ABSTRACT
XNAT Central is a publicly accessible medical imaging data repository based on the XNAT open-source imaging informatics platform. It hosts a wide variety of research imaging data sets. The primary motivation for creating XNAT Central was to provide a central repository to host and provide access to a wide variety of neuroimaging data. In this capacity, XNAT Central hosts a number of data sets from research labs and investigative efforts from around the world, including the OASIS Brains imaging studies, the NUSDAST study of schizophrenia, and more. Over time, XNAT Central has expanded to include imaging data from many different fields of research, including oncology, orthopedics, cardiology, and animal studies, but continues to emphasize neuroimaging data. Through the use of XNAT's DICOM metadata extraction capabilities, XNAT Central provides a searchable repository of imaging data that can be referenced by groups, labs, or individuals working in many different areas of research. The future development of XNAT Central will be geared towards greater ease of use as a reference library of heterogeneous neuroimaging data and associated synthetic data. It will also become a tool for making data available supporting published research and academic articles.
Subject(s)
Information Dissemination , Neuroimaging , Access to Information , Brain/anatomy & histology , Brain/pathology , Databases, Factual , Humans , Information Systems , Medical Informatics , Schizophrenia/pathologyABSTRACT
ConnectomeDB is a database for housing and disseminating data about human brain structure, function, and connectivity, along with associated behavioral and demographic data. It is the main archive and dissemination platform for data collected under the WU-Minn consortium Human Connectome Project. Additional connectome-style study data is and will be made available in the database under current and future projects, including the Connectome Coordination Facility. The database currently includes multiple modalities of magnetic resonance imaging (MRI) and magnetoencephalograpy (MEG) data along with associated behavioral data. MRI modalities include structural, task, resting state and diffusion. MEG modalities include resting state and task. Imaging data includes unprocessed, minimally preprocessed and analysis data. Imaging data and much of the behavioral data are publicly available, subject to acceptance of data use terms, while access to some sensitive behavioral data is restricted to qualified investigators under a more stringent set of terms. ConnectomeDB is the public side of the WU-Minn HCP database platform. As such, it is geared towards public distribution, with a web-based user interface designed to guide users to the optimal set of data for their needs and a robust backend mechanism based on the commercial Aspera fasp service to enable high speed downloads. HCP data is also available via direct shipment of hard drives and Amazon S3.
Subject(s)
Brain/anatomy & histology , Brain/physiology , Connectome , Databases, Factual , Information Dissemination/methods , Access to Information , Behavior , Brain Mapping , Humans , Internet , Magnetic Resonance Imaging , Magnetoencephalography , Neuroimaging , Quality ControlABSTRACT
The Human Connectome Project (HCP) has developed protocols, standard operating and quality control procedures, and a suite of informatics tools to enable high throughput data collection, data sharing, automated data processing and analysis, and data mining and visualization. Quality control procedures include methods to maintain data collection consistency over time, to measure head motion, and to establish quantitative modality-specific overall quality assessments. Database services developed as customizations of the XNAT imaging informatics platform support both internal daily operations and open access data sharing. The Connectome Workbench visualization environment enables user interaction with HCP data and is increasingly integrated with the HCP's database services. Here we describe the current state of these procedures and tools and their application in the ongoing HCP study.
Subject(s)
Brain/anatomy & histology , Brain/physiology , Computational Biology/methods , Connectome/methods , Data Mining/methods , Databases, Factual , User-Computer Interface , Computational Biology/standards , Connectome/standards , Data Mining/standards , Database Management Systems/standards , Humans , Information Storage and Retrieval/methods , Information Storage and Retrieval/standards , Models, Anatomic , Models, Neurological , Nerve Net/anatomy & histology , Nerve Net/physiology , Quality ControlABSTRACT
Digital Imaging and Communications in Medicine (DICOM) is the dominant standard for medical imaging data. DICOM-compliant devices and the data they produce are generally designed for clinical use and often do not match the needs of users in research or clinical trial settings. DicomBrowser is software designed to ease the transition between clinically oriented DICOM tools and the specialized workflows of research imaging. It supports interactive loading and viewing of DICOM images and metadata across multiple studies and provides a rich and flexible system for modifying DICOM metadata. Users can make ad hoc changes in a graphical user interface, write metadata modification scripts for batch operations, use partly automated methods that guide users to modify specific attributes, or combine any of these approaches. DicomBrowser can save modified objects as local files or send them to a DICOM storage service using the C-STORE network protocol. DicomBrowser is open-source software, available for download at http://nrg.wustl.edu/software/dicom-browser.
Subject(s)
Data Display , Image Interpretation, Computer-Assisted/methods , Radiology Information Systems , Search Engine , Software , User-Computer Interface , Computer Communication Networks , Database Management Systems , Diagnostic Imaging/methods , Humans , Sensitivity and Specificity , Software ValidationABSTRACT
While brain imaging in the clinical setting is largely a practice of looking at images, research neuroimaging is a quantitative and integrative enterprise. Images are run through complex batteries of processing and analysis routines to generate numeric measures of brain characteristics. Other measures potentially related to brain function - demographics, genetics, behavioral tests, neuropsychological tests - are key components of most research studies. The canonical scanner - PACS - viewing station axis used in clinical practice is therefore inadequate for supporting neuroimaging research. Here, we model the neuroimaging research enterprise as a workflow. The principal components of the workflow include data acquisition, data archiving, data processing and analysis, and data utilization. We also describe a set of open-source applications to support each step of the workflow and the transitions between these steps. These applications include DIGITAL IMAGING AND COMMUNICATIONS IN MEDICINE viewing and storage tools, the EXTENSIBLE NEUROIMAGING ARCHIVE TOOLKIT data archiving and exploration platform, and an engine for running processing/analysis pipelines. The overall picture presented is aimed to motivate open-source developers to identify key integration and communication points for interoperating with complimentary applications.
