personalisation: patient-specific parameter estimation
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Personalised Electromechanical Model of theHeart 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, France2 INRIA, Macs project, Le Chesnay, France
3 King's College London, Division of Imaging Sciences, London, UK4 University College London, Centre for Medical Image Computing, London, UK
5 Department of Cardiology, St Thomas' Hospital, London, UK6 The Heart Hospital, University College London Hospitals, London, UK
7 INRIA, Sysiphe project, Le Chesnay, France
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Cardiac data
Personalisationelectro-physiology
Cardiac modeling
solid mechanics
Clinical applications
Diagnosis
Therapy planning
blood flow
perfusion & metabolism
anatomy
Personalised and predictive medicine
Personalisation: patient-specific parameter estimation
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Cardiac Resynchronisation TherapyCRT 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.
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Predict pressure changes for different pacing conditions:Predict pressure changes for different pacing conditions:in-silico in-silico optimisation of pacemaker leads locations and settingsoptimisation of pacemaker leads locations and settings
Predict pressure changes for different pacing conditions:Predict pressure changes for different pacing conditions:in-silico in-silico optimisation of pacemaker leads locations and settingsoptimisation of pacemaker leads locations and settings
CineMRICineMRI
EndocardialMapping
EndocardialMapping
AnatomicalMRI
AnatomicalMRI
Personalised Electrophysiology
Personalised Electrophysiology
PersonalisedAnatomy
PersonalisedAnatomy
Personalised Kinematics
Personalised Kinematics
Personalised Mechanics
Personalised Mechanics
Data:Data:PressureCatheterPressureCatheter
Geometry Fibres
Geometry Fibres
Conductivity Isochrones
Conductivity Isochrones
Contours Motion
Contours Motion
Contractility Stress
Contractility Stress
Output:Output:
Method:Method:
Result:Result:
Personalised Models for CardiacResynchronisation Therapy
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Clinical Data: XMR Suite
Clinical case presented: Sixty year old woman with NYHA class III symptomsDilated cardiomyopathy + non-viable areas consistent with previous infarctionno flow-limiting diseaseLV Ejection fraction 30% on maximal tolerated medicationLeft bundle branch block (LBBB)
XMR = hybrid X-ray/MR imaging
Common sliding patient table
Path to MR-guided intervention
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XMR System at King’s College London
T
M1
M2
M3
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.
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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.
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Clinical MR images
3D+t Cine 3D Late Enhancement
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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
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Predict pressure changes for different pacing conditions:Predict pressure changes for different pacing conditions:in-silico in-silico optimisation of pacemaker leads locations and settingsoptimisation of pacemaker leads locations and settings
Predict pressure changes for different pacing conditions:Predict pressure changes for different pacing conditions:in-silico in-silico optimisation of pacemaker leads locations and settingsoptimisation of pacemaker leads locations and settings
CineMRICineMRI
EndocardialMapping
EndocardialMapping
AnatomicalMRI
AnatomicalMRI
Personalised Electrophysiology
Personalised Electrophysiology
PersonalisedAnatomy
PersonalisedAnatomy
Personalised Kinematics
Personalised Kinematics
Personalised Mechanics
Personalised Mechanics
Data:Data:PressureCatheterPressureCatheter
Geometry Fibres
Geometry Fibres
Conductivity Isochrones
Conductivity Isochrones
Contours Motion
Contours Motion
Contractility Stress
Contractility Stress
Output:Output:
Method:Method:
Result:Result:
Application to CardiacResynchronisation Therapy
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Predict pressure changes for different pacing conditions:Predict pressure changes for different pacing conditions:in-silico in-silico optimisation of pacemaker leads locations and settingsoptimisation of pacemaker leads locations and settings
