Credits - ETSI · ARTreat CV/Atheroschlerosis IP preDiCT Heart/CV disease STREP ContraCancrum...

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Virtual Physiological Humans

From Medical Images to

Personalized Cardiac Models

Nicholas Ayachehttp://www-sop.inria.fr/Asclepios/

Bio-ICT: The Heart in the Computer

2 April 2009

Credits

Asclepios Team past/current members

Academic, Clinical and Industrial Partners

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Nicholas Ayache Ercim Infinity April 09

Towards Personnalized Cardiac Models

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X. PennecH. Delingette

O. ClatzG. Malandain

M. Sermesant

Project-Team Asclepios – March 2008



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Medical Imaging Today

• Large Choice of in vivo modalities• High temporal and spatial resolution• Large parameter spaces• Large Databases• Emerging Modalities/Therapies

• Quantity of information too high : requires Computer Science

200 microns

Colon crypts

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Computational Medical Image Analysis (1980 - Today)

• Assist Diagnosis• Objective quantitative measurements• fusion of multimodal, multidimensional,

multiparameter images

• Assist Therapy • Plan, simulate (before)• Control (during), follow-up (after)

J. Duncan & N. A, Medical Image Analysis, Progress over two decades and the challenges ahead, IEEE – Pami, 2000.



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Computational Models of the Human Body

Courtesy of Peter Hunter (Auckland and Oxford Universities)

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Courtesy of Peter Hunter (Auckland and Oxford Universities)

Multi physics & Multi scale



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Towards a Personalized Virtual Physiological Human

diagnosis

personalization

evolution

simulation

planning

geometrystatistics

physics…

physiology

MedicalImages

&Signalsin

viv

o ComputationalModels of

Human Organs& Pathologies

in silico

prevention

therapy

Computational Models for the Human Body, N. A. (Editor), Elsevier, July 2004. Virtual Physiological Human (VPH) European Program (2008-2012)

mul

tisca

le

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• AIMS/GOALS OF VPH-I (2008-12)• Patient-specific computer models

• ICT-based tools

• Data integration and new knowledge extraction

• Funding available: 72M EUR• 12 Collaborative projects (3 IPs/9 STREPs)

• 2 co-ordination and support actions

• 1 NoE to bring these together

The VPH Initiative (VPH – I)



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VPH call projects

Acronym Topic Project type

VPH NoE Networking NoE

VPHOP Osteoporosis IP

euHeart Heart/CV disease IP

ARTreat CV/Atheroschlerosis IP

preDiCT Heart/CV disease STREP

ContraCancrum Cancer STREP

ARCH Vascular/AVF & haemodialysis STREP

PASSPORT Liver/surgery STREP

PredictAD Alzheimers/BM & diagnosis STREP

NeoMARK Oral cancer/BM, D & T STREP

VPH2 Heart/LVD surgery STREP

IMPPACT Liver cancer/RFA therapy STREP

HAMAM Breast cancer/diagnosis STREP

Action-Grid Grid access EU Ğ LA & Balkans CA

RADICAL Security and privacy in VPH CA

1 NoE

3 IPs

9 STREPs

2 CAs

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euHeart: Giving Heart Modeling a Clinical Focus

Goals: development, personalization and validationof computational models of the heart to improve:

• Diagnosis• Treatment planning• Interventions• Design of implantable devices

5 clinical focus areas (RFA, CRT, HF, CAD, VAD)

Project coordination: Philips ResearchScientific coordination: The University of OxfordClinical coordination: King’s College London

17 partners (6 companies, 6 universities, 5 clinics)

Budget ~19M€ (~14M€ EU funding)

4 years (06.2008 – 05.2012)

© INRIA

© UOXF

© Philips Research

Courtesy ofO. Ecabert

euHeart



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euHeart: A Technical Project with a Clinical Focus

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CONFIDENTIAL Philips Research Europe - Aachen 13


