NA-MIC National Alliance for Medical Image Computing XNAT eXtenible aNatomy Archiving Toolkit Steve Pieper, PhD Isomics, Inc. Slides Courtesy Dan Marcus, Washington U., St Louis Randy Buckner, Harvard U.

National Alliance for Medical Image Computing Topics

National Alliance for Medical Image Computing NIH Data Sharing Policy

National Alliance for Medical Image Computing Additional Goals for Data Sharing Tools Tools should be painless and even helpful –Ideally you should want to use the tools independent of the Data Sharing requirements Tools should be compatible with existing methodologies –DICOM transfers –easy upload/download of data for processing –database schema adaptable to application use cases Tools should not require excessive system or database administrator time committment Tools must be robust and efficient

NA-MIC National Alliance for Medical Image Computing Building informatics tools for brain imaging research Randy Buckner Dan Marcus May 10, 2006

National Alliance for Medical Image Computing Data Capture DATA INPUTS NEUROIMAGING GENETICS OTHER SOURCES

National Alliance for Medical Image Computing Data Capture DATA INPUTS NEUROIMAGING GENETICS OTHER SOURCES BAD DATA Integrity: Do I have the data? Quality Control: Are the data any good?

National Alliance for Medical Image Computing Local Use DATA INPUTS NEUROIMAGING GENETICS OTHER SOURCES BAD DATA Application: Can I do things with the data? Automation: Can I do these things automatically?

National Alliance for Medical Image Computing Collaboration DATA INPUTS NEUROIMAGING GENETICS OTHER SOURCES BAD DATA Access: Are colleagues getting the data they need? Security: Are colleagues getting data they shouldn’t?

National Alliance for Medical Image Computing Public access DATA INPUTS NEUROIMAGING GENETICS OTHER SOURCES BAD DATA Privacy: Am I respecting the rights of the study participants? Convenience: How usable are the data?

National Alliance for Medical Image Computing QUARANTINELOCAL USECOLLABORATIONPUBLIC ACCESS CAPTURE NEUROIMAGING GENETICS OTHER SOURCES The XNAT workflow Quality control Data archiving Data access Security Visualization Automation Integration Data sharing

National Alliance for Medical Image Computing What is XNAT? The Extensible Neuroimaging (anatomy) Archive Toolkit (XNAT) is… A workflow A data archive Web-based productivity tools New: XNAT is now part of the NA-MIC Kit –WashU is funded by NA-MIC to improve ease-of-installation and interoperability with other tools

National Alliance for Medical Image Computing More about XNAT Open Source –BSD-style license based on Slicer License DICOM Server –Push images directly to XNAT Can Attach Arbitrary Files to a Subject or Project Web Interface, Java API, and Command Line Access Utilities Leverages standard web database tools –Java, Apache, Tomcat, Postgres Active Developer and User Community –Core developer group under Dan Marcus at Washington University Saint Louis –Further development and application under Randy Buckner at Harvard –Supported by BIRN, NA-MIC, and other projects

National Alliance for Medical Image Computing The XNAT Architecture Client applications XNAT engine Data store Relational database XFT Data files XML Schemas XML Schemas Jakarta Turbine ActionsScreensDisplay XNAT API Form handler Search engine List generator Database generator Report generator

National Alliance for Medical Image Computing Data Documents Welcome to XNAT XNAT Generator … … XML Schema CRATE TABLE table ( name,VARCHAR(50), idMethod,VARCHAR(50), DEFAULT 'null‘, type,VARCHAR(50), ID INT UNSIGNED, NOT NULL AUTO_INCREMENT, PRIMARY KEY (ID) ); … CREATE TABLE table_column ( nameVARCHAR(50) javaNameVARCHAR(50) primaryKeyVARCHAR(50) CRATE TABLE table ( name,VARCHAR(50), idMethod,VARCHAR(50), DEFAULT 'null‘, type,VARCHAR(50), ID INT UNSIGNED, NOT NULL AUTO_INCREMENT, PRIMARY KEY (ID) ); … CREATE TABLE table_column ( nameVARCHAR(50) javaNameVARCHAR(50) primaryKeyVARCHAR(50) public class Experiment extends org.cnl.cnda.om.BaseExperiment implements Persistent { public static final long MILLIS_IN_DAY = ; public static long getDays(Calendar c1, Calendar c2) { long time1 = c1.getTime().getTime(); public class Experiment extends org.cnl.cnda.om.BaseExperiment implements Persistent { public static final long MILLIS_IN_DAY = ; public static long getDays(Calendar c1, Calendar c2) { long time1 = c1.getTime().getTime(); $page.setTitle("CNDA -- Integrating the Neurouniverse") $page.setLinkColor($ui.alink) $page.setVlinkColor($ui.vlink) … $page.setTitle("CNDA -- Integrating the Neurouniverse") $page.setLinkColor($ui.alink) $page.setVlinkColor($ui.vlink) … Database Schema 78 male right ADRC 5/1/2002 Randy Buckner Dan 78 male right ADRC 5/1/2002 Randy Buckner Dan Java ClassesHTML Pages

National Alliance for Medical Image Computing XNAT integrates storage, web, and exchange formats Database XMLWeb pages

