Summary
Advances in medical imaging have enabled unprecedented ability to image
cardiac anatomy and function. So far these technologies have had relatively
modest clinical impact as the analysis of such rich multi-modal
datasets has proven challenging.
In silico models hold vast potential to better harness such datasets
by enabling their integration into quantitative frameworks that can aid
in gaining better mechanistic insight into cardiac function in health and disease,
and thus paving the way towards optimal therapeutic strategies.
Our objective is to develop the most advanced biophysically detailed
in-silico model of total electro-mechano-fluidic function of the heart.
This model will be parametrized, verified and used to study cause-effect
relationships between flow and pressure and their impact upon pumping performance.
A novel set of features such as combined models of both heart and attached
outflow vessels and the computational efficiency will provide a unique
platform for translational research.
This ambitious endeavor is feasible only by combining the expertise
of the applicant in modeling soft tissue mechanics and his supervisors
in modeling electrophysiology (Gernot Plank, MUG) and blood flow
(Shawn Shadden, UC Berkeley).
Clinical input and datasets for model parametrization and validation
are provided by Titus Kühne (DHZ Berlin) and by clinical
collaborators of Prof. Shadden at UCSF.
During the return-phase, the applicant will use the infrastructure
of Prof. Plank’s lab and the large network of academic and industrial
collaborations as an incubator for building up his own research group
in computational hemodynamics. This is ideal in many regards,
as the expertise of the applicant's group will be entirely orthogonal
to the expertise in Prof. Plank's lab, thus promoting a fast pathway
towards full indepence, and core expertise necessary
for further developing and maintaining a highly complex computing
environment is synergistically shared between the labs.
cardiac anatomy and function. So far these technologies have had relatively
modest clinical impact as the analysis of such rich multi-modal
datasets has proven challenging.
In silico models hold vast potential to better harness such datasets
by enabling their integration into quantitative frameworks that can aid
in gaining better mechanistic insight into cardiac function in health and disease,
and thus paving the way towards optimal therapeutic strategies.
Our objective is to develop the most advanced biophysically detailed
in-silico model of total electro-mechano-fluidic function of the heart.
This model will be parametrized, verified and used to study cause-effect
relationships between flow and pressure and their impact upon pumping performance.
A novel set of features such as combined models of both heart and attached
outflow vessels and the computational efficiency will provide a unique
platform for translational research.
This ambitious endeavor is feasible only by combining the expertise
of the applicant in modeling soft tissue mechanics and his supervisors
in modeling electrophysiology (Gernot Plank, MUG) and blood flow
(Shawn Shadden, UC Berkeley).
Clinical input and datasets for model parametrization and validation
are provided by Titus Kühne (DHZ Berlin) and by clinical
collaborators of Prof. Shadden at UCSF.
During the return-phase, the applicant will use the infrastructure
of Prof. Plank’s lab and the large network of academic and industrial
collaborations as an incubator for building up his own research group
in computational hemodynamics. This is ideal in many regards,
as the expertise of the applicant's group will be entirely orthogonal
to the expertise in Prof. Plank's lab, thus promoting a fast pathway
towards full indepence, and core expertise necessary
for further developing and maintaining a highly complex computing
environment is synergistically shared between the labs.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/750835 |
| Start date: | 01-03-2017 |
| End date: | 31-08-2019 |
| Total budget - Public funding: | 218 176,20 Euro - 218 176,00 Euro |
Cordis data
Original description
Advances in medical imaging have enabled unprecedented ability to imagecardiac anatomy and function. So far these technologies have had relatively
modest clinical impact as the analysis of such rich multi-modal
datasets has proven challenging.
In silico models hold vast potential to better harness such datasets
by enabling their integration into quantitative frameworks that can aid
in gaining better mechanistic insight into cardiac function in health and disease,
and thus paving the way towards optimal therapeutic strategies.
Our objective is to develop the most advanced biophysically detailed
in-silico model of total electro-mechano-fluidic function of the heart.
This model will be parametrized, verified and used to study cause-effect
relationships between flow and pressure and their impact upon pumping performance.
A novel set of features such as combined models of both heart and attached
outflow vessels and the computational efficiency will provide a unique
platform for translational research.
This ambitious endeavor is feasible only by combining the expertise
of the applicant in modeling soft tissue mechanics and his supervisors
in modeling electrophysiology (Gernot Plank, MUG) and blood flow
(Shawn Shadden, UC Berkeley).
Clinical input and datasets for model parametrization and validation
are provided by Titus Kühne (DHZ Berlin) and by clinical
collaborators of Prof. Shadden at UCSF.
During the return-phase, the applicant will use the infrastructure
of Prof. Plank’s lab and the large network of academic and industrial
collaborations as an incubator for building up his own research group
in computational hemodynamics. This is ideal in many regards,
as the expertise of the applicant's group will be entirely orthogonal
to the expertise in Prof. Plank's lab, thus promoting a fast pathway
towards full indepence, and core expertise necessary
for further developing and maintaining a highly complex computing
environment is synergistically shared between the labs.
Status
CLOSEDCall topic
MSCA-IF-2016Update Date
28-04-2024
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