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Optimizing the Target-Filter Combination in Digital Mammography in the Sense of Image Quality and Average Glandular Dose

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5116))

Abstract

This paper presents the optimized target – filter combinations to meet with the high image quality and the lowest possible average glandular dose in digital mammography based on amorphous selenium (a-Se) detector technology. Study also provides recommendations for target-filter combinations in digital mammography for different breast thicknesses. The full field digital mammography (FFDM) system based on a-Se technology, which is also a platform of tomosynthesis prototype, was used in this study. X-ray target – filter combinations, which we studied, were tungsten (W) – rhodium (Rh), W – silver (Ag), W– molybdenum (Mo), and W – tin (Sn). The average glandular doses (AGD) were calculated using a specific program, which automates the method described by Dance et al. Image quality was evaluated technically; contrast and noise analysis. By using W–Rh, W–Ag, W–Sn, W–Mo target-filter combinations it is possible to achieve a significantly lower average glandular dose compared to Mo–Mo or Mo–Rh. The average glandular dose reduction was achieved from 40 % to 60 % depending, which were the selected target-filter combination and the breast thickness. In the future, the evaluation will concentrate to study the effect of higher kV (> 35 kV) values, which might be useful when optimizing the dose in digital mammography.

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References

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Elizabeth A. Krupinski

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© 2008 Springer-Verlag Berlin Heidelberg

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Varjonen, M., Strömmer, P. (2008). Optimizing the Target-Filter Combination in Digital Mammography in the Sense of Image Quality and Average Glandular Dose. In: Krupinski, E.A. (eds) Digital Mammography. IWDM 2008. Lecture Notes in Computer Science, vol 5116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70538-3_79

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  • DOI: https://doi.org/10.1007/978-3-540-70538-3_79

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70537-6

  • Online ISBN: 978-3-540-70538-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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