DEMO SIGNED
Dementia modelling
Total Cost €
0EC-Contrib. €
0Partnership
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0DEMO project word cloud
Explore the words cloud of the DEMO project. It provides you a very rough idea of what is the project "DEMO" about.
Project "DEMO" data sheet
The following table provides information about the project.
| Coordinator |
BIOMEDIQ AS
Organization address contact info |
| Coordinator Country | Denmark [DK] |
| Project website | http://dementia-modelling.eu/ |
| Total cost | 853˙451 € |
| EC max contribution | 853˙451 € (100%) |
| Programme |
1. H2020-EU.1.3.1. (Fostering new skills by means of excellent initial training of researchers) |
| Code Call | H2020-MSCA-ITN-2016 |
| Funding Scheme | MSCA-ITN-EID |
| Starting year | 2016 |
| Duration (year-month-day) | from 2016-09-01 to 2020-08-31 |
Partnership
Take a look of project's partnership.
| # | ||||
|---|---|---|---|---|
| 1 | BIOMEDIQ AS | DK (KOBENHAVN) | coordinator | 580˙163.00 |
| 2 | UNIVERSITY COLLEGE LONDON | UK (LONDON) | participant | 142˙717.00 |
| 3 | KING'S COLLEGE LONDON | UK (LONDON) | participant | 130˙570.00 |
Map
Project objective
Dementia constitutes a major burden on society, both in monetary costs and the suffering of patients and their relatives. It comprises a number of diseases including Alzheimer’s disease (AD) and vascular dementia (VaD). In recent years, imaging biomarkers have been developed including measures of brain morphology (MRI T1), vascular pathologies (MRI T2*/FLAIR), white matter abnormalities (MRI DWI), perfusion (MRI ASL), glucose turnover (PET FDG), and accumulation of pathological proteins (PET PIB/AV45). Quantitative measures using these biomarkers in large cohort studies have the potential to model the pathological process of the disease. This proposal would create an innovative training network, in which early stage researchers will develop new computational imaging biomarkers, under the supervision of experienced researchers, for the purpose of modeling dementia etiology. One researcher will investigate quantification of vascular pathologies, another will develop quantitative measures of white matter abnormalities from structural MRI, and the final researcher will construct a quantitative model of disease etiology using a maximum-likelihood framework. The early stage researchers will be enrolled as PhD students at University College London (UCL) under the EPSRC Centre for Doctoral Training in Medical Imaging (CDT), which is based in Centre for Medical Image Computing (CMIC), with the Dementia Research Centre (DRC) being one of the main clinical collaborators for CDT studentships. However, they will spend the majority of time at the research facilities of Biomediq A/S, Copenhagen Denmark, where they will be exposed to industry and work under professional guidance.
Deliverables
| Recruitment completed | Other | 2019-07-24 11:09:15 |
| Webpage completion | Websites, patent fillings, videos etc. | 2019-07-18 19:26:50 |
Take a look to the deliverables list in detail: detailed list of DEMO deliverables.
Publications
| year | authors and title | journal | last update |
|---|---|---|---|
| 2019 |
Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai, M. Jorge Cardoso, Marc Modat, Sébastien Ourselin, Lauge Sørensen Training recurrent neural networks robust to incomplete data: Application to Alzheimer’s disease progression modeling published pages: 39-46, ISSN: 1361-8415, DOI: 10.1016/j.media.2019.01.004 |
Medical Image Analysis 53 | 2019-10-08 |
| 2018 |
Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai, M. Jorge Cardoso, Marc Modat, Sebastien Ourselin, Lauge Sørensen Robust training of recurrent neural networks to handle missing data for disease progression modeling published pages: , ISSN: 2331-8422, DOI: |
1st International Conference on Medical Imaging with Deep Learning (MIDL) | 2019-07-18 |
| 2018 |
Mauricio Orbes-Arteaga, Lauge Sørensen, Marc Modat, M. Jorge Cardoso, Sébastien Ourselin, Mads Nielsen, Akshay Pai Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs published pages: , ISSN: 2331-8422, DOI: |
1st International conference on Medical Imaging with Deep Learning (MIDL) | 2019-07-18 |
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The information about "DEMO" are provided by the European Opendata Portal: CORDIS opendata.