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The Consortium

Universities and research institutes

• INRIA, Sophia Antipolis, FR• INSERM, Rennes, FR• University of Karlsruhe, DE• UPF, Barcelona, SP• University of Sheffield, UK• University of Oxford, UK• Amsterdam Medical Center, NL

Hospitals and clinics

• KCL, London UK• DKFZ, Heidelberg, DE• INSERM, Rennes, FR• HSCM, Madrid, SP• Amsterdam Medical Center, NL

Industrial partners• Berlin Heart, DE• HemoLab, NL• Philips Healthcare, NL & SP• Philips Research, DE• PolyDimension, DE• Volcano, BE



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Towards a Patient-Specific Computational Heart Model

Multi-level modeling

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Average structure

1. Geometry & Statistics • Heart Database (E. McVeigh, NIH)

DTI Image Statistical Analysis J.M. Peyrat, M. Sermesant, X. Pennec, H. Delingette, C. Xu, E. McVeigh, N. Ayache 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



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Fiber Tracking on the Average Cardiac DTI

http://www.inria.fr/asclepios/software/MedINRIA

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2. Cardiac Electrophysiology

Atrialdepolarisation

Ventriculardepolarisation

Ventricularrepolarisation



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2. Electrophysiological models

1. Ionic modelsHodgkin-Huxley, Luo-Rudy, Noble

2. Phenomenological models FitzHugh-Nagumo, Aliev-Panfilov, Mitchell-Schaeffer,

3. Eikonal EquationKeener, Colli-Franzone

Cellular automata

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a) Ionic modelsHodgkin-Huxley, Luo-Rudy, Noble

b) Phenomenological models FitzHugh-Nagumo, Aliev-Panfilov, Mitchell-Schaeffer,

c) Eikonal EquationKeener, Colli-Franzone

Cellular automata

PDE

FMA



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Anisotropic Fast Marching for Depolarization Fronts

40 000 elements1s/beatEpicardial Pacing

Pseudo-potentialBlue: excitable

Red: depolarisedYellow: refractory

Depolarisation FrontBlue: depolarised side

Red: excitable sideBlack: Repolarisation Front

M. Sermesant, E. Konukoglu, H. Delingette, Y. Coudiere, P. Chinchaptanam, K.S. Rhode, R. Razavi, and N. Ayache. An anisotropic multi-front fast marching method for real-time simulation of cardiac electrophysiology.In Functional Imaging and Modeling of the Heart 2007 (FIMH'07), volume 4466 of LNCS, pp 160-169, June 2007.

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u action potentialD Diffusion tensor (fiber orientation)z repolarization variable

a) Ionic modelsHodgkin-Huxley, Luo-Rudy, Noble

b) Phenomenological models FitzHugh-Nagumo, Aliev-Panfilov, Mitchell-Schaeffer,

c) Eikonal EquationKeener, Colli-Franzone

( ) ( )

( )⎪⎪⎩

⎪⎪⎨

⎧

−=∂∂

−+∇=∂∂

czubtz

zufuDdivtu

Cellular automata

PDE

FMA



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Electrical Simulation

Color : action potential u

( ) ( )

( )⎪⎪⎩

⎪⎪⎨

⎧

−=∂∂

−+∇=∂∂

czubtz

zufuDdivtu

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3. Electro-mechanical Model

Kc stiffnessu action potentialεc strainσc stress

J. Bestel, F. Clément, and M. Sorine. A Biomechanical Model of Muscle ContractionMICCAI 2001.

Inspired byrheological modelOf Hill-Maxwell

Model of Bestel-Clément-Sorinenano

micro

méso

macro

ATP

sarcomeres

fibers

organ

active non-linear viscoelastic anisotropic incompressible material.



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Electro-Mechanical Simulation

• Action potential u controls contractile element:

u > 0 : Contraction u ≤ 0 : Relaxation

• u also modifies stiffness k of the material.

