The document discusses the Virtual Physiological Human (VPH) project, aimed at improving diagnosis and treatment of cardiovascular diseases through a network of European institutions. It highlights the integration of ICT tools and multi-scale computational models within clinical environments as key objectives to optimize healthcare delivery. The project aims to utilize advanced biomedical imaging and personalized models for better patient outcomes in various cardiac conditions.

1. ICT for a global infrastructure for health
research
VPH Models, images and personalization
World of Health IT
Barcelona, March 15-18th 2010
www.vph-noe.eu
www.aneurist.org
Alejandro F. Frangi, PhD
Center 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

2. 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

3. Virtual Physiological Human (VPH)
or the Digital Me
 A European Network of Excellence operated by 12 core EU institutions
“help support and progress
13 Core Partners
European research in
4 UK (UCL, UOXF, UNOTT, USFD)
biomedical modeling and 3 France (CNRS, INRIA, ERCIM)
simulation of the human 2 Spain (UPF, IMIM)
body.This will improve our Two important modeling issues 1 Germany1(EMBL [EBI]) Sweden (KI)
ability to predict, 1 Belgium (ULB)
diagnose and treat 1 New Zealand (UOA)
disease, and have a Model parameter personalization
dramatic impact on the
future of healthcare, the Populational inference of variability
pharmaceutical and
medical device Associate / General Members
industries.” 19 Candidate General Members
3 Candidate Associate Members
(organisations)
5 Candidate Associate Members (industry)
9 Associate Projects
www.vph-noe.eu … and growing
3

4. Exemplar from a wider initiative: VPH-I
Industry Parallel VPH projects
Grid access CA
CV/ Atheroschlerosis Liver surgery
IP STREP
Breast cancer/
Heart/ LVD surgery diagnosis STREP
STREP
Osteoporosis
Oral cancer/ BM IP
D&T STREP
Cancer
Networking STREP
Heart /CV NoE
disease STREP
Vascular/ AVF & Liver cancer/RFA
haemodialysis STREP therapy STREP
Heart /CV
disease STREP
Alzheimer's/ BM &
diagnosis STREP
Other Security and
Privacy in VPH CA Clinics

5. euHeart: Integrated and Personalized Cadiac Care
 Overall aim
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.
 Specific objectives
 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 FACT SHEET
 To develop and apply specific and general strategies for model
Project acronym: euHeart
personalisation.
 To integrate the multidisciplinary results into prototypes and to Project title Personalised & Integrated CardiacCare:
Patient-specific Cardiovascular Modelling and
carry out validation at clinical sites. Simulation for In Silico Disease Understanding &
Management and for Medical Device
 To optimise catheter and surgical interventions and tuning of devices
for better treatment delivery and clinical outcome. Number of partners: 17
Budget 19.05M€
 To collect evidence of and to quantify the clinical benefit of the EC Contribution 13.90M€
Duration 48 months
approaches developed above for prediction, accurate diagnosis, and Starting date 01/06/08
disease quantification as well as improved therapy of CVD. Contract number FP7-IST-224495

6. Focus on five clinically driven problems
Cardiac
Radiofrequency
Ablation
Simulator
specific
Patient-
Cardiac
Resynch Therapy
Heart
Aortic Disease
Valvular and
Failure
Coronary
Artery
Disease

7. The (template) clinical problem
 Cardiac resynchronization therapy (CRT)
 is a proven treatment for selected patients with
heart failure-induced conduction disturbances
and ventricular dyssynchrony
 CRT is designed to reduce symptoms and improve
cardiac function by restoring the mechanical
sequence of ventricular activation and contraction

8. Current practice and caveats in CRT
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.
8
Abraham WT. Cardiac resynchronization therapy. Prog Cardiovasc Dis. 2006;48(4):232-8.

9. 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 biomarkers
image-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

10. Integrated diagnostic &
interventional suites
MRXO: An integrated MR, CT and CathLab facility
World’s first hybrid OR for neurosurgical procedures. Tokai University, JP

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

13. GoogleHeart

14. Computational statistical atlases
Whole Heart Point Distribution Model
 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
h h Φ PCA s h
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.

15. Modeling heart’s structure
Model with meaningful structures
Human Heart Anatomy
Purkinje Fibers Arterial system Venous system

16. 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)

17. His bundle and Purkinje system
The Purkinje system Ventricles
Tawara, 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)

18. Inclusion of functional meaningful structures

19. Example: modeling helping understanding our of
mechanisms of disease and treament
19

20. 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
a) b)
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.

21. 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

22. Conclusion 2
Imaging can help model “personalization”
 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
Computational Initial and boundary Tissue types &
domain (anatomy) conditions properties
Challenge
 Information can be either structural or functional: multimodal imaging

23. 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.

24. Multimodal model-to-image adaptation/coupling
 Multi-phase segmentation (3D+t tracking)
CTA (15 phases) 3DUS (15/20 phases) MRI (20/30 phases)

25. Model-to-samples adaptation/coupling
 Model-based inference of the localization of other, non image-based,
functional structures from population to individual’s space

26. 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.

27. Population-based personalized cardiac models
Structural & Computational Computational Understanding,
functional anatomical physiological diagnostics or
data or atlases modeling modeling prognosis
Population data In vivo & in silico
and atlases Populational Inference and
Multimodal Personalized Phenotyping
image analysis condition-specific
and anatomical biophysical
Responder
Personalized model building simulations
Selection
measurements Personalization Therapy
optimization

28. 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 populational
information