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The Dependence of Tomosynthesis Imaging Performance on the Number of Scan Projections

  • Baorui Ren
  • Tao Wu
  • Andrew Smith
  • Chris Ruth
  • Loren Niklason
  • Zhenxue Jing
  • Jay Stein
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4046)

Abstract

In general, the use of more projections results in fewer tomosynthesis reconstruction artifacts. However, under a fixed dose, an excess number of projections will make the detector noise more pronounced in each of the x-ray shots and thus degrade image quality. Even in the absence of detector noise the advantages of higher projection numbers eventually have diminishing returns, making more projections unnecessary. In this study, we explore the dependence of tomosynthesis imaging performance on the number of projections, while keeping other factors fixed. We take the contrast-to-noise ratio as the figure of merit to search for the range of optimal projection number. The study is carried out through both simulations and experiments, with phantoms consisting of micro-calcification and mass objects, and a cadaver breast. The goal of this paper is to describe our methodology in general, and use a prototype tomosynthesis system as an example. The knowledge learned will help the design of future generation clinical tomosynthesis systems.

Keywords

Shot Noise Projection Image Projection Number Detector Noise Mass Object 
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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References

  1. 1.
    Boone, J.M., et al.: Scatter/primary in mammography: Comprehensive results. Med. Phys. 27, 2408–2416 (2000)CrossRefGoogle Scholar
  2. 2.
    Yorker, J.G., et al.: Characterization of a full-field digital mammography detector based on direct x-ray conversion in selenium. In: Proc. SPIE, vol. 4682, pp. 21–29.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Baorui Ren
    • 1
  • Tao Wu
    • 1
  • Andrew Smith
    • 1
  • Chris Ruth
    • 1
  • Loren Niklason
    • 1
  • Zhenxue Jing
    • 1
  • Jay Stein
    • 1
  1. 1.Hologic, Inc.Bedford

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