Imaging Workspace An Overview and Roadmap Eliot L. Siegel, MD Imaging Workspace Lead SME January 23, 2008

Introduction Imaging has been separate island with its own standards (DICOM) DICOM allows high level of interoperability among clinical imaging modalities such as CT and MRI Patient centered and very limited for data mining and research Completely separate from world of XML, service oriented architecture, Grid Computing Different vendors algorithms provide different results with no standards for annotation and image mark-up

caBIG IMG: A Brief History The In Vivo Imaging Workspace was added to the caBIG program in April of 2005: 1.To advance imaging informatics for treatment of patients with cancer 2.To leverage caBIG technology such as the caGRID, Internet tools such as XML, and existing DICOM standards by creating “middleware,” to facilitate sharing of images in a variety of settings 3.To strive towards a standardized way to evaluate and annotate images, especially for evaluation of tumor burden and response 4.To facilitate secure and easy sharing of images and image analysis & visualization algorithms with an emphasis on the cancer community

caBIG IMG: A Brief History Adoption activities defined (CTTI/NTROI) Interoperability of caBIG Imaging tools demonstrated caBIG Imaging WS established (110 years after the discovery of the x-ray) SMEs selected NCIA development began/LIDC- use case Strategic plan set XIP, Middleware, AIM development began First RSNA presentation Adoption activities planned Next phase of development NLST IP

National Cancer Imaging Archive The NCIA network-accessible "in vivo image repository" provides image archives to assist development and validation of imaging software tools Multiple image libraries currently stored on archive including LIDC Not only repository, but software for NCIA is free and open source Federated system so single inquiry can produce responses from multiple repositories Includes visualization tool

What are the Imaging Workspace “Products”?

eXtensible Imaging Platform (XIP) Allows Easy Sharing of Image Enhancement/Analysis/Visualization Algorithms XIP Application (Can be replaced with any DICOM WG23- compatible Host) XIP Host Adapter XIP Modules Host Independent WG 23 XIP Host WG 23 Web-based Application Medical Imaging Workstation Standalone Application Distribute DICOM, HL7, & other services per IHE caGRID Services via Imaging Middleware XIP Application Builder XIP Class Library Auto Conversion Tool Host-Specific Plug-in Libs WG 23 Distribute ITKVTK XIP LIB... A free and open source platform that facilitates the sharing not of images and other patient data but of image display, processing, and analysis algorithms themselves.

Imaging Middleware (including GridCAD and Virtual PACS) Middleware provides connection between DICOM and Grid computing The GRID has tremendous potential to promote interoperability, improve security, and support more efficient sharing of image data and software algorithms Middleware projects such as CAD (computer aided detection) for lung nodules on CT scans demonstrate the power and potential of GRID computing

Annotations and Imaging Markup (AIM) The first project of its kind that we’re aware of to propose/create a “standard” means of adding information/knowledge to an image in a clinical environment in which there is currently chaos in order to create a future in which image content can be easily and automatically searched.

Algorithm Validation Tools (AVT) Baseline Max Diameter 36.2mm Volume 6.1cm 3 Baseline +20 weeks Max Diameter 32.6mm Volume 9.48cm3 55% increase The purpose of AVT is to provide a set of tools capable of generating measurements using validated and consistent methods for detecting change; and to associcate information including clincial outcome data that would be helpful in assessing the performance of image-based change assessment tools

DICOM Ontology

DICON Ontology A single common reference information model for DICOM Unify and make explicit all the key entities and relations in DICOM in a human-usable but machine-processable format. Represent the existing DICOM model, whether it be implicit or explicit, as an ontology.

Query Formulation Create an Imaging Query Formulation tool Automates the creation of ontology-based queries to image resources Query Formulation Engine will translate user queries that are formulated using the QueryTool UI into an ontology- based query graph “Mary J.” Breast Cancer Chemo- therapy “ Mary J’s CT Images” Progressive Disease (RECIST) PATIENT DISEASE TREATMEN T DISEASE STATUS DISEASE IMAGING has Disease Assessment has RECIST Assessment has Disease has Treatment Physical Exam has Disease Assessment

The caBIG Imaging Roadmap: Transitioning from Development to Real World Application NCIA Image repository XIP Toolkit to build sharable imaging applications Middleware Connect to the grid/ bulk data transfer AIM Share annotations & markups AVT Measurement/ change detection validation Supported by caBIG vocabularies and ontologies such as RadLex, ACRIN Data Elements, and DICOM Data Elements and Ontology caBIG Imaging Suite End users can select any or all of the tools to meet their research or clinical needs Query Form Allow for “Google” type query of image data

In The Near Future: Practical Applications of Workspace Tools Open Source Imaging Viewer Clinical Trials Tool Integration Other Adoption Activities