Predict pressure changes for different pacing conditions:Predict pressure changes for different pacing conditions:in-silico in-silico optimisation of pacemaker leads locations and settingsoptimisation of pacemaker leads locations and settings
CineMRICineMRI
EndocardialMapping
EndocardialMapping
AnatomicalMRI
AnatomicalMRI
Personalised Electrophysiology
Personalised Electrophysiology
PersonalisedAnatomy
PersonalisedAnatomy
Personalised Kinematics
Personalised Kinematics
Personalised Mechanics
Personalised Mechanics
Data:Data:PressureCatheterPressureCatheter
Geometry Fibres
Geometry Fibres
Conductivity Isochrones
Conductivity Isochrones
Contours Motion
Contours Motion
Contractility Stress
Contractility Stress
Output:Output:
Method:Method:
Result:Result:
Application to CardiacResynchronisation Therapy
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Personalised Anatomy
Interactive Surface GeneratorLabelled Myocardial Volumetric Mesh
Scars
Segmentation done with
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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
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Statistical atlas of cardiac fibre architecture registered to patient anatomy
Personalised Anatomy
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Predict pressure changes for different pacing conditions:Predict pressure changes for different pacing conditions:in-silico in-silico optimisation of pacemaker leads locations and settingsoptimisation of pacemaker leads locations and settings
Predict pressure changes for different pacing conditions:Predict pressure changes for different pacing conditions:in-silico in-silico optimisation of pacemaker leads locations and settingsoptimisation of pacemaker leads locations and settings
CineMRICineMRI
EndocardialMapping
EndocardialMapping
AnatomicalMRI
AnatomicalMRI
Personalised Electrophysiology
Personalised Electrophysiology
PersonalisedAnatomy
PersonalisedAnatomy
Personalised Kinematics
Personalised Kinematics
Personalised Mechanics
Personalised Mechanics
Data:Data:PressureCatheterPressureCatheter
Geometry Fibres
Geometry Fibres
Conductivity Isochrones
Conductivity Isochrones
Contours Motion
Contours Motion
Contractility Stress
Contractility Stress
Output:Output:
Method:Method:
Result:Result:
Application to CardiacResynchronisation Therapy
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Predict pressure changes for different pacing conditions:Predict pressure changes for different pacing conditions:in-silico in-silico optimisation of pacemaker leads locations and settingsoptimisation of pacemaker leads locations and settings
Predict pressure changes for different pacing conditions:Predict pressure changes for different pacing conditions:in-silico in-silico optimisation of pacemaker leads locations and settingsoptimisation of pacemaker leads locations and settings
CineMRICineMRI
EndocardialMapping
EndocardialMapping
AnatomicalMRI
AnatomicalMRI
Personalised Electrophysiology
Personalised Electrophysiology
PersonalisedAnatomy
PersonalisedAnatomy
Personalised Kinematics
Personalised Kinematics
Personalised Mechanics
Personalised Mechanics
Data:Data:PressureCatheterPressureCatheter
Geometry Fibres
Geometry Fibres
Conductivity Isochrones
Conductivity Isochrones
Contours Motion
Contours Motion
Contractility Stress
Contractility Stress
Output:Output:
Method:Method:
Result:Result:
Application to CardiacResynchronisation Therapy
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Cardiac Cell Models
Three Main classes Biophysical Ionic ModelsNoble, Luo-Rudy, Beeler-Reuter, Fenton-Karma,... Phenomenological ModelsFitzhugh-Nagumo, Aliev-Panfilov,... Eikonal ModelsKeener, Colli-Franzone, ..
T: Depolarisation timec0, k, D: speed parameters
TDdivTDTkc t0
For CRT, the main electrophysiology feature is the activation time, the model is chosen accordingly Eikonal-Diffusion Model
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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 Models1Tc
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
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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
QRSQRSTTJ
regionsmendocardiu
ii 2
1
2
1
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.
