Journal of Digital Imaging

, Volume 24, Issue 6, pp 1096–1102 | Cite as

Five Levels of PACS Modularity: Integrating 3D and Other Advanced Visualization Tools

  • Kenneth C. Wang
  • Ross W. Filice
  • James F. Philbin
  • Eliot L. Siegel
  • Paul G. Nagy
Article

Abstract

The current array of PACS products and 3D visualization tools presents a wide range of options for applying advanced visualization methods in clinical radiology. The emergence of server-based rendering techniques creates new opportunities for raising the level of clinical image review. However, best-of-breed implementations of core PACS technology, volumetric image navigation, and application-specific 3D packages will, in general, be supplied by different vendors. Integration issues should be carefully considered before deploying such systems. This work presents a classification scheme describing five tiers of PACS modularity and integration with advanced visualization tools, with the goals of characterizing current options for such integration, providing an approach for evaluating such systems, and discussing possible future architectures. These five levels of increasing PACS modularity begin with what was until recently the dominant model for integrating advanced visualization into the clinical radiologist's workflow, consisting of a dedicated stand-alone post-processing workstation in the reading room. Introduction of context-sharing, thin clients using server-based rendering, archive integration, and user-level application hosting at successive levels of the hierarchy lead to a modularized imaging architecture, which promotes user interface integration, resource efficiency, system performance, supportability, and flexibility. These technical factors and system metrics are discussed in the context of the proposed five-level classification scheme.

Keywords

PACS 3D imaging (imaging, three-dimensional) Computer systems Advanced visualization Server-based rendering Application hosting 

Notes

Acknowledgments

KCW gratefully acknowledges the support of RSNA Research and Education Foundation Fellowship Training Grant #FT0904, as well as that of the Walter and Mary Ciceric Research Award.

References

  1. 1.
    Kato Y, Katada K, Hayakawa M, Nakane M, Ogura Y, Sano K, Kanno T: Can 3D-CTA surpass DSA in diagnosis of cerebral aneurysm? Acta Neurochir 143:245–250, 2001CrossRefGoogle Scholar
  2. 2.
    Matsumoto M, Kodama N, Endo Y, Sakuma J, Suzuki KY, Sasaki T, Murakami K, Suzuki KE, Katakura T, Shishido: Dynamic 3D-CT angiography. Am J Neuroradiol 28:299–304, 2007PubMedGoogle Scholar
  3. 3.
    Rubin GD, Dake MD, Napel SA, McDonnell CH, Jeffrey RB: Three-dimensional spiral CT angiography of the abdomen: initial clinical experience. Radiology 186:147–152, 1993PubMedGoogle Scholar
  4. 4.
    Willmann JK, Wildermuth S: Multidetector-row CT angiography of upper- and lower-extremity peripheral arteries. Eur Radiol 15:D3–D9, 2005PubMedCrossRefGoogle Scholar
  5. 5.
    Desjardins B, Kazerooni EA: ECG-gated cardiac CT. Am J Roentgenol 182:993–1010, 2004Google Scholar
  6. 6.
    Lawler LP, Pannu HK, Fishman EK: MDCT evaluation of the coronary arteries, 2004: How we do it—data acquisition, postprocessing, display, and interpretation. Am J Roentgenol 184:1402–1412, 2005Google Scholar
  7. 7.
    Macari M, Bini EJ, Jacobs SL, Lange N, Lui YW: Filling defects at CT colonography: pseudo- and diminutive lesions (the good), polyps (the bad), flat lesions, masses, and carcinomas (the ugly). Radiographics 23:1073–1091, 2003PubMedCrossRefGoogle Scholar
  8. 8.
    Shi R, Schraedley-Desmond P, Napel S, Olcott EW, Jeffrey RB, Yee J, Zalis ME, Margolis D, Paik DS, Sherbondy AJ, Sundaram P, Beaulieu CF: CT colonography: influence of 3D viewing and polyp candidate features on interpretation with computer-aided detection. Radiology 239:768–776, 2006PubMedCrossRefGoogle Scholar
  9. 9.
    Soto JA, Lucey BC, Stuhlfaut JW, Varghese JC: Use of 3D imaging in CT of the acute trauma patient: impact of a PACS-based software package. Emergency Radiology 11:173–176, 2005PubMedCrossRefGoogle Scholar
  10. 10.
    Rodt T, Bartling SO, Zajaczek JE, Vafa MA, Kapapa T, Majdani O, Krauss JK, Zumkeller M, Matthies H, Becker H, Kaminsky J: Evaluation of surface and volume rendering in 3D-CT of facial fractures. Dentomaxillofacial Radiol 35:227–231, 2006CrossRefGoogle Scholar
  11. 11.
    Horton KM, Fishman EK: Multidetector CT angiography of pancreatic carcinoma: part I, evaluation of arterial involvement. Am J Roentgenol 178:827–831, 2002Google Scholar
  12. 12.
    Takeshita K, Kutomi K, Takada K, Kohtake H, Furui S: 3D pancreatic arteriography with MDCT during intraarterial infusion of contrast material in the detection and localization of insulinomas. Am J Roentgenol 184:852–854, 2005Google Scholar
  13. 13.
    Silverman PM, Zeiberg AS, Sessions RB, Troost TR, Davros WJ, Zeman RK: Helical CT of the upper airway: normal and abnormal findings on three-dimensional reconstructed images. Am J Roentgenol 165:541–546, 1995Google Scholar
  14. 14.
    Horton KM, Horton MR, Fishman EK: Advanced visualization of airways with 64-MDCT: 3D mapping and virtual bronchoscopy. Am J Roentgenol 189:1387–1396, 2007CrossRefGoogle Scholar
  15. 15.
    Croitoru S, Gross M, Barmeir E: Duplicated ectopic ureter with vaginal insertion: 3D CT urography with IV and percutaneous contrast administration. AJR 189:W272–W274, 2007PubMedCrossRefGoogle Scholar
  16. 16.
    Levoy M. Polygon-assisted JPEG and MPEG compression of synthetic images. Proceedings Special Interest Group on Computer Graphics and Interactive Techniques, 21–28, 1995.Google Scholar
  17. 17.
    Yoon I, Neumann U (2000) Web-based remote rendering with IBRAC (image-based rendering acceleration and compression). Eurographics 19:321–330Google Scholar
  18. 18.
    Bohne-Lang A, Groch W-D, Ranzinger R: AISMIG – An interactive server-side molecule image generator. Nucleic Acids Res 33:W705–W709, 2005PubMedCrossRefGoogle Scholar
  19. 19.
    Poliakov AV, Albright E, Corina D et al. (2001) Server-based approach to web visualization of integrated 3D medical image data. Proc Am Med Inform Assoc Symp 533–537Google Scholar
  20. 20.
    Erickson BJ, Persons KR, Hangiandreou NJ, James EM, Hanna CJ, Gehring DG: Requirements for an enterprise digital image archive. Journal of Digit Imaging 14:72–82, 2001CrossRefGoogle Scholar
  21. 21.
    Liu BJ, Cao F, Zhou MZ, Mogel G, Documet L: Trends in PACS image storage and archive. Computerized Medical Imaging Graphics 27:165–174, 2003CrossRefGoogle Scholar
  22. 22.
    Huang HK: Enterprise PACS and image distribution. Computerized Medical Imaging and Graphics 27:241–253, 2003PubMedCrossRefGoogle Scholar
  23. 23.
    Toland C, Meenan C, Toland M, Safdar N, Vandermeer P, Nagy P: A suggested classification guide for PACS client applications: the five degrees of thickness. Journal of Digital Imaging 19:78–83, 2006PubMedCrossRefGoogle Scholar
  24. 24.
    Health Level Seven International. Available at http://www.hl7.org/implement/standards/ccow.cfm. Accessed 13 July 2010
  25. 25.
    Paladini G, Azar FS: An extensible imaging platform for optical imaging applications. Proc SPIE 7171:717108, 2009CrossRefGoogle Scholar
  26. 26.
    Prior FW, Erickson BJ, Tarbox L: Open source software projects of the caBIG in vivo imaging workspace software special interest group. Journal of Digital Imaging 20:94–100, 2007PubMedCrossRefGoogle Scholar
  27. 27.
    ClearCanvas Inc. Available at http://www.clearcanvas.ca. Accessed 13 July 2010

Copyright information

© Society for Imaging Informatics in Medicine 2011

Authors and Affiliations

  • Kenneth C. Wang
    • 1
  • Ross W. Filice
    • 2
  • James F. Philbin
    • 3
  • Eliot L. Siegel
    • 1
  • Paul G. Nagy
    • 3
  1. 1.Department of Radiology, Rm. 1B-112Baltimore VA Medical CenterBaltimoreUSA
  2. 2.Center for Drug Evaluation and ResearchFood and Drug AdministrationSilver SpringUSA
  3. 3.School of Medicine, Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins UniversityBaltimoreUSA

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