Purpose: To present a dataset of computational digital breast phantoms derived from high-resolution three-dimensional (3D) clinical breast images for the use in virtual clinical trials in two-dimensional (2D) and 3D x-ray breast imaging. Acquisition and validation methods: Uncompressed computational breast phantoms for investigations in dedicated breast CT (BCT) were derived from 150 clinical 3D breast images acquired via a BCT scanner at UC Davis (California, USA). Each image voxel was classified in one out of the four main materials presented in the field of view: fibroglandular tissue, adipose tissue, skin tissue, and air. For the image classification, a semi-automatic software was developed. The semi-automatic classification was compared via manual glandular classification performed by two researchers. A total of 60 compressed computational phantoms for virtual clinical trials in digital mammography (DM) and digital breast tomosynthesis (DBT) were obtained from the corresponding uncompressed phantoms via a software algorithm simulating the compression and the elastic deformation of the breast, using the tissue's elastic coefficient. This process was evaluated in terms of glandular fraction modification introduced by the compression procedure. The generated cohort of 150 uncompressed computational breast phantoms presented a mean value of the glandular fraction by mass of 12.3%; the average diameter of the breast evaluated at the center of mass was 105 mm. Despite the slight differences between the two manual segmentations, the resulting glandular tissue segmentation did not consistently differ from that obtained via the semi-automatic classification. The difference between the glandular fraction by mass before and after the compression was 2.1% on average. The 60 compressed phantoms presented an average glandular fraction by mass of 12.1% and an average compressed thickness of 61 mm. Data format and access: The generated digital breast phantoms are stored in DICOM files. Image voxels can present one out of four values representing the different classified materials: 0 for the air, 1 for the adipose tissue, 2 for the glandular tissue, and 3 for the skin tissue. The generated computational phantoms datasets were stored in the Zenodo public repository for research purposes (http://

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We computed normalized glandular dose (DgN) coefficients for mean glandular dose estimates in contemporary 2D mammography units, taking into account a homogeneous model for the breast which reflects recent literature reports. We developed a Monte Carlo code based on the simulation toolkit GEANT4 ver. 10.00. The breast was modelled as a cylinder with a semi-cylindrical section with a radius of 10 cm, enveloped in a 1.45 mm thick skin layer, as found out in recent reports in the analysis of breast computed tomography clinical scans. The compressed breast thickness was between 3 cm and 8 cm. The DgN coefficients were calculated for monoenergetic x-ray beams between 4.25 keV and 49.25 keV and were fitted with polynomial curves. Polyenergetic DgN coefficients were then computed for spectra obtained for various anode/filter combinations as adopted in routine clinical practice: Mo/Mo 30 µm (25–40 kV), Mo/Rh 25 µm (25–40 kV), Rh/Rh 25 µm (25–40 kV), W/Ag 50 µm (26–34 kV), W/Al 500 µm (26–38 kV), W/Al 700 µm (28–40 kV) and W/Rh 50 µm (24–35 kV). Monoenergetic DgN curve fit coefficients and polyenergetic DgNp coefficients were released for research and clinical work. Polyenergetic DgNp coefficients were 6% higher than those provided in the recent literature, on average. The differences range between  −18% and 30%; up to 50% of the computed coefficients differed by less than 10%. The dataset of DgN coefficients are provided as tables for varying glandular fraction by mass and compressed breast thickness. Moreover, a computer code has been developed for generating user specific coefficients DgNp for user defined x-ray spectra up to 49 kV, calculated by spectral weighting from the dataset of monoenergetic DgN coefficients.

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