National Alliance for Medical Image Computing The XNAT workflow in practice QUARANTINELOCAL USECOLLABORATIONPUBLIC ACCESS CAPTURE NEUROIMAGING GENETICS OTHER SOURCES

National Alliance for Medical Image Computing mBDR, Central, MyXNAT Morphometry BIRN Data Repository –Currated, User Support to Coordinate Submissions XNAT Central –Anyone Can Create a Project and Upload Your Local Database –Run your own instance inside your firewall

National Alliance for Medical Image Computing XNAT in practice: local use Study Relationship between white matter lesions and cognition in nondemented aging and in early-stage Alzheimer’s Disease Investigators Jeff Burns and colleagues at Washington University’s ADRC XNAT’s role Data integration, automated image processing, image visualization, data entry, quality control, data access

National Alliance for Medical Image Computing XNAT in practice: local use 1.Data capture (& quality control

National Alliance for Medical Image Computing XNAT in practice: local use 1.Data capture (& quality control) 2.Auto. Image processing (& more QC)

National Alliance for Medical Image Computing XNAT in practice: local use 1.Data capture (& quality control) 2.Auto. Image processing (& more QC) 3.Online data analysis & entry (& more QC)

National Alliance for Medical Image Computing XNAT in practice: local use 1.Data capture (& quality control) 2.Auto. Image processing (& more QC) 3.Online data analysis & entry (& more QC) 4.Exploration, download

National Alliance for Medical Image Computing XNAT in practice: local use 1.Data capture (& quality control) 2.Auto. Image processing (& more QC) 3.Online data analysis & entry (& more QC) 4.Exploration, download 5.Offline analysis & publication

National Alliance for Medical Image Computing XNAT in practice: collaboration Study SASHA (Semi-automated shape analysis): quantitative analysis of shape and size of cortical and subcortical brain regions in non-AD and AD adults. Investigators BIRN investigators at Washington University (Randy Buckner), MGH (Bruce Fischl), BWH (Steve Pieper), and Johns Hopkins (Mike Miller) XNAT’s role Data integration, distribution, image visualization

National Alliance for Medical Image Computing XNAT in practice: collaboration Freesurfer 3D Slicer LDDMM Teragrid SRB XNAT Image acquisition Segment & visualization Grid proc. & storage Shape analysis

National Alliance for Medical Image Computing XNAT in practice: public access Study OASIS: A Cross-sectional sample of MR images and related data from 400 individuals across the lifespan, including 200 younger, 100 older non-demented, and 100 early stage AD Investigators Dan Marcus, Randy Buckner, Tracy Wang, Jamie Parker, Washington University’s ADRC XNAT’s role Data integration, automated image processing, user interface, image visualization, distribution

National Alliance for Medical Image Computing Open Access Structural Imaging Series 400 subjects –100 < 65 years old –100 > 65 years old –100 DAT 3 or 4 T1-weighted MPRAGE images for each subject 20 reliability scans

National Alliance for Medical Image Computing XNAT-based OASIS website

National Alliance for Medical Image Computing A review of where we’re at QUARANTINELOCAL USECOLLABORATIONPUBLIC ACCESS CAPTURE NEUROIMAGING GENETICS OTHER SOURCES

National Alliance for Medical Image Computing mBIRN: Data Sharing Applied to Morphometry and Disease Alzheimer’s Individual Subcortical Segmentation Elderly Control

National Alliance for Medical Image Computing mBIRN Goals Support Multi-Site Multi- Vendor Structural Neuroimaging Research Provide End-to-End Suite of Tools and Techniques –Sensitive Detection –Large N for Statistical Power Support Scientific Interpretation 1 Year Volumetric Change red/yellow 5-20% increase blue 5-10% decrease BIRN-ADNI Collaboration

National Alliance for Medical Image Computing mBIRN Integration BIRN acquisition protocols distortion correction tools local databases workflows analysis tools population statistics PubMed IBVD BrainInfo Visualization & Interpretation (.xcat) (.xar)

National Alliance for Medical Image Computing mBIRN Informatics BIRN Data Repository (BDR) eXtensible Neuroimaging Archive Tool (XNAT) XML-Based Clinical Experiment Data Exchange Schema (XCEDE) Stores Images, Demographics, Clinical Data, Analysis Results 3D Slicer Interoperability (currently read-only, read/write prototype exists)

National Alliance for Medical Image Computing Query Atlas Module BIRN Scientific Interpretation Tool –Compatible with fMRI and Morphometry Datasets in BIRN- Standard Formats Provides a Link Between Images and Text Databases –Uses Anatomic Labeling + Ontologies –Links to Definitions, Publications, Quantifications –E.g. Wikipedia, PubMed, IBVD, BrainInfo

National Alliance for Medical Image Computing Query Atlas Features Input:.xcat or.qdec from.xar Hardware Accelerated Interactive 3D Annotation Ontology Engine –FreeSurfer, UMLS, BIRNLex, NeuroNames, IBVD –Interactive Browser Direct Browser Launch to Search Sites using Selected Terms

National Alliance for Medical Image Computing QDEC Query, Design, Estimate, Contrast of Population Statistics – XNAT to Select Subjects – FreeSurfer QDEC Runs on Server –.QDEC file in.XAR Web Download – Slicer3 Interactive Visualization – Integrated with Query Atlas