Databasing and Data Sharing #3516(Wth) Databasing and Data Sharing http://ceib.san.gva.es BIMCV: Synergy between data in population medical imaging, computer aided diagnosis and AVR María de la Iglesia Vayá1,2,6, Ángel Fernández-Cañada Vilata2, Jorge Isnardo Altamirano2, F. Xavier Castellanos3, Gonzalo Rojas Costa4 , Erika Proal5, José María Salinas1,2, Jacobo Martínez6, José Miguel Puig Saqués2 Introduction The main objective of the BIMCV node "Medical Imaging Databank of the Valencia Region" is to set up an infrastructure with mass storage capacity, multiple image quality control indices, pixel and DICOM anonymization (through GIMD The project from the Regional Ministry of Health in the Valencia Region, figure 1) and also high throughput computational modeling capabilities. The aim is to transform the Medical Imaging Databank of the Valencia Region into an area for the natural development of imaging-assisted medical-decision systems (SADI), having as main objective that BIMCV becomes an environment for translational innovation for healthcare interventions and management. The whole approach is based on the Cloud CEIB architecture (de la Iglesia et al., 2011; Salinas et al., 2012). Finally, we want to validate different visualization techniques. Some authors described a stereoscopic method to view neuroradiological 3D images (Rojas et al., 2014). We propose augmented virtual reality approaches for visualization, being Google Glass or smart phones some of the selected devices. Methods The Valencia BIMCV node, which has been submitted to EuroBioimaging call for expression of interests "Population Imaging", gives a clear answer to the creation of an infrastructure for large population anonymized base, with associated data acquisition, the quantitative analysis of standardized data and access to the image data and his respective quality markers (framewise displacement, DVARS, tSNR...). The system architecture has been developed by the Regional Ministry for Health (CS) through the Center of Excellence for Biomedical Imaging of the CS (http://ceib.san.gva.es) and has been evaluated by independent experts as future EuroBioImaging node platform (http://www.eurobioimaging.eu), who have reviewed the scientific excellence of the proposed research skills and group activity (de la Iglesia et al 20112013, Salinas, JM, et al, 20112013). We pretend to model and improve the characterization of structural alterations in patients with neurological disease, which will allow us to better understand the pathology and to obtain structural imaging biomarkers related to cognitive impairment. Finally, with Augmented Virtual Reality, we prove the viability of incorporating these techniques into clinical practice. For this purpose, a software application will be develop based on Rojas's prototype (Rojas et al., 2014). Results The main result will be the generation and evaluation of parametric indexes extracted from the image data of brain NMR, to characterize and modeling the volume of gray matter degeneration in the population due to neurological impairment, to set parameters of degeneration and to contrast the status of an individual with respect to model population by sex and age segmentation, thereby achieving early diagnostic capabilities. Finally, we expect to apply new visualization techniques specifically trying to incorporate the Augmented Virtual Reality through edge devices (Google Glass). Conclusions Putting up a structure with massive storage capacity and intensive process capabilities. Ensuring quality criteria in terms of image and associated data and provide high performance computing resources for postprocessing of medical population image data. Preparing the databases, structuring and anonymising the information. Application of techniques such as virtual and augmented reality as a new way to display the results. ARiBraiN3T 1Unidad mixta FISABIO & Prince Felipe Research Centre (CIPF), Valencia, Spain, 2Centre of Excellence in Biomedical Image (CEIB), Regional Ministry of Health in the Valencia Region, Valencia, Spain, 3Phyllis Green and Randolph Cōwen Institute for Pediatric Neuroscience, NYU Langone Medical Center, New York, NY, 4Advanced Medical Image Processing Lab, Department of Radiology, Clínica las Condes, Santiago, Chile, 5NEUROingenia, mexico city, Mexico, 6Fundación para el Fomento de la Investigación Sanitaria de la Comunitat Valenciana, FISABIO, Valencia, Spain