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Beam Optimization for Digital Mammography – II

  • Mark B. Williams
  • Priya Raghunathan
  • Anthony Seibert
  • Alex Kwan
  • Joseph Lo
  • Ehsan Samei
  • Laurie Fajardo
  • Andrew D. A. Maidment
  • Martin Yaffe
  • Aili Bloomquist
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4046)

Abstract

Optimization of acquisition technique factors (target, filter, and kVp) in digital mammography is required for maximization of the image SNR, while minimizing patient dose. The goal of this study is to compare, for each of the major commercially available FFDM systems, the effect of various technique factors on image SNR and radiation dose for a range of breast thickness and tissue types. This phantom study follows the approach of an earlier investigation [1], and includes measurements on recent versions of two of the FFDM systems discussed in that paper, as well as on three FFDM systems not available at that time. The five commercial FFDM systems tested are located at five different university test sites and include all FFDM systems that are currently FDA approved. Performance was assessed using 9 different phantom types (three compressed thicknesses, and three tissue composition types) using all available x-ray target and filter combinations. The figure of merit (FOM) used to compare technique factors is the ratio of the square of the image SNR to the mean glandular dose (MGD). This FOM has been used previously by others in mammographic beam optimization studies [2],[3]. For selected examples, data are presented describing the change in SNR, MGD, and FOM with changing kVp, as well as with changing target and/or filter type. For all nine breast types the target/filter/kVp combination resulting in the highest FOM value is presented. Our results suggest that in general, technique combinations resulting in higher energy beams resulted in higher FOM values, for nearly all breast types.

Keywords

Digital Mammography Breast Type Breast Thickness Full Field Digital Mammography Average Glandular Dose 
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

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mark B. Williams
    • 1
  • Priya Raghunathan
    • 1
  • Anthony Seibert
    • 2
  • Alex Kwan
    • 2
  • Joseph Lo
    • 3
  • Ehsan Samei
    • 3
  • Laurie Fajardo
    • 4
  • Andrew D. A. Maidment
    • 5
  • Martin Yaffe
    • 6
  • Aili Bloomquist
    • 6
  1. 1.University of Virginia 
  2. 2.University of California-Davis 
  3. 3.Duke University 
  4. 4.University of Iowa 
  5. 5.University of Pennsylvania 
  6. 6.University of Toronto 

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