Ayache-Chapelle-Clément-Coudière-Delingette- Sermesant-Sorine (FIMH’01)

M. Sermesant, H. Delingette, N. Ayache. An Electromechanical Model of the Heart for Image Analysis and Simulation. IEEE Transactions on Medical Imaging. 2006 May;25(5):612-25.

action potential u

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action potential u

Ayache-Chapelle-Clément-Coudière-Delingette- Sermesant-Sorine (FIMH’01)

M. Sermesant, H. Delingette, N. Ayache. An Electromechanical Model of the Heart for Image Analysis and Simulation. IEEE Transactions on Medical Imaging. 2006 May;25(5):612-25.

Electro-Mechanical Simulation• 4 Physiological Phases:

• Filling• Isovolumetric Contraction• Ejection• Isovolumetric Relaxation

• 2 Volumetric Conditions:

• Pressure Field in the endocardium

• Isovolumetric Constraint of myocardium



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Physiological Parameters

Color: Action

potential

D. Chapelle, P Moireau, M. Sermesant, M. Fernandez, H. Delingette: The Digital Heart – INRIA DVD

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Pathology SimulationLeft Bundle Branch Block

Slowed

9 times

D. Chapelle, P Moireau, M. Sermesant, M. Fernandez, H. Delingette: The Digital Heart – INRIA DVD



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Personalized Cardiac Models

Clinical Data• Electrophysiology• Image Motion

in silico Heart Model• Simulated Electrophysiology• Simulated Motion

Feedback

Patient Parameters• Electrical• mechanical

Compare simulation & measurements to learn model parameters

• Moreau-Villeger, Delingette, Sermesant, Mc Veigh, N.A. et al., IEEE Trans. on BioEng. 2006

•Sermesant, Peyrat, Chinchapatnam, Billet, Mansi, Rhode, Delingette, Razavi, Ayache, Toward Patient-Specific Myocardial Models of the Heart, Heart Failure Clinics, July 2008.

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In vivo clinical measurements (XMR Imaging)

Razavi R, Hill DL, Keevil SF, Miquel ME, Muthurangu V, Hegde S, Rhode K, Barnett M, van Vaals J, Hawkes DJ, Baker E. Cardiac catheterisation guided by MRI in children and adults with congenital heart disease. Lancet. 2003 Dec 6; 362(9399): 1877-82.

Reza Razavi, Guy’s and St Thomas’ Hospitals, London

Personalisation of Local Conductivity with Division of Imaging Sciences, KCL London

• Mean error ~3 ms,

•correlation with ischemic regions from late-enhancement MRI

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, in press

Measured SimulatedConductivity MapDepolarisation Isochrones

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PredictedBiV Pacing

MeasuredBiV Pacing

Prediction of BiV Pacing IsochronesJoint work with the Division of Imaging Sciences, King’s College London, UK

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 (in press).

Depolarisation Isochrones

Conductivity Map



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Minimize objective function to reproduce observed displacements:

Personalization of Mechanics

measurementsobservations from simulations

parameters state variables

I : set of instants when there are observations

• M. Sermesant, P. Moireau, O. Camara, J. Sainte-Marie, R. Andriantsimiavona, R. Cimrman, D.L.G. Hill, D. Chapelle, R. Razavi, Cardiac Function Estimation from MRI using a Heart Model and Data Assimilation: Advances and Difficulties, Medical Image Analysis, Vol 10, April 2006, pp 642-656.

• F. Billet, M. Sermesant, H. Delingette and N. Ayache: Cardiac Motion Recovery by Coupling an Electromechanical Model and Cine-MRI Data: First Steps, in Miccai’08 workshop on biomechanics, 2008.

Constraint :

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Personalized Physiological Curves on a Control

Sermesant, Peyrat, Chinchapatnam, Billet, Mansi, Rhode, Delingette, Razavi, Ayache, Toward Patient-Specific Myocardial Models of the Heart, Heart Failure Clinics, July 2008.



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Personalized Physiological Curves on a Patient with Right Ventricle

Overload

Sermesant, Peyrat, Chinchapatnam, Billet, Mansi, Rhode, Delingette, Razavi, Ayache, Toward Patient-Specific Myocardial Models of the Heart, Heart Failure Clinics, July 2008.

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Clinical dataClinical data

Simulation

3D+t Segmentation

Personalised simulationof the heart motion

Patient with Chronic Pulmonary Valve Regurgitations (repaired Tetralogy of Fallot)

3D+t MRI

LV

RV

From segmentat

Health-e-Child European Project (coordinator : Siemens)

T. Mansi et al.



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• Valve replacement : suppress regurgitations• Surgery Simulation of RV using SOFA using SOFA

framework framework www.sofa-framework.org

Simulating Surgery

T. Mansi, B. Andre, M. Lynch, M. Sermesant, H. Delingette, Y. Boudjemline, N. Ayache.Virtual Pulmonary Valve Replacement Interventions with a Personalised Cardiac Electromechanical Model, Workshop on 3D Physiological Human, in press.

Health-e-Child Project(coordinated by Siemens)

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Assessing the Effect of Surgery

Before & after surgery



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Patient with a LBBB

Sermesant M, Rhode K, Sanchez-Ortiz GI, Camara O, Andriantsimiavona R, Hegde S, Rueckert D, Lambiase P, Bucknall C, Rosenthal E, Delingette H, Hill DL, N.A., Razavi R. Simulation of cardiac pathologies using an electromechanical biventricular model and XMR interventional imaging. Med Image Anal. 2005 Oct;9(5):467-80.

Man, 68, Left Bundle Branch Block

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Simulating Resynchronization on LBBB Patient

antero septal Infarct +Left Bundle Branch Block

EF = 41%

bi-ventricular resynchronization

EF = 47%

Slowed down

3 times

standard EF = 57%

LVBlood Volume



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Resynchronization

scars

Woman, 60 years, LBBB

F. Billet, M. Sermesant, H. Delingette, and N. Ayache. Cardiac Motion Recovery by Coupling an Electromechanical Model and Cine-MRI Data: First Steps. Workshop MICCAI-2008, September 2008.

Conductivity map

Fiber map

Personalized mechanics

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Simulation of BV pacing

Depolarization isochrones

ms

Coronary sinus catheter

Endocardial catheter



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Simulation/measure of P & dP/dt

withoutpacing

withpacing

M. Sermesant, et al., Personalised Electromechanical Model of the Heart for the Prediction of the Acute Effects of Cardiac Resynchronisation Therapy, FIMH 2009.

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Medical Perspectives

• Quantitative Diagnosis• From electrical and mechanical

parameters of 3-D model

• Realistic Simulation of Therapy• Surgery• BiV Cardiac Resynchronization• RF Ablation, • etc.



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FIMH 20093-5 June

Nice, France

5th International Conference

Functional imaging and Modeling of the Heart

Philips, Siemens, Microsoft Research

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Conclusion: Virtual Physiological Patient

• Combining • in vivo digital images • in silico models of life

• Provides new tools• To analyze and simulate patient condition • To quantify diagnosis • To optimize therapy

• Computational Medicine of XXIst century….



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Thank you!

www.inria.fr/sophia/asclepios/

Google : Asclepios / FIMH 2009

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FIMH 20093-5 June

Nice, France

5th International Conference

Functional imaging and Modeling of the Heart

Philips, Siemens, Microsoft Research

Credits - ETSI · ARTreat CV/Atheroschlerosis IP preDiCT Heart/CV disease STREP ContraCancrum...

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Transcript of Credits - ETSI · ARTreat CV/Atheroschlerosis IP preDiCT Heart/CV disease STREP ContraCancrum...