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InsiliCardio

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Image based High resolution In silico Modeling of Total Cardiac Function

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 prove... ver más

31/08/2019

MEDICAL UNIVERSITY...

218K€

Presupuesto del proyecto: 218K€

Líder del proyecto

MEDIZINISCHE UNIVERSITAT GRAZ No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 3 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2019-08-31

MSCA-IF-2016: Individual Fellowships

I+D

Cerrada hace 8 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

3 Participantes

MEDICAL UNIVERSITY OF GRAZ

218.18K€ | Lider

THE REGENTS OF THE UNIVERSIT...

LOS ANGELES UCLA SANTA BARBA...

129.10K€ | Participante

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Duración del proyecto: 30 meses Fecha Inicio: 2017-02-20
Fecha Fin: 2019-08-31

Líder del proyecto

MEDIZINISCHE UNIVERSITAT GRAZ No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 218K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.

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