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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.

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

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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