State of the Art in Quantitative Coronary Arteriography as of 1996

  • Johan H. C. Reiber
  • Lars R. Schiemanck
  • Pieter M. J. Van Der Zwet
  • Bob Goedhart
  • Gerhard Koning
  • Martin Lammertsma
  • Martijn Danse
  • Jan J. Gerbrands
  • Martin J. Schalij
  • Albert V. G. Bruschke
Part of the Developments in Cardiovascular Medicine book series (DICM, volume 186)

Summary

In this chapter the important developments which have led to the third generation in quantitative coronary arteriographic (QCA) analytical software are presented, as well as current developments in the fields of image compression and storage. The conventional QCA approaches with automated contour detection techniques based on Minimum Cost contour detection Algorithms (MCA) have been well established and validated. However, further improvements in the calculations of the diameter and reference diameter functions were needed, especially for complex morphology and for stent applications. The development of the Gradient Field Transform (GFTR) approach for the quantitation of complex lesions represents a major step forward in QCA. With the advent of the cineless catheterization laboratory, the issue of image compression has become of major relevance. Phantom studies with lossy JPEG image compression at 5122 matrix size demonstrate that the compression factor (CF) should not exceed the level of 10. On the other hand, if JPEG and LOT lossy compression schemes (CF’s of 5,8 and 12) are applied to routinely acquired coronary angiographic image results, QCA measurements demonstrate that all three compression factors lead to significantly increased random differences in the measurements. These results suggest that even the JPEG and LOT compression ratio of 5 is not acceptable for QCA. Finally, an extensive QCA study has demonstrated that S-VHS video tape is unacceptable for QCA and should be excluded from quantitative angiographic clinical trials.

Keywords

Image Compression Vessel Segment Compression Scheme Compression Factor Reference Diameter 
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.

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Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Johan H. C. Reiber
  • Lars R. Schiemanck
  • Pieter M. J. Van Der Zwet
  • Bob Goedhart
  • Gerhard Koning
  • Martin Lammertsma
  • Martijn Danse
  • Jan J. Gerbrands
  • Martin J. Schalij
  • Albert V. G. Bruschke

There are no affiliations available

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