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Quantitative assessment of microcalcification cluster image quality in digital breast tomosynthesis, 2-dimensional and synthetic mammography

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Abstract

Quantitative assessment of microcalcification (MC) cluster image quality is presented, in terms of cluster signal-difference-to-noise ratio (SDNR) intercomparison among digital breast tomosynthesis (DBT) and 2-dimensional (2D) and synthetic-2-dimensional (s2D) mammography. A phantom that provides realistic appearance of MC clusters located in uniform and nonuniform background was imaged in 2D and DBT, considering various scattering conditions. MC cluster SDNR differentiation is investigated with respect to MC particle size (uniform background) and surrounding parenchyma density (nonuniform background). An accurate MC cluster segmentation method was used to delineate individual MC particles and estimate MC cluster SDNR. Analysis of the uniform part of the phantom indicated higher performance of DBT and 2D over s2D for the smallest cluster size (106–177 μm), no difference among mammographic modes for the largest MC cluster (224–354 μm), and enhanced role of 2D for decreasing cluster size and increasing scattering. Analysis of the nonuniform part of the phantom indicated DBT performed better than 2D and s2D in case of dense parenchyma pattern, while 2D and s2D did not differ across parenchyma density patterns and scattering conditions. The presented MC cluster SDNR analysis was capable of revealing subtle differences among mammographic modes and suggests a methodology for clinical image quality assessment.

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Acknowledgments

The authors would like to thank the staff of Breast Unit of the Department of Radiology at the University Hospital of Patras, Greece, for their contribution in this work.

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Authors and Affiliations

  1. Department of Medical Physics, School of Medicine, University of Patras, 26504, Patras, Greece

    Andreas E. Petropoulos, Spyros G. Skiadopoulos, Anna N. Karahaliou, Nikolaos S. Arikidis & Lena I. Costaridou

  2. Department of Medical Physics, University Hospital of Patras, 26504, Patras, Greece

    Gerasimos A. T. Messaris

Authors
  1. Andreas E. Petropoulos
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  2. Spyros G. Skiadopoulos
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  3. Anna N. Karahaliou
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  6. Lena I. Costaridou
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Correspondence to Lena I. Costaridou.

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Appendix

Appendix

This paragraph and corresponding figures account for supplementary material summarizing the results of the histogram analysis (i.e., mean gray level value, skewness, entropy, and kurtosis) used for assessing the parenchyma density pattern, surrounding each one of the five microcalcification (MC) clusters of the nonuniform background part of the TORMAM phantom. Figure 10 illustrates the five MC clusters of the nonuniform background part of the TORMAM phantom (20 mm thickness, 2D mode) and corresponding histograms of the surrounding parenchyma pattern. The surrounding MC cluster parenchyma region (blue overlay) is defined by dilating (by a 15-pixel radius element) the individual MC particle contours and excluding from the analysis the 3-pixel dilated segmented MC particles. MC clusters are sorted in increasing surrounding parenchyma density (i.e., fatty, glandular, dense), as quantified by histogram features (Fig. 11). This sorting was verified in each one of the three mammographic modes (DBT, 2D, s2D). Histogram feature quantification was performed in Matlab software environment (Matlab, R2015a, MathWorks, Natick).

Fig. 10
figure 10

Regions of interest containing the microcalcification clusters (upper row) of the nonuniform background part of the TORMAM phantom (2D image, 20 mm thickness) and corresponding histograms (lower row) of each cluster surrounding parenchyma density pattern

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Fig. 11
figure 11

Sorting of the five microcalcification clusters with respect to increasing surrounding parenchyma pattern density (i.e., D1: fatty, D2–D4: glandular, D5: dense) according to features mean gray level, skewness, entropy, and kurtosis

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Petropoulos, A.E., Skiadopoulos, S.G., Karahaliou, A.N. et al. Quantitative assessment of microcalcification cluster image quality in digital breast tomosynthesis, 2-dimensional and synthetic mammography. Med Biol Eng Comput 58, 187–209 (2020). https://doi.org/10.1007/s11517-019-02072-0

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  • DOI: https://doi.org/10.1007/s11517-019-02072-0

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