Virtopsy: The Virtual Autopsy*

Chapter
First Online:
Part of the Law, Governance and Technology Series book series (LGTS, volume 5)

Abstract

This chapter provides an overview of the Virtopsy procedure, a computerised approach to autopsy, lessening the need for invasive examination. Invasiveness results in the loss of evidence, and of the structural integrity of organs; it is also offensive to some worldviews. At the Institute of Forensic Medicine of the University of Bern, the Virtopsy project has unfolded during the 2000s, its aim being the application of high tech methods from the fields of measurement engineering, automation and medical imaging to create a complete, minimally invasive, reproducible and objective forensic assessment method. The data generated can be digitally stored or quickly sent to experts without a loss of quality. If new questions arise, the data can be revised even decades after the incident. This chapter describes technical aspects of the Virtopsy procedure, including imaging modalities and techniques (the Virtobot system, photogrammetry and surface scanning, post-mortem computer tomography, magnetic resonance imaging, post-mortem CT angiography, tissue/liquid sampling), then turning to the workflow of Virtopsy, and to a technical discussion of visualisation. Medical image data are for either radiologists and pathologists, or medical laypersons (such as in a courtroom situation). The final part of this chapter discusses Virtopsy in relation to the Swiss justice system.

Keywords

Hounsfield Unit Linear Attenuation Coefficient Polygon Model Volume Dataset Medical Image Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access

References

  1. Aamodt, A., Kvistad, K. A., Andersen, E., Lund-Larsen, J., Eine, J., Benum, P., et al. (1999). Determination of the Hounsfield value for CT based design of custom femoral stems. The Journal of Bone & Joint Surgery,46 81 B(1), 143–147. http://web.jbjs.org.uk/cgi/reprint/81 B/1/143.pdfCrossRefGoogle Scholar
  2. Aghayev, E., Ebert, L. C., Christe, A., Jackowski, C., Rudolph, T., Koval, J., et al. (2008). CT based navigation for post-mortem biopsy: A feasibility study. Journal of Forensic Legal Medicine, 15(6), 382–387.CrossRefGoogle Scholar
  3. Aghayev, E., Thali, M. J., Sonnenschein, M., Jackowski, C., Dirnhofer, R., & Vock, P. (2007). Post-mortem tissue sampling using computed tomography guidance. Forensic Science International, 166(2/3), 199–203.CrossRefGoogle Scholar
  4. Dolz, M. S., Cina, S. J., & Smith, R. (2000). Stereolithography: A potential new tool in forensic medicine. American Journal of Forensic Medicine and Pathology, 21(2), 119–123.CrossRefGoogle Scholar
  5. Ebert, L. C., Ptacek, W., Naether, S., Fürst, M., Ross, S., Buck, U., et al. (2010). Virtobot: A multi-functional robotic system for 3D surface scanning and automatic post mortem biopsy. International Journal of Medical Robotics, 6(1), 18–27.Google Scholar
  6. Grabherr, S., Djonov, V., Friess, A., Thali, M. J., Ranner, G., Vock, P., et al. (2006). Postmortem angiography after vascular perfusion with diesel oil and a lipophilic contrast agent. AJR: American Journal of Roentgenology, 187(5), W515–523.CrossRefGoogle Scholar
  7. Jackowski, C., Thali, M., Sonnenschein, M., Aghayev, E., Yen, K., Dirnhofer, R., et al. (2004). Visualization and quantification of air embolism structure by processing postmortem MSCT data. Journal of Forensic Science, 49(6), 1339–1342.CrossRefGoogle Scholar
  8. Jackowski, C., Thali, M. J., Buck, U., Aghayev, E., Sonnenschein, M., Yen, K., et al. (2006). Noninvasive estimation of organ weights by postmortem magnetic resonance imaging and multislice computed tomography. Investigative Radiology, 41(7), 572–578.CrossRefGoogle Scholar
  9. Kalender, W. A., Seissler W., Klotz, E., & Vock, P. (1990). Spiral volumetric CT with single-breathhold technique, continuous transport, and continuous scanner rotation. Radiology, 176, 181–183.Google Scholar
  10. Kassin, S., & Dunn, M. A. (1997). Computer-animated displays and the jury: Facilitative and prejudicial effects. Law and Human Behavior, 21, 269–281.CrossRefGoogle Scholar
  11. Luhmann, T., Robson, S., Kyle, S., & Harley, I. (2006). Close range photogrammetry: Principles, techniques and applications. (Translated from the German.) Scotland: Whittles Publishing.Google Scholar
  12. Menard, V. S. (1993). Admission of computer generated visual evidence: Should there be clear standards? Software Law Journal, 6, 325.Google Scholar
  13. Ross, S., Spendlove, D., Bolliger, S., Christe, A., Oesterhelweg, L., Grabherr, S., et al. (2008). Postmortem whole-body CT angiography: Evaluation of two contrast media solutions. AJR: American Journal of Roentgenology, 190(5), 1380–1389.CrossRefGoogle Scholar
  14. Selbak, J. (1994). Digital litigation: The prejudicial effects of computer-generated animation in the courtroom. High Technology Law Journal, 9, 337.Google Scholar
  15. Thali, M. J., Braun, M., & Dirnhofer, R. (2003). Optical 3D surface digitizing in forensic medicine. Forensic Science International, 137, 203–208.CrossRefGoogle Scholar
  16. Thali, M. J., Braun, M., Wirth, J., Vock, P., & Dirnhofer, R. (2003). 3D surface and body documentation in forensic medicine: 3D/CAD photogrammetry merged with 3D radiological scanning. Journal of Forensic Science, 48(6), 1356–1365.Google Scholar
  17. Keppens, J. (2007). Towards qualitative approaches to bayesian evidential reasoning. In Proceedings of the 11th international conference on artificial intelligence and law, pp. 17–25.Google Scholar
  18. Schmid, N (2009). Handbuch des Schweizerischen Strafprozessrechts. Zürich & St. Gallen, Switzerland: Dike Verlag.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Department of ComputingGoldsmiths’ College, University of LondonLondonEngland

Personalised recommendations