Computer-Assisted Quantification

  • Philipp Peloschek
  • Georg Langs
  • Reinhard Windhager
  • Franz KainbergerEmail author
Part of the Medical Radiology book series (MEDRAD)


Computer-aided image analysis and decision support has become an indispensable part of treatment planning in orthopaedic surgery and in osteology. The first use of computers for image interpretation probably was for a musculoskeletal application and was published by Lodwick who developed a computer diagnostic model for the pattern analysis of osteolytic bone tumours in 1963 (Lodwick et al. 1963). The next important step forward was, after quantitative computed tomography, the development of dual X-ray absorptiometry (DXA, the abbreviation DEXA should not be used) in 1987 (Wahner et al. 1994). With this technique, the image is not in the centre of the radiology report but is used as a guidance tool for generating measurable quantification parameters of the bone density, the trabecular architecture, and the body composition with the impact of expressing trends. A more detailed analysis of bone structure in the form of a “virtual biopsy” is possible with micro-CT systems and, for in vivo investigations, with high-resolution peripheral quantitative computed tomography (HRpqCT) (Rügsegger et al. 1996). Semiautomated and later fully automated measurements of the skeleton is a further application which bases on contour finding techniques and was developed for orthopaedic treatment planning and skeletal age assessment (Niethard 1999). Parallel to these applications for radiography and computed tomography, quantification schemes of MR data were established for the biochemical imaging of cartilage, for the perfusion of the synovial tissue in arthritis, and for the chemical shift imaging to measure the fat fraction in the bone marrow of patients with skeletal storage diseases (Kubassova 2007; Maas et al. 2011; Reiser et al. 1989; Trattnig et al. 2012). Many of these skeletal and soft tissue applications have been designed towards better patient’s and social outcomes, with important research in the field of maxillofacial surgery for designing individual prosthetic devices with and without 3D printing and for automated steering of implant positioning (Ploder et al. 1995; Windhager et al. 1996).



We want to thank Prof. Horst Bischof for his mentorship during the recent years.

This contribution was supported by a grant from the Austrian Funds for Scientific Research (No. ~P17083-N04).


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  • Philipp Peloschek
    • 1
  • Georg Langs
    • 2
  • Reinhard Windhager
    • 3
  • Franz Kainberger
    • 2
    Email author
  1. 1.Radiology Center ViennaViennaAustria
  2. 2.CIR—Computational Imaging Research Lab, Department of Biomedical Imaging and Image-Guided TherapyMedical University of ViennaViennaAustria
  3. 3.Department of Orthopaedic & Trauma SurgeryMedical University of ViennaViennaAustria

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