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Baseline Electrophysiology Personalisation
Measured Endocardial Isochrones
Adjusted Volumetric Isochrones
Endocardial IsochronesError
(QRS error = 12 ms)
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Personalised Electrophysiology
Final Parameter Map
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Predict pressure changes for different pacing conditions:Predict pressure changes for different pacing conditions:in-silico in-silico optimisation of pacemaker leads locations and settingsoptimisation of pacemaker leads locations and settings
Predict pressure changes for different pacing conditions:Predict pressure changes for different pacing conditions:in-silico in-silico optimisation of pacemaker leads locations and settingsoptimisation of pacemaker leads locations and settings
CineMRICineMRI
EndocardialMapping
EndocardialMapping
AnatomicalMRI
AnatomicalMRI
Personalised Electrophysiology
Personalised Electrophysiology
PersonalisedAnatomy
PersonalisedAnatomy
Personalised Kinematics
Personalised Kinematics
Personalised Mechanics
Personalised Mechanics
Data:Data:PressureCatheterPressureCatheter
Geometry Fibres
Geometry Fibres
Conductivity Isochrones
Conductivity Isochrones
Contours Motion
Contours Motion
Contractility Stress
Contractility Stress
Output:Output:
Method:Method:
Result:Result:
Application to CardiacResynchronisation Therapy
![Page 22: Personalisation: patient-specific parameter estimation](https://reader037.vdocuments.net/reader037/viewer/2022103101/5681449d550346895db14dc2/html5/thumbnails/22.jpg)
Predict pressure changes for different pacing conditions:Predict pressure changes for different pacing conditions:in-silico in-silico optimisation of pacemaker leads locations and settingsoptimisation of pacemaker leads locations and settings
Predict pressure changes for different pacing conditions:Predict pressure changes for different pacing conditions:in-silico in-silico optimisation of pacemaker leads locations and settingsoptimisation of pacemaker leads locations and settings
CineMRICineMRI
EndocardialMapping
EndocardialMapping
AnatomicalMRI
AnatomicalMRI
Personalised Electrophysiology
Personalised Electrophysiology
PersonalisedAnatomy
PersonalisedAnatomy
Personalised Kinematics
Personalised Kinematics
Personalised Mechanics
Personalised Mechanics
Data:Data:PressureCatheterPressureCatheter
Geometry Fibres
Geometry Fibres
Conductivity Isochrones
Conductivity Isochrones
Contours Motion
Contours Motion
Contractility Stress
Contractility Stress
Output:Output:
Method:Method:
Result:Result:
Application to CardiacResynchronisation Therapy
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3D Electromechanical ModelLaw of dynamics:
boundary pressures
θ = model parameters
positionvelocityacceleration
mass damping stiffness
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)
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Pro-Active Deformable Model
)(, imgimg YYKuFKYYCYM
Internal Force External Force
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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
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Predict pressure changes for different pacing conditions:Predict pressure changes for different pacing conditions:in-silico in-silico optimisation of pacemaker leads locations and settingsoptimisation of pacemaker leads locations and settings
Predict pressure changes for different pacing conditions:Predict pressure changes for different pacing conditions:in-silico in-silico optimisation of pacemaker leads locations and settingsoptimisation of pacemaker leads locations and settings
CineMRICineMRI
EndocardialMapping
EndocardialMapping
AnatomicalMRI
AnatomicalMRI
Personalised Electrophysiology
Personalised Electrophysiology
PersonalisedAnatomy
PersonalisedAnatomy
Personalised Kinematics
Personalised Kinematics
Personalised Mechanics
Personalised Mechanics
Data:Data:PressureCatheterPressureCatheter
Geometry Fibres
Geometry Fibres
Conductivity Isochrones
Conductivity Isochrones
Contours Motion
Contours Motion
Contractility Stress
Contractility Stress
Output:Output:
Method:Method:
Result:Result:
Application to CardiacResynchronisation Therapy
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Predict pressure changes for different pacing conditions:Predict pressure changes for different pacing conditions:in-silico in-silico optimisation of pacemaker leads locations and settingsoptimisation of pacemaker leads locations and settings
