Personalised Electromechanical Model of the Heart for the Prediction of the Acute Effects of Cardiac Resynchronisation Therapy M. Sermesant 1,3, F. Billet

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

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

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.

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


PersonalisedAnatomy

PersonalisedAnatomy

Personalised Kinematics


Personalised Mechanics


Data:Data:PressureCatheterPressureCatheter

Geometry Fibres

Geometry Fibres

Conductivity Isochrones


Contours Motion

Contours Motion

Contractility Stress


Output:Output:

Method:Method:

Result:Result:

Personalised Models for CardiacResynchronisation Therapy

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

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

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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.

Clinical MR images

3D+t Cine 3D Late Enhancement

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



CineMRICineMRI

EndocardialMapping

EndocardialMapping

AnatomicalMRI

AnatomicalMRI



PersonalisedAnatomy

PersonalisedAnatomy






Geometry Fibres

Geometry Fibres



Contours Motion

Contours Motion



Output:Output:

Method:Method:

Result:Result:

Application to CardiacResynchronisation Therapy



CineMRICineMRI

EndocardialMapping

EndocardialMapping

AnatomicalMRI

AnatomicalMRI



PersonalisedAnatomy

PersonalisedAnatomy






Geometry Fibres

Geometry Fibres



Contours Motion

Contours Motion



Output:Output:

Method:Method:

Result:Result:


Personalised Anatomy

Interactive Surface GeneratorLabelled Myocardial Volumetric Mesh

Scars

Segmentation done with

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


Mean Structure

Statistical atlas of cardiac fibre architecture registered to patient anatomy




CineMRICineMRI

EndocardialMapping

EndocardialMapping

AnatomicalMRI

AnatomicalMRI



PersonalisedAnatomy

PersonalisedAnatomy






Geometry Fibres

Geometry Fibres



Contours Motion

Contours Motion



Output:Output:

Method:Method:

Result:Result:




CineMRICineMRI

EndocardialMapping

EndocardialMapping

AnatomicalMRI

AnatomicalMRI



PersonalisedAnatomy

PersonalisedAnatomy






Geometry Fibres

Geometry Fibres



Contours Motion

Contours Motion



Output:Output:

Method:Method:

Result:Result:


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

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

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.

Baseline Electrophysiology Personalisation

Measured Endocardial Isochrones

Adjusted Volumetric Isochrones

Endocardial IsochronesError

(QRS error = 12 ms)


Final Parameter Map



CineMRICineMRI

EndocardialMapping

EndocardialMapping

AnatomicalMRI

AnatomicalMRI



PersonalisedAnatomy

PersonalisedAnatomy






Geometry Fibres

Geometry Fibres



Contours Motion

Contours Motion



Output:Output:

Method:Method:

Result:Result:




CineMRICineMRI

EndocardialMapping

EndocardialMapping

AnatomicalMRI

AnatomicalMRI



PersonalisedAnatomy

PersonalisedAnatomy






Geometry Fibres

Geometry Fibres



Contours Motion

Contours Motion



Output:Output:

Method:Method:

Result:Result:


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)

Pro-Active Deformable Model

)(, imgimg YYKuFKYYCYM

Internal Force External Force


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



CineMRICineMRI

EndocardialMapping

EndocardialMapping

AnatomicalMRI

AnatomicalMRI



PersonalisedAnatomy

PersonalisedAnatomy






Geometry Fibres

Geometry Fibres



Contours Motion

Contours Motion



Output:Output:

Method:Method:

Result:Result:




CineMRICineMRI

EndocardialMapping

EndocardialMapping

AnatomicalMRI

AnatomicalMRI



PersonalisedAnatomy

PersonalisedAnatomy






Geometry Fibres

Geometry Fibres



Contours Motion

Contours Motion



Output:Output:

Method:Method:

Result:Result:


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

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 electromechanical model reproduces pressure characteristics (dP/dt)max



CineMRICineMRI

EndocardialMapping

EndocardialMapping

AnatomicalMRI

AnatomicalMRI



PersonalisedAnatomy

PersonalisedAnatomy






Geometry Fibres

Geometry Fibres



Contours Motion

Contours Motion



Output:Output:

Method:Method:

Result:Result:




CineMRICineMRI

EndocardialMapping

EndocardialMapping

AnatomicalMRI

AnatomicalMRI



PersonalisedAnatomy

PersonalisedAnatomy






Geometry Fibres

Geometry Fibres



Contours Motion

Contours Motion



Output:Output:

Method:Method:

Result:Result:


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

Prediction of the Acute Effects of Pacing

Baseline dP/dt Pacing dP/dt

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

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

Documents

Personalised Electromechanical Model of the Heart for the Prediction of the Acute Effects of Cardiac Resynchronisation Therapy M. Sermesant 1,3, F. Billet