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Computing and Visualization in Science

, Volume 18, Issue 6, pp 203–212 | Cite as

Virtual reality in advanced medical immersive imaging: a workflow for introducing virtual reality as a supporting tool in medical imaging

  • Markus M. Knodel
  • Babett Lemke
  • Michael Lampe
  • Michael Hoffer
  • Clarissa Gillmann
  • Michael Uder
  • Jens Hillengaß
  • Gabriel Wittum
  • Tobias Bäuerle
Original Article

Abstract

Radiologic evaluation of images from computed tomography (CT) or magnetic resonance imaging for diagnostic purposes is based on the analysis of single slices, occasionally supplementing this information with 3D reconstructions as well as surface or volume rendered images. However, due to the complexity of anatomical or pathological structures in biomedical imaging, innovative visualization techniques are required to display morphological characteristics three dimensionally. Virtual reality is a modern tool of representing visual data, The observer has the impression of being “inside” a virtual surrounding, which is referred to as immersive imaging. Such techniques are currently being used in technical applications, e.g. in the automobile industry. Our aim is to introduce a workflow realized within one simple program which processes common image stacks from CT, produces 3D volume and surface reconstruction and rendering, and finally includes the data into a virtual reality device equipped with a motion head tracking cave automatic virtual environment system. Such techniques have the potential to augment the possibilities in non-invasive medical imaging, e.g. for surgical planning or educational purposes to add another dimension for advanced understanding of complex anatomical and pathological structures. To this end, the reconstructions are based on advanced mathematical techniques and the corresponding grids which we can export are intended to form the basis for simulations of mathematical models of the pathogenesis of different diseases.

Keywords

Virtual reality Imaging Cave automatic virtual environment Postprocessing Volume rendering Surface reconstruction Surface rendering 

Notes

Acknowledgements

We thank M. Aumüller, HLRS Stutttgart, for excellent and friendly support for the CAVE system installation and upgrade, and J. Pieper, GCSC, and A. Chizhov, Ioffe Physical-Technical Institute, RAS, St. Petersburg, for their support when performing the photos at the CAVE. MMK thanks Elisa Ficarra (Politecnico di Torino) for a stimulating discussion about the subject. The authors acknowledge the Goethe Universität Frankfurt for general support and computational resources and the University Erlangen-Nuremberg as well as the Politecnico di Torino for general support. This work has been supported in part by the “Fondazione Cassa di Risparmio di Torino” (Italy), through the “La Ricerca dei Talenti” (HR Excellence in Research) programme and in major part by the Institute of Radiology of the University Medical Center Erlangen. The Authors wish to express their sincere thanks to the anonymous Referees for their thorough and critical reviews of our work.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Goethe Center for Scientific Computing (GCSC)Goethe Universität FrankfurtFrankfurtGermany
  2. 2.Applied Mathematics and Computational Science, Computer, Electrical and Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology, KAUSTThuwalSaudi Arabia
  3. 3.Institute of Radiology, University Medical Center ErlangenFriedrich-Alexander University Erlangen-NurembergErlangenGermany
  4. 4.Medizinische Klinik, Abteilung Innere Medizin VUniversitätsklinikum HeidelbergHeidelbergGermany
  5. 5.Department of MedicineRoswell Park Cancer InstituteBuffaloUSA

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