1. Tony Pan, Stephen Langella, Shannon Hastings, Scott Oster, Ashish Sharma, Metin Gurcan, Tahsin Kurc, Joel Saltz Department of Biomedical Informatics The Ohio State University Medical Center, Columbus OH Eliot Siegel, Khan M. Siddiqui University of Maryland School of Medicine, Baltimore, MD A Novel Use of Grid Computing to Provide High Performance Interactive CAD Decision Support For more information, please contact Tony Pan (tpan@bmi.osu.edu) Dept. of Biomedical Informatics, The Ohio State University http://bmi.osu.edu

2. First Time CAD-on-the-Grid A demonstration driven by NCI caBIG in-vivo Imaging Workspace requirements caBIG — Cancer Biomedical Informatics Grid: A National Cancer Institute initiative to create a biomedical informatics network for cancer research. GRID — “Grid computing is increasingly being viewed as the next phase of distributed computing. Built on pervasive Internet standards, grid computing enables organizations to share computing and information resources across department and organizational boundaries in a secure, highly efficient manner.” – www.ggf.org

3. Benefits Remote execution of multiple CAD algorithms using multiple image databases Facilitate research and clinical decision support with large number of subjects and multiple CAD algorithms. –Parameter studies, clinical and preclinical trials, etc Enable better algorithm development and validation through the use of many distributed, shared image datasets Support remote algorithm execution – reduce data transfer and avoid the need to transmit PHI Reduce overall processing time and algorithm development cycle through remote compute resource recruitment and CAD compute farms Scalable and open source — caBIG and grid standards

4. Architecture Expose algorithms and data as Grid Services

5. Image Data Service Expose data in DICOM PACS with grid service wrappers An open source DICOM server — Pixelmed XML based data transfer 1x Los Angeles 3x Chicago 1x Columbus 5 Participating Data Services

6. CAD Algorithm Service caGRID middleware to wrap CAD applications with grid services Interact with Data Services to retrieve images Invoke algorithm with required inputs Transform and report results to result storage service caGrid Introduce Hides complexity of plugging an algorithm into the grid caGrid GUMS/CAMS Used to provide security services CAD algorithms provided by iCAD and Siemens Medical Solutions. Prototypes for investigational use only; not commercially available

7. Framework Support Services Result storage server — A distributed XML database for caching CAD results GME — A central service to manage xml schemas which define communication protocols

8. 16 15 17 14 18 5 12 Slice = 127W/L = 2200/-500 User Interface Available data services Queried results DICOM image viewer Click to browse images, submit CAD analysis, and view results

9. Future Direction Location independence Move algorithms to data Move data to algorithms Move both data and algorithms to compute servers Currently supported – ongoing collaborations to deploy these capabilities Security and Privacy Collaborations to deploy caGrid authentication and authorization in cooperative research efforts Encryption and Just-In-Time anonymization for the image data services Scaling and Deployment Greater number and variety of CAD vendors Additional application areas, including CAD for other diseases and microscopy image analysis

10. Acknowledgements Siemens Medical Solutions Dennis O’Dell Marcos Salganicoff Toshiro Kubota Rajesh Amara iCAD Euvondia Friedmann Tom Fister Maha Sallam Tim Carter For more information, please contact Tony Pan (tpan@bmi.osu.edu) Dept. of Biomedical Informatics, The Ohio State University http://bmi.osu.edu The RIDER dataset used during this demonstration is provided courtesy of NCI Cancer Imaging Program This project was funded by NIH BISTI Center for Grid Enabled Medical Imaging, NCI, NSF, and the State of Ohio Board of Regents BRTT program

11. Q & A

12. Thank You

13. An Overview Expose algorithms and data management as Grid Services Remote execution of multiple CAD algorithms using multiple image databases CAD algorithm validation with larger set of images Better research support — recruit from multiple institutions, demographic relationships, outcome management etc. Remote CAD execution - reduced data transfer & avoid need to transmit PHI CAD compute farms that reduce the turnover time Scalable and open source — caBIG standards An application in the in-vivo imaging workspace of caBIG initiative caBIG — Cancer Bioinformatics Grid — An NIH/NCI initiative GRID — An infrastructure that permits the use of loosely coupled pool of compute and storage resources