This reintegration project aims at combining content and context information for search and retrieval of brain magnetic resonance (MR) images from large databases. The number of digital images that need to be analyzed, processed, searched and stored at healthcare centres is exponentially increasing. To identify the underlying causes of a pathology medical experts generally benefit from similar cases. Patient search systems used for this purpose at the healthcare centres mostly use keyword-based approaches. However, combining image features (content) with patient demographic information, clinical findings and ontologies capturing disease-specific, clinical and anatomical information (context) will improve search and retrieval of medical images. Solutions in the literature generally either focus on only image content or benefit from only context information. As proposed in this grant, combining content and context knowledge for image search and retrieval will aid medical experts in finding similar cases more efficiently, and therefore will improve diagnosis and treatment efficacy of diseases whose causes and progresses are not yet fully known, and diseases with high prevalence, e.g. neurodegenerative disorders. Furthermore, such solutions may also lead into discoveries of new biomarkers. This reintegration grant proposes a novel work that combines content and context information for brain MR image search and retrieval, that processes multiple MR contrasts, that is specialised at neurology-radiology field and that can be used with disease focus.Realisation of this multidisciplinary project will result in 1.creation of powerful image search and retrieval that will aid experts in finding similar cases more efficiently, 2.improvement in diagnosis and treatment, 3.new scientific publications and patent applications, 4.contributions to European economic, social and scientific progress, and 5.long-lasting professional reintegration of the researcher.