Hola,
¿eres nuevo aquí?

Regístrate gratis y conecta tu empresa con financiación pública, partners y proyectos.

Tengo cuenta

Regístrate

Ver video

CMRPredict

Financiado

Cerrado

Patient specific magnetic resonance image guided biomechanical modelling of the...

Patient specific magnetic resonance image guided biomechanical modelling of the heart A novel tool towards personalized medicine in heart failure Heart failure (HF) is a progressing disease currently affecting 2% of the population in the developed world with a mortality rate of 50% within the first five years. While HF with reduced ejection fraction, primarily associated wi... ver más

31/08/2020

ETH Zürich

248K€

Presupuesto del proyecto: 248K€

Líder del proyecto

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH No se ha especificado una descripción o un objeto social para esta compañía.

Fecha límite participación Sin fecha límite de participación.

Ver 4 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2020-08-31

MSCA-IF-2016: Individual Fellowships

I+D

Cerrada hace 8 años

0% 100% 100%

Información adicional privada

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

4 Participantes

ETH Zürich

247.84K€ | Lider

DIAGNOSTICA CONSULTORIA TECN...

282.16K€ | Participante

THE REGENTS OF THE UNIVERSIT...

Conecta tu I+D

¿Tienes un proyecto y buscas un partner? Gracias a nuestro motor inteligente podemos recomendarte los mejores socios y ponerte en contacto con ellos. Te lo explicamos en este video

Proyectos interesantes

COMPCARDMECH Computational Tools for Cardiac Mechanics 275K€ Cerrado

CardioZoom High fidelity Cardiovascular Modeling from Super Fast Magnet... 1M€ Cerrado

SMHEART Smart Cardiac Magnetic Resonance Delivering One-Click and Co... 1M€ Cerrado

TIN2014-59932-JIN CARACTERIZACION Y DIAGNOSTICO NO INVASIVO DE ARRITMIAS CARDI... 202K€ Cerrado

TED2021-129774B-C22 CORAZON DIGITAL: NUEVAS HERRAMIENTAS Y MODELOS BASADOS EN CF... 234K€ Cerrado

COMHEART2CLINIC Translation of Computational Heart Models into the Clinic 30K€ Cerrado

Duración del proyecto: 41 meses Fecha Inicio: 2017-03-17
Fecha Fin: 2020-08-31

Líder del proyecto

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 248K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Heart failure (HF) is a progressing disease currently affecting 2% of the population in the developed world with a mortality rate of 50% within the first five years. While HF with reduced ejection fraction, primarily associated with myocardial infarction, can be detected with sufficient accuracy, HF with preserved ejection fraction is far more difficult to diagnose. Accordingly, there is an urgent need to better diagnose these patients to ultimately guide and improve treatment. Among the clinical imaging modalities, Cardiovascular Magnetic Resonance (CMR) is the gold standard for assessing cardiac mass and ejection fraction, and is capable to assess local cardiac mechanics and tissue properties. Beyond these established methods, cardiac diffusion tensor imaging has emerged as a new tool to enable insights into the microscopic morphology of the beating heart. Unfortunately, due to scan time limitations during clinical routine, compromises in spatial resolution and coverage have to be made. To overcome practical limitations of clinical in vivo CMR imaging and to enable prediction of disease progression for individual patients, additional tools are required. To this end, biomechanical models have attracted considerable attention. Once adapted sufficiently to in-vivo imaging, these models promise patient-specific insights into causes and progression of disease and, help guiding treatment. It is the objective of the present fellowship proposal to significantly advance patient-specific, image-guided modelling of HF by incorporating the most recent developments in both CMR imaging and biophysical modelling. The proposed framework will address limitations of current approaches, which impose generic assumptions about cardiac tissue properties and structure. With recent innovations in CMR imaging, as developed by the applicant, data on local changes of myocardial microstructure will be obtained to achieve the next level of diagnostic and predictive cardiac modelling of HF.

Conecta tu I+D

Entra hoy

¿Olvidé mi contraseña?

Financiación

Empresas

CTIs/Universidades

Proyectos

Investigadores