheter Pressure Catheter  Geometry  Fibres  Geometry  Fibres  Conductivity  Isochrones  Conductivity  Isochrones  Contours  Motion  Contours  Motion  Contractility  Stress  Contractility  Stress Output: Method: Result: Application to Cardiac Resynchronisation Therapy

11. Personalised Anatomy Interactive Surface Generator Labelled Myocardial Volumetric Mesh Scars Segmentation done with

12. 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 dtMRI Statistical Analysis Personalised Anatomy Mean Structure

13. Statistical atlas of cardiac fibre architecture registered to patient anatomy Personalised Anatomy

14. Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Cine MRI Cine MRI Endocardial Mapping Endocardial Mapping Anatomical MRI Anatomical MRI Personalised Electrophysiology Personalised Anatomy Personalised Anatomy Personalised Kinematics Personalised Mechanics Data: Pressure Catheter Pressure Catheter  Geometry  Fibres  Geometry  Fibres  Conductivity  Isochrones  Conductivity  Isochrones  Contours  Motion  Contours  Motion  Contractility  Stress  Contractility  Stress Output: Method: Result: Application to Cardiac Resynchronisation Therapy

15. Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Cine MRI Cine MRI Endocardial Mapping Endocardial Mapping Anatomical MRI Anatomical MRI Personalised Electrophysiology Personalised Anatomy Personalised Anatomy Personalised Kinematics Personalised Mechanics Data: Pressure Catheter Pressure Catheter  Geometry  Fibres  Geometry  Fibres  Conductivity  Isochrones  Conductivity  Isochrones  Contours  Motion  Contours  Motion  Contractility  Stress  Contractility  Stress Output: Method: Result: Application to Cardiac Resynchronisation Therapy

16. 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,.. T: Depolarisation time c 0, k, D: speed parameters For CRT, the main electrophysiology feature is the activation time, the model is chosen accordingly  Eikonal-Diffusion Model

17. Fast-Marching Method: solves very efficiently Eikonal equation: Anisotropic Propagation new algorithm even for high anisotropy Add curvature effect to correct equation second term 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 Fast Electrophysiology Models 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

18. 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 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.

19. Baseline Electrophysiology Personalisation Measured Endocardial Isochrones Adjusted Volumetric Isochrones Endocardial Isochrones Error (QRS error = 12 ms)

20. Personalised Electrophysiology Final Parameter Map

21. Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Cine MRI Cine MRI Endocardial Mapping Endocardial Mapping Anatomical MRI Anatomical MRI Personalised Electrophysiology Personalised Anatomy Personalised Anatomy Personalised Kinematics Personalised Mechanics Data: Pressure Catheter Pressure Catheter  Geometry  Fibres  Geometry  Fibres  Conductivity  Isochrones  Conductivity  Isochrones  Contours  Motion  Contours  Motion  Contractility  Stress  Contractility  Stress Output: Method: Result: Application to Cardiac Resynchronisation Therapy

22. Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Cine MRI Cine MRI Endocardial Mapping Endocardial Mapping Anatomical MRI Anatomical MRI Personalised Electrophysiology Personalised Anatomy Personalised Anatomy Personalised Kinematics Personalised Mechanics Data: Pressure Catheter Pressure Catheter  Geometry  Fibres  Geometry  Fibres  Conductivity  Isochrones  Conductivity  Isochrones  Contours  Motion  Contours  Motion  Contractility  Stress  Contractility  Stress Output: Method: Result: Application to Cardiac Resynchronisation Therapy

23. 3D Electromechanical Model Law of dynamics: boundary pressures θ = model parameters positionvelocityacceleration mass dampingstiffness Blood pressure forces Contraction forces Controlled by u Boundary forces State Vector How to adjust the Electromechanical Model motion to the patient motion? u=electric control (related to action potential)

24. Pro-Active Deformable Model Internal Force External Force

25. Personalised Kinematics 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. Colour encodes the contraction force intensity

26. Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Cine MRI Cine MRI Endocardial Mapping Endocardial Mapping Anatomical MRI Anatomical MRI Personalised Electrophysiology Personalised Anatomy Personalised Anatomy Personalised Kinematics Personalised Mechanics Data: Pressure Catheter Pressure Catheter  Geometry  Fibres  Geometry  Fibres  Conductivity  Isochrones  Conductivity  Isochrones  Contours  Motion  Contours  Motion  Contractility  Stress  Contractility  Stress Output: Method: Result: Application to Cardiac Resynchronisation Therapy

27. Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Cine MRI Cine MRI Endocardial Mapping Endocardial Mapping Anatomical MRI Anatomical MRI Personalised Electrophysiology Personalised Anatomy Personalised Anatomy Personalised Kinematics Personalised Mechanics Data: Pressure Catheter Pressure Catheter  Geometry  Fibres  Geometry  Fibres  Conductivity  Isochrones  Conductivity  Isochrones  Contours  Motion  Contours  Motion  Contractility  Stress  Contractility  Stress Output: Method: Result: Application to Cardiac Resynchronisation Therapy

28. Modelling Cardiac Electromechanics Bestel-Clément-Sorine constitutive law E S series element E p parallel element E c contractile element Active nonlinear viscoelastic anisotropic and incompressible material 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.  Manual adjustment of mechanical parameters J. Sainte-Marie, D. Chapelle, R. Cimrman and M. Sorine. Modeling and estimation of the cardiac electromechanical activity. Computers & Structures, 84:1743-1759, 2006

29. Measured (solid red) and simulated (dashed blue) dP/dt curves in sinus rhythm. Measured (solid red) and simulated (dashed blue) pressure curves in sinus rhythm. Personalised Mechanics Personalised electromechanical model reproduces pressure characteristics  (dP/dt) max

30. Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Cine MRI Cine MRI Endocardial Mapping Endocardial Mapping Anatomical MRI Anatomical MRI Personalised Electrophysiology Personalised Anatomy Personalised Anatomy Personalised Kinematics Personalised Mechanics Data: Pressure Catheter Pressure Catheter  Geometry  Fibres  Geometry  Fibres  Conductivity  Isochrones  Conductivity  Isochrones  Contours  Motion  Contours  Motion  Contractility  Stress  Contractility  Stress Output: Method: Result: Application to Cardiac Resynchronisation Therapy

31. Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Predict pressure changes for different pacing conditions: in-silico optimisation of pacemaker leads locations and settings Cine MRI Cine MRI Endocardial Mapping Endocardial Mapping Anatomical MRI Anatomical MRI Personalised Electrophysiology Personalised Anatomy Personalised Anatomy Personalised Kinematics Personalised Mechanics Data: Pressure Catheter Pressure Catheter  Geometry  Fibres  Geometry  Fibres  Conductivity  Isochrones  Conductivity  Isochrones  Contours  Motion  Contours  Motion  Contractility  Stress  Contractility  Stress Output: Method: Result: Application to Cardiac Resynchronisation Therapy

32. P1TRIV Electrophysiology Personalisation Coronary sinus catheter Endocardial catheter RV catheter Measured Baseline Endocardial Isochrones Adjusted Volumetric Isochrones Measured Pacing Endocardial Isochrones Coronary sinus catheter Endocardial catheter RV catheter LBBB

33. Prediction of the Acute Effects of Pacing Baseline dP/dt Pacing dP/dt

34. Prediction of the Acute Effects of Pacing Predictions Personalisation

35. 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

36. 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

37. http://tinyurl.com/ci2bm09 Early bird before 1st August