skip to main content
10.1145/1529282.1529461acmconferencesArticle/Chapter ViewAbstractPublication PagessacConference Proceedingsconference-collections
research-article

Thin client architecture in support of remote radiology learning

Published:08 March 2009Publication History

ABSTRACT

We implemented a system for remote radiology learning which provides immediate feedback to the learner. Using a thin remote client, expert readers are asked to answer questions about specified radiological findings. These scans are presented as realtime 2D and 3D presentations which allow the user to freely manipulate them using a thin Java client with all 3D rendering performed on the server side. Answers are stored on the server and are used to provide feedback to learners who are presented with the same questions, using the remote client. Learners can practice on real datasets while receiving immediate feedback on their diagnosis and measurements. Novel concepts introduced are (1) the use of server-side rendering in radiology learning, (2) providing immediate and specific feedback to trainees, (3) the ability to provide useful feedback when a definitive gold standard does not exist and (4) a thin, highly compatible client that runs on common, existing hardware which allows to have more people participating in very complex radiological evaluations, even if there are not at the same site.

References

  1. Wood, B. Feedback: A Key Feature of Medical Training. Radiology, 215 (Apr. 2000), 17--19.Google ScholarGoogle Scholar
  2. Rubin G., Napel S., Leung A. Volumetric Analysis of Volumetric Data: Achieving a Paradigm Shift. Radiology, 200 (Aug. 1996), 312--317.Google ScholarGoogle Scholar
  3. Armato SG 3rd, McNitt-Gray MF, Reeves AP, Meyer CR, McLennan G, Aberle DR, Kazerooni EA, MacMahon H, van Beek EJ, Yankelevitz D, Hoffman EA, Henschke CI, Roberts RY, Brown MS, Engelmann RM, Pais RC, Piker CW, Qing D, Kocherginsky M, Croft BY, Clarke LP. The Lung Image Database Consortium (LIDC): an evaluation of radiologist variability in the identification of lung nodules on CT scans. Acad Radiol. 14(11) (2007 Nov), 1409--21.Google ScholarGoogle ScholarCross RefCross Ref
  4. Fovia, Inc. Fovia High Definition Volume Rendering® (HDVR#8482;)Google ScholarGoogle Scholar
  5. Bryson, S., Data Manipulation in Virtual Reality, in The Visualization Handbook, C. D. Hanson and C. R. Johnson, Editors. 2005, Elsevier. p. 413--430.Google ScholarGoogle Scholar
  6. Sun Microsystems, Java Object Serialization Specification, December 1998.Google ScholarGoogle Scholar
  7. Gilbert, David. 2004. JFreeChart. http://www.jfree.org/jfreechart/Google ScholarGoogle Scholar
  8. Kuratorium OFFIS. DCMTK: DICOM-Toolkit. http://dicom.offis.de/dcmtk.php.en.Google ScholarGoogle Scholar
  9. Crabb A. DICOM.pm: A DICOM Library in Perl. http://dicomperl.sourceforge.net/Google ScholarGoogle Scholar
  10. Sun Microsystems. Java Web Start Technology. http://java.sun.com/products/javawebstartGoogle ScholarGoogle Scholar
  11. Digital Imaging and Communications in Medicine (DICOM). NEMA Publications PS 3.1-PS 3.12. The National Electrical Manufacturers Association. Rosslyn, VA, 1992, 1993, 1994, 1995.Google ScholarGoogle Scholar
  12. Goldberg HI, Fell S, Myers HJ, Taylor RC. A computerassisted, interactive radiology learning program. Invest Radiol., 25(8) (Aug. 1990), 947--51.Google ScholarGoogle ScholarCross RefCross Ref
  13. Selcer B. Computer-assisted Interactive Radiology Courseware. Journal of Veterinary Medical Education. 20/3 (1993)Google ScholarGoogle Scholar
  14. Flanders A. What Is the Future of Electronic Learning in Radiology? RadioGraphics, 27. (2007), 559--561Google ScholarGoogle Scholar
  15. Sparacia G., Cannizzaro F., D'Alessandro D., D'Alessandro M., Caruso G., Lagalla R. Informatics in Radiology: Initial Experiences in Radiology e-learning. RadioGraphics. 27 (2007), 573--581.Google ScholarGoogle ScholarCross RefCross Ref
  16. Nunes F., Costa R. The Virtual Reality challenges in the health care area: a panoramic view. Symposium on Applied Computing: Proceedings of the 2008 ACM symposium on Applied computing. (2008), 1312--1316 Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Thin client architecture in support of remote radiology learning

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Conferences
          SAC '09: Proceedings of the 2009 ACM symposium on Applied Computing
          March 2009
          2347 pages
          ISBN:9781605581668
          DOI:10.1145/1529282

          Copyright © 2009 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 8 March 2009

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article

          Acceptance Rates

          Overall Acceptance Rate1,650of6,669submissions,25%
        • Article Metrics

          • Downloads (Last 12 months)1
          • Downloads (Last 6 weeks)0

          Other Metrics

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader