Abstract:

The accurate quantification of breast density in screening programs can aid assessing breast cancer risk and potential for lesion masking. With increasing use of digital breast tomosynthesis (DBT) and ongoing studies on its application in breast cancer screening, it is important to be able to accurately quantify the breast density , i.e., determine the breast glandularity, from the pseudo-3D DBT images. In this work we propose a non-learning regularization method that compensates for limited angle artifacts to estimate the breast density, without relying on the precise localization of the tissue structures inside the breast. Drawing inspiration from the phenomenon of gravitational accretion, we established a correspondence between the reconstructed fractions of each tissue type, i.e., adipose and fibro-glandular tissues, with the number of particles with attractive interactions. This creates a redistribution by clustering of the tissue fractions that allows the material identification of each voxel and the breast density quantification. We extend our previous work by 1) improving the mechanics of the redistribution and 2) adding realistic noise to our simulations. We evaluated our method using 45 3D digital breast phantoms based on segmented breast CT patient. We aimed to obtain rapid glandularity estimations to be a clinically-relevant method. In the noise-free scenario, the difference between the actual and reconstructed glandularities was, on average, −0.012, ranging between −0.087 and +0.044; while in the noisy scenario it was −0.001, ranging between −0.046 and +0.121. These results indicate that the proposed method leads to 1-minute noise-independent estimations of breast density.