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Beam optimization for digital mammography - II

Publication ,  Journal Article
Williams, MB; Raghunathan, P; Seibert, A; Kwan, A; Lo, J; Samei, E; Fajardo, L; Maidment, ADA; Yaffe, M; Bloomquist, A
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2006

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, MOD, 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. © Springer-Verlag Berlin Heidelberg 2006.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2006

Volume

4046 LNCS

Start / End Page

273 / 280

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Williams, M. B., Raghunathan, P., Seibert, A., Kwan, A., Lo, J., Samei, E., … Bloomquist, A. (2006). Beam optimization for digital mammography - II. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4046 LNCS, 273–280. https://doi.org/10.1007/11783237_38
Williams, M. B., P. Raghunathan, A. Seibert, A. Kwan, J. Lo, E. Samei, L. Fajardo, A. D. A. Maidment, M. Yaffe, and A. Bloomquist. “Beam optimization for digital mammography - II.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4046 LNCS (January 1, 2006): 273–80. https://doi.org/10.1007/11783237_38.
Williams MB, Raghunathan P, Seibert A, Kwan A, Lo J, Samei E, et al. Beam optimization for digital mammography - II. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2006 Jan 1;4046 LNCS:273–80.
Williams, M. B., et al. “Beam optimization for digital mammography - II.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4046 LNCS, Jan. 2006, pp. 273–80. Scopus, doi:10.1007/11783237_38.
Williams MB, Raghunathan P, Seibert A, Kwan A, Lo J, Samei E, Fajardo L, Maidment ADA, Yaffe M, Bloomquist A. Beam optimization for digital mammography - II. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2006 Jan 1;4046 LNCS:273–280.

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2006

Volume

4046 LNCS

Start / End Page

273 / 280

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences