The BioMart community portal: an innovative alternative to large, centralized data repositories.

The BioMart Community Portal (www.biomart.org) is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. All resources available through the portal are independently administered and funded by their host organizations. The BioMart data federation technology provides a unified interface to all the available data. The latest version of the portal comes with many new databases that have been created by our ever-growing community. It also comes with better support and extensibility for data analysis and visualization tools. A new addition to our toolbox, the enrichment analysis tool is now accessible through graphical and web service interface. The BioMart community portal averages over one million requests per day. Building on this level of service and the wealth of information that has become available, the BioMart Community Portal has introduced a new, more scalable and cheaper alternative to the large data stores maintained by specialized organizations

Pathologies vasculaires placentaires entre 26 et 34 SA (évaluation de la prise en charge et du devenir néonatal, étude rétrospective à propos de 113 grossesses uniques)

DIJON-BU Médecine Pharmacie (212312103) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Patch-based super-resolution for arterial spin labeling MRI

International audiencen

Patch-based super-resolution for arterial spin labeling MRI

International audiencen

Automated Electrodes Detection During Simultaneous EEG/fMRI

The coupling of Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) enables the measure of brain activity at high spatial and temporal resolution. The localization of EEG sources depends on several parameters including the knowledge of the position of the electrodes on the scalp. An accurate knowledge about this information is important for source reconstruction. Currently, when acquiring EEG and fMRI together, the position of the electrodes is generally estimated according to fiducial points by using a template. In the context of simultaneous EEG/fMRI acquisition, a natural idea is to use magnetic resonance (MR) images to localize EEG electrodes. However, most MR compatible electrodes are built to be almost invisible on MR Images. Taking advantage of a recently proposed Ultra short Echo Time (UTE) sequence, we introduce a fully automatic method to detect and label those electrodes in MR images. Our method was tested on 8 subjects wearing a 64-channel EEG cap. This automated method showed an average detection accuracy of 94% and the average position error was 3.1 mm. These results suggest that the proposed method has potential for determining the position of the electrodes during simultaneous EEG/fMRI acquisition with a very light cost procedure

Using negative signal in mono-TI pulsed arterial spin labeling to outline pathological increases in arterial transit times

International audienceThe presence of unexpected negative perfusion estimates has been sparsely discussed in the ASL literature [2,3]. In the study of perfusion maps extracted from a single inversion time in ASL (mono-TI ASL), it is however common to deal with areas of significant negative signal. This is problematic since performing statistical analysis based on this data might therefore lead to inacurrate results. Though isolated negative values could be attributed to noise, clusters of significant negative signal should be explained by another phenomenon. Following previous authors, which outlined that negative values might arise due to increased transit times, we investigated this hypothesis based on real clinical datasets including healthy control and patient data

Using negative signal in mono-TI pulsed arterial spin labeling to outline pathological increases in arterial transit times

International audienceThe presence of unexpected negative perfusion estimates has been sparsely discussed in the ASL literature [2,3]. In the study of perfusion maps extracted from a single inversion time in ASL (mono-TI ASL), it is however common to deal with areas of significant negative signal. This is problematic since performing statistical analysis based on this data might therefore lead to inacurrate results. Though isolated negative values could be attributed to noise, clusters of significant negative signal should be explained by another phenomenon. Following previous authors, which outlined that negative values might arise due to increased transit times, we investigated this hypothesis based on real clinical datasets including healthy control and patient data

Using negative signal in mono-TI pulsed arterial spin labeling to outline pathological increases in arterial transit times

International audienceThe presence of unexpected negative perfusion estimates has been sparsely discussed in the ASL literature [2,3]. In the study of perfusion maps extracted from a single inversion time in ASL (mono-TI ASL), it is however common to deal with areas of significant negative signal. This is problematic since performing statistical analysis based on this data might therefore lead to inacurrate results. Though isolated negative values could be attributed to noise, clusters of significant negative signal should be explained by another phenomenon. Following previous authors, which outlined that negative values might arise due to increased transit times, we investigated this hypothesis based on real clinical datasets including healthy control and patient data

A CONTRARIO DETECTION OF FOCAL BRAIN PERFUSION ABNORMALITIES BASED ON AN ARTERIAL SPIN LABELING TEMPLATE

Arterial Spin Labeling (ASL) is a recent MRI perfusion technique which enables quantification of cerebral blood flow (CBF). The presence of regions with atypical CBF can characterize a pathology. In brain tumors for instance, perfusion increase can be directly related to the grading of the malignant tissues. It is therefore of great interest to identify these regions in order to provide the patients with the most appropriate therapy. In this paper, we propose to detect abnormal brain perfusion by using an a contrario framework and an ASL template as a model of normal perfusion. Validation was undertaken by qualitative comparison with CBF extracted from dynamic susceptibility weighted contrast enhanced (DSC) imaging. We experimented this framework on four patients presenting brain tumors. Results show that high perfusion regions found in DSC CBF maps are correctly identified as hyperperfusions with a contrario detection. Automatic detection has clear advantages over manual delineation since it is less time-consuming, does not depend on medical expertise and allows quantification of perfusion abnormalities within the detected regions. Index Terms — Arterial spin labeling, Cerebral blood flow, Template, a contrario detection. 1