Predict pressure changes for different pacing conditions:Predict pressure changes for different pacing conditions:in-silico in-silico optimisation of pacemaker leads locations and settingsoptimisation of pacemaker leads locations and settings
CineMRICineMRI
EndocardialMapping
EndocardialMapping
AnatomicalMRI
AnatomicalMRI
Personalised Electrophysiology
Personalised Electrophysiology
PersonalisedAnatomy
PersonalisedAnatomy
Personalised Kinematics
Personalised Kinematics
Personalised Mechanics
Personalised Mechanics
Data:Data:PressureCatheterPressureCatheter
Geometry Fibres
Geometry Fibres
Conductivity Isochrones
Conductivity Isochrones
Contours Motion
Contours Motion
Contractility Stress
Contractility Stress
Output:Output:
Method:Method:
Result:Result:
Application to CardiacResynchronisation Therapy
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Modelling Cardiac Electromechanics
Bestel-Clément-Sorine constitutive law
ES series elementEp parallel elementEc 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
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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
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Predict pressure changes for different pacing conditions:Predict pressure changes for different pacing conditions:in-silico in-silico optimisation of pacemaker leads locations and settingsoptimisation of pacemaker leads locations and settings
Predict pressure changes for different pacing conditions:Predict pressure changes for different pacing conditions:in-silico in-silico optimisation of pacemaker leads locations and settingsoptimisation of pacemaker leads locations and settings
CineMRICineMRI
EndocardialMapping
EndocardialMapping
AnatomicalMRI
AnatomicalMRI
Personalised Electrophysiology
Personalised Electrophysiology
PersonalisedAnatomy
PersonalisedAnatomy
Personalised Kinematics
Personalised Kinematics
Personalised Mechanics
Personalised Mechanics
Data:Data:PressureCatheterPressureCatheter
Geometry Fibres
Geometry Fibres
Conductivity Isochrones
Conductivity Isochrones
Contours Motion
Contours Motion
Contractility Stress
Contractility Stress
Output:Output:
Method:Method:
Result:Result:
Application to CardiacResynchronisation Therapy
![Page 31: Personalisation: patient-specific parameter estimation](https://reader037.vdocuments.net/reader037/viewer/2022103101/5681449d550346895db14dc2/html5/thumbnails/31.jpg)
Predict pressure changes for different pacing conditions:Predict pressure changes for different pacing conditions:in-silico in-silico optimisation of pacemaker leads locations and settingsoptimisation of pacemaker leads locations and settings
Predict pressure changes for different pacing conditions:Predict pressure changes for different pacing conditions:in-silico in-silico optimisation of pacemaker leads locations and settingsoptimisation of pacemaker leads locations and settings
CineMRICineMRI
EndocardialMapping
EndocardialMapping
AnatomicalMRI
AnatomicalMRI
Personalised Electrophysiology
Personalised Electrophysiology
PersonalisedAnatomy
PersonalisedAnatomy
Personalised Kinematics
Personalised Kinematics
Personalised Mechanics
Personalised Mechanics
Data:Data:PressureCatheterPressureCatheter
Geometry Fibres
Geometry Fibres
Conductivity Isochrones
Conductivity Isochrones
Contours Motion
Contours Motion
Contractility Stress
Contractility Stress
Output:Output:
Method:Method:
Result:Result:
Application to CardiacResynchronisation Therapy
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P1TRIV Electrophysiology Personalisation
Coronary sinus catheter
Endocardial catheter
RV catheter
Measured BaselineEndocardial Isochrones
Adjusted Volumetric Isochrones
Measured PacingEndocardial Isochrones
Coronary sinus catheter
Endocardial catheter
RV catheter
LBBB
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Prediction of the Acute Effects of Pacing
Baseline dP/dt Pacing dP/dt
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0
200
400
600
800
1000
1200
1400
B as eline Atrial R V B iVP re LVP 1(ANTLAT)
B iVs im P 1TR IV
meas ured
s imulated
Prediction of the Acute Effects of Pacing
PredictionsPredictionsPersonalisationPersonalisation
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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
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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
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http://tinyurl.com/ci2bm09 Early bird before 1st August