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The imparted energy to the glandular tissue in the breast (glandular imparted energy, GIE) is proposed for an improved assessment of the individual radiation-induced risk resulting from X-ray breast imaging. GIE is computed from an estimation of the quantity and localisation of glandular tissue in the breast. After a digital breast tomosynthesis (DBT) acquisition, the volumetric glandular content (volumetric breast density, VBD) is computed from the central X-ray projection. The glandular tissue distribution is determined by labelling the DBT voxels to ensure the conservation of the VBD. Finally, the GIE is calculated by Monte Carlo computation on the resulting tissue-labelled DBT volume. For verification, the method was applied to 10 breast-shaped digital phantoms made of different glandular spheres in an adipose background, and to a digital anthropomorphic phantom. Results were compared to direct GIE computations on the phantoms considered as 'ground-truth'. The major limitations in accuracy are those of DBT, in particular the limited z-resolution. However, for most phantoms, the results can be considered as acceptable., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

The radiation risk in mammography is traditionally evaluated using the average glandular dose. This quantity for the average breast has proven to be useful for population statistics and to compare exposure techniques and systems. However it is not indicating the individual radiation risk based on the individual glandular amount and distribution. Simulations of exposures were performed for six appropriate virtual phantoms with varying glandular amount and distribution. The individualised average glandular dose (iAGD), i.e. the individual glandular absorbed energy divided by the mass of the gland, and the glandular imparted energy (GIE), i.e. the glandular absorbed energy, were computed. Both quantities were evaluated for their capability to take into account the glandular amount and distribution. As expected, the results have demonstrated that iAGD reflects only the distribution, while GIE reflects both the glandular amount and distribution. Therefore GIE is a good candidate for individual radiation risk assessment., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Breast density has become an important issue in current breast cancer screening, both as a recognized risk factor for breast cancer and by decreasing screening efficiency by the masking effect. Different qualitative and quantitative methods have been proposed to evaluate area-based breast density and volumetric breast density (VBD). We propose a validation method comparing the computation of VBD obtained from digital mammographic images (VBDMX) with the computation of VBD from thorax CT images (VBDCT). We computed VBDMX by applