ICT for a global infrastructure for health

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VPH Models, images and personalization

World of Health IT

Barcelona, March 15-18th 2010

Alejandro F. Frangi, PhDCenter for Computational Imaging & Simulation Technologies in Biomedicine

Universitat Pompeu Fabra, Barcelona, Spain

Networking Center on Biomedical Research – Bioengineering, Biomaterials and Nanomedicine

Institució Catalana de Recerca i Estudis Avançats

alejandro.frangi@upf.edu

www.cilab.upf.edu

www.vph-noe.eu

www.aneurist.org

Outline

The vision, the context

VPH & euHeart

The clinical application & relevance

A case study from euHeart: CRT

Computational atlases

Of anatomy and function

Interplay between imaging and modeling

Imaging trends

Modeling for imaging

Imaging for modeling

Conclusions & outlook

2

A European Network of Excellence operated by 12 core EU institutions

3

Virtual Physiological Human (VPH)or the Digital Me

www.vph-noe.eu

13 Core Partners

4 UK (UCL, UOXF, UNOTT, USFD)

3 France (CNRS, INRIA, ERCIM)

2 Spain (UPF, IMIM)

1 Germany (EMBL [EBI])

1 Sweden (KI)

1 Belgium (ULB)

1 New Zealand (UOA)

Associate / General Members

19 Candidate General Members

3 Candidate Associate Members

(organisations)

5 Candidate Associate Members (industry)

9 Associate Projects

… and growing

“help support and progress

European research in

biomedical modeling and

simulation of the human

body. This will improve our

ability to predict,

diagnose and treat

disease, and have a

dramatic impact on the

future of healthcare, the

pharmaceutical and

medical device

industries.”

Two important modeling issues

Model parameter personalization

Populational inference of variability

Exemplar from a wider initiative: VPH-I

Networking

NoE

Osteoporosis

IP

Alzheimer's/ BM &

diagnosis STREP

Heart /CV

disease STREP

Cancer

STREP

Liver surgery

STREP

Heart/ LVD surgery

STREP

Oral cancer/ BM

D&T STREP

CV/ Atheroschlerosis

IP

Breast cancer/

diagnosis STREP

Vascular/ AVF &

haemodialysis STREP

Liver cancer/RFA

therapy STREP

Security and

Privacy in VPH CA

Grid access CA

Heart /CV

disease STREP

Industry

ClinicsOther

Parallel VPH projects

euHeart: Integrated and Personalized Cadiac Care

To develop, share and integrate multi-physics and multi-level models of the heart

To develop and validate automated methods for the consistent interpretation of multi-modal clinical images

To develop and apply specific and general strategies for model personalisation.

To integrate the multidisciplinary results into prototypes and to carry out validation at clinical sites.

To optimise catheter and surgical interventions and tuning of devices for better treatment delivery and clinical outcome.

To collect evidence of and to quantify the clinical benefit of the approaches developed above for prediction, accurate diagnosis, and disease quantification as well as improved therapy of CVD.

The aim of the euHeart project is to incorporate ICT tools and integrative multi-scale computational models of the heart within clinical environments to improve diagnosis, treatment planning and interventions for CVD and thus to reduce the allied healthcare costs.

Overall aim

Specific objectives

FACT SHEET

Project acronym: euHeart

Project title Personalised & Integrated CardiacCare:

Patient-specific Cardiovascular Modelling and

Simulation for In Silico Disease Understanding &

Management and for Medical Device

Number of partners: 17

Budget 19.05M€

EC Contribution 13.90M€

Duration 48 months

Starting date 01/06/08

Contract number FP7-IST-224495

Focus on five clinically driven problems

Cardiac

Radiofrequency

Ablation

Cardiac

Resynch Therapy

Heart

Failure

Coronary

Artery

Disease

Va

lvu

lar a

nd

Ao

rtic D

ise

as

e

Pa

tien

t-

sp

ec

ific

Sim

ula

tor

The (template) clinical problem

Cardiac resynchronization therapy (CRT)

is a proven treatment for selected patients with heart failure-induced conduction disturbancesand ventricular dyssynchrony

CRT is designed to reduce symptoms and improve cardiac function by restoring the mechanical sequence of ventricular activation and contraction

8

Strickberger SA, et al. Patient selection for cardiac resynchronization therapy: from the Council on Clinical Cardiology Subcommittee on Electrocardiography and Arrhythmias and the Quality of Care and Outcomes Research Interdisciplinary Working Group, in collaboration with the Heart Rhythm Society. Circulation. 2005 Apr 26;111(16):2146-50.

Abraham WT. Cardiac resynchronization therapy. Prog Cardiovasc Dis. 2006;48(4):232-8.

Current practice and caveats in CRT

Biomedical imaging revolution trends Explosion of 3D+t multimodal diagnostic imaging to be quantified and integrated!

multimodal structural and functional imaging (MR/A, MSCT/A, 3DUS,PET/MR, SPECT/CT) additionally… physiological signal monitoring systems (CARTO, ECG, BP, etc)

Technological synergies synergistic developments in hardware & software

close cooperation between engineers, clinicians and technology providers

Beyond basic diagnostics disease understanding & image-based molecular biomarkersimage-guided therapy planning, delivery and monitoring

computerized methods: image computing, and physical modeling and simulation multimodal interventional suites

Longitudinal imaging studies clinical trials based on imaging biomarkers Need for identifying effective imaging biomarkers & high-throghput image analytics services Need for models for disease understanding and biomarker interpretation

Integrated diagnostic &

interventional suites

MRXO: An integrated MR, CT and CathLab facility

World’s first hybrid OR for neurosurgical procedures. Tokai University, JP

Integrated diagnostic & interventional suites An integrated CathLab facility with Stereotaxis Steering & Navigation

GoogleHeart

Automatically built from high-resolution scans

Multi-slice CT 3D+t scans

100 randomized subjects

15 cardiac phases each

Triangulated surfaces

All main structures included

Point Distribution Models (PDMs) learnt from the training set

Average heart & principal shape component analysis

Linear shape model

PCAhh h Φ s

Computational statistical atlasesWhole Heart Point Distribution Model

S. Ordas, E. Oubel, R. Sebastian, A.F. Frangi (2007) Computational Anatomy Atlas of the Heart;

International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 338-42.

A.F. Frangi, D. Rueckert, J.A. Schnabel, W.J. Niessen (2002). Automatic construction of multi-ple-object

three-dimensional statistical shape models: Application to cardiac modeling. IEEE Trans on Medical

Imaging. 21(9):1151-66.

Modeling heart’s structure

Human Heart Anatomy

Purkinje Fibers Arterial system Venous system

Model with meaningful structures

Myocardial fiber structure

Myocardial fibers: diffusion tensor imaging, velocity encoded MRI

Mathematical model: Streeter (1979), Helm (2005)

Warping diffusion tensors from template to subjects

Sundar et al.: principal directions of the

original DT (blue) and the mapped DT (red)

His bundle and Purkinje system

VentriclesThe Purkinje systemTawara, S., 1906. The conduction system of the

mammalian heart. An Anatomico-histological Study :

of the atrioventricular Bundle and I the Purkinje

Fibers, Verlag v. Gustav Fischer.

Myerburg, R. J., et al. 1972. Physiology of canine

intraventricular conduction and endocardial

excitation. Circ Res 30 (2)

Ansari, A., et al 1999. Distribution of the

purkinje fibres in the sheep heart. Anat Rec 254 (1),

92-97

Miquerol, L., et al. 2004. Architectural and

functional assymetry of the His-Purkinje system of

the Murine heart. Cardiovasc. Res. 63, 77-86

Oosthoek, P.W. et al, 1993.

Immunohistochemical delineation of the conduction

system II. The Atrioventricular node and Purkinje

fibers. Circ. Res. 73; 482-491

Courtesy: R. Sebastián (Universidad de Valencia)

Inclusion of functional meaningful structures

Example: modeling helping understanding our of

mechanisms of disease and treament

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Optimization of AV and VV delay in CRT

AV and VV delays have been optimized for LBBB and AV node block, using 12 different lead positions and varying the conductivity value for the myocardium

Reumann M, Farina D, Miri R, Lurz S, Osswald B, Dossel O. Computer model for the optimization of AV and VV delay in cardiac resynchronization therapy. Med Biol Eng Comput. 2007 Sep;45(9):845-54.

a) b)

Conclusion 1

Integrative models/modeling can help imaging

Common coordinate system for structural/functional data integration multimodal and multiscale information

Introduce prior knowledge in many, otherwise ill posed, problems Segmentation, motion analysis, registration, reconstruction, etc Models include: anatomical, image formation, physics, biology, biochemistry, etc.

Computational models as “virtual imaging” techniques Estimation of the non-measurable from the observable (e.g. intracavitary potentials,

intraneurysmal flows, etc) Support treatment and disease understanding Limit the use from invasive procedures (e.g. electrophysiology, haemodynamics) Models include: reduced to highly detailed structural/functional

Towards searching/navigating into a “mixed reality world” High-dimensional multimodal and multiscale space With both measured and simulated processes over time

Models have to be informed with subject-specific and condition-specific subject information (e.g. ion channels profile or cellular models connected to conditions of the patient)

Subject-specific information needs to originate in in vivo, dynamic and (preferably) non invasive signal and imaging systems

Information can be either structural or functional: multimodal imaging

Conclusion 2

Imaging can help model “personalization”

Initial and boundary

conditions

Computational

domain (anatomy)

Tissue types &

properties

Challenge

Multimodal model-to-image adaptation/coupling

Segmentation framework: SParse Active Shape Models (SPASM) Iteratively looks into the image data for new positions to deform the shape model

The solution is statistically constrained by the shape model

van Assen HC, Danilouchkine MG, Frangi AF, Ordas S, Westenberg JJ, Reiber JH, Lelieveldt BP. SPASM: a 3D-ASM for segmentation of sparse

and arbitrarily oriented cardiac MRI data. Med Image Anal. 2006 Apr;10(2):286-303.

Multi-phase segmentation (3D+t tracking)

CTA (15 phases) MRI (20/30 phases)3DUS (15/20 phases)

Multimodal model-to-image adaptation/coupling

Model-to-samples adaptation/coupling

Model-based inference of the localization of other, non image-based, functional structures from population to individual’s space

Patient-specific electromechanical model for

arrhythmia ablation within an XMR suite

Integration of MR, CathLab and Ensite information into an electromechanical modeling of the myocardium using XMR

Sermesant M, Moireau P, Camara O, Sainte-Marie J, Andriantsimiavona R, Cimrman R, Hill DL, Chapelle D, Razavi R. Cardiac function estimation from MRI using a heart model and data assimilation: advances and

difficulties. Med Image Anal. 2006 Aug;10(4):642-56.

Personalized and

condition-specific

biophysical

simulations

Population-based personalized cardiac models

Personalized

measurements

Multimodal

image analysis

and anatomical

model building

Structural &

functional

data or atlases

Population data

and atlases

Computational

anatomical

modeling

Computational

physiological

modeling

Understanding,

diagnostics or

prognosis

Responder

Selection

Therapy

optimization

In vivo & in silico

Phenotyping

Personalization

Populational Inference

Conclusions & Outlook

Populational atlases provide a means to define a subject-independent coordinate system

Statistical models provide a natural way to handle and parameterize varying dynamic anatomy

Model-to-image adaptation can be performed efficiently and cross-modality thus providing

patient-specific structural and functional information where available

and population specific where needed

Personalization goes beyond imaging integrations

Integration of multimodal physiological signals

Biophysical parameter identification from patient/populational data

Parametric inference from disease condition and other populationalinformation