Multimodal analysis of MR data using Bayesian methods

Project Information

BAYESIANMULTIMODALMR

Grant agreement ID: 326644

Project closed

Start date 1 October 2013

End date 8 April 2016

Funded under

• Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)

Total cost € 231 283,20

EU contribution € 231 283,20

Coordinated by

LIVERPOOL JOHN MOORES UNIVERSITY
United Kingdom

Objective

Through the development of this training-through-research project, the fellow expects to acquire new skills, knowledge and perspectives, as well as to develop and widen her competences significantly, all contributing to her career development. The purpose of this proposal is to acquire new research expertise in Bayesian methodologies applied to source identification in blind signal separation and applied also to fusion of different modalities of physiological measurements for tumour delineation in brain.
Magnetic resonance (MR) is key for the non-invasive analysis of brain tumours in the field of neuro-oncology. MR imaging (MRI) provides a morphologic characterisation of tissues, while MR spectroscopy (MRS) provides their biochemical information, resulting in precise metabolomic signatures. In this project, a multimodal MRI and MRS data analysis using Bayesian methods is proposed to address some medical questions that remain open, such as using additional knowledge to help extract better sources from the MR spectra, identify the exact number of underlying tissue types present in a sample and their spectroscopic patterns, and the use of them to track response to therapy. To address them, it will be necessary to solve some challenges from the methodological viewpoint, such as the extraction of relevant source signals in a multimodal (MRI and MRS) unsupervised approach, and the identification of the most appropriate number of source signals. A novel Bayesian approach of NMF tailored to facilitate the unsupervised multimodal analysis of MR data is planned to be developed, since Bayesian models deal with multimodal systems in a natural way, and the relationship between variables is explicit, as well as the handling of the prior knowledge. Data from pre-clinical models and human data from the European project “ETUMOUR” will be used to test the models developed as part of this project.

Fields of science (EuroSciVoc)

CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.

• natural sciences › computer and information sciences › data science
• natural sciences › mathematics › applied mathematics › statistics and probability › bayesian statistics
• natural sciences › physical sciences › optics › spectroscopy

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Programme(s)

• FP7-PEOPLE - Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)

Topic(s)

• FP7-PEOPLE-2012-IEF - Marie-Curie Action: "Intra-European fellowships for career development"

Call for proposal

FP7-PEOPLE-2012-IEF
See other projects for this call

Funding Scheme

MC-IEF - Intra-European Fellowships (IEF)

Coordinator

LIVERPOOL JOHN MOORES UNIVERSITY

• EU contribution: € 231 283,20
• Address: RODNEY STREET 2 EGERTON COURT ; L3 5UX Liverpool ; United Kingdom
• Activity type: Higher or Secondary Education Establishments
• Total cost: No data