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The quantification of breast density, as an evaluation of the fibro-glandular tissue content, has a key role in assessing breast cancer risk and cancer masking in mammography, which are of paramount importance for establishing screening recommendations. Digital breast tomosynthesis (DBT) is a promising imaging technology for breast cancer screening, which allows a pseudo-3D representation of the breast and therefore has the potential to improve detection of breast cancer compared to conventional 2D mammography. We propose a regularization method for DBT reconstruction based on the accretion of adipose and fibro-glandular tissues to provide an accurate breast density estimation. Inspired by the phenomenon of planetary accretion, we established a correspondence between the fraction of each tissue type with the number of particles with attractive interactions, which allows for the redistribution of the tissues, allowing their better identification at each voxel and improving breast density quantification. Our method is combined with a polychromatic reconstruction algorithm with material decomposition. Furthermore, our work offers a non-learning method for DBT glandularity estimation, which does not rely on an accurate recovery of each tissue localization. We evaluated our method using 51 3D digital phantoms based on segmented breast CT patient scans and we found that the error of the reconstructed glandularity was +0.4% on average, ranging between −9.6% and +5.2%, for glandularity values ranging between 0.09 and 0.55; while the relative error was +4.7% on average, ranging between −45.6% and +38.5%.
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