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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.

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

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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