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CMRPredict TERMINATED

Patient specific magnetic resonance image guided biomechanical modelling of the heart – Anovel tool towards personalized medicine in heart failure

Total Cost €

0

EC-Contrib. €

0

Partnership

0

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 CMRPredict project word cloud

Explore the words cloud of the CMRPredict project. It provides you a very rough idea of what is the project "CMRPredict" about.

causes    structure    resolution    cardiovascular    guiding    sufficiently    tissue    disease    mortality    considerable    emerged    sufficient    cardiac    myocardial    guide    routine    fellowship    gold    magnetic    preserved    microstructure    made    vivo    first    coverage    biomechanical    data    modalities    compromises    individual    diagnose    infarction    ejection    spatial    tensor    once    urgent    tool    diffusion    prediction    progressing    framework    morphology    population    practical    significantly    progression    additional    local    fraction    patient    attracted    models    rate    difficult    biophysical    time    clinical    treatment    assessing    imaging    world    patients    promise    hf    ultimately    heart    accordingly    detected    impose    beating    assumptions    incorporating    primarily    standard    guided    insights    image    innovations    cmr       mass    unfortunately    scan    limitations    predictive    overcome    50    resonance    tools    diagnostic    mechanics    microscopic    accuracy   

Project "CMRPredict" data sheet

The following table provides information about the project.

Coordinator
EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH 

Organization address
address: Raemistrasse 101
city: ZUERICH
postcode: 8092
website: https://www.ethz.ch/de.html

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Switzerland [CH]
 Total cost 247˙840 €
 EC max contribution 247˙840 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2016
 Funding Scheme MSCA-IF-GF
 Starting year 2017
 Duration (year-month-day) from 2017-09-01   to  2020-08-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH CH (ZUERICH) coordinator 247˙840.00
2    University of California San Francisco School of Medicine US (San Francisco) partner 0.00

Map

 Project objective

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.

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The information about "CMRPREDICT" are provided by the European Opendata Portal: CORDIS opendata.

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