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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 DT  div DT   
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