Explore the words cloud of the CMRPredict project. It provides you a very rough idea of what is the project "CMRPredict" about.
The following table provides information about the project.
EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH
|Coordinator Country||Switzerland [CH]|
|Total cost||247˙840 €|
|EC max contribution||247˙840 € (100%)|
1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
|Duration (year-month-day)||from 2017-09-01 to 2020-08-31|
Take a look of project's partnership.
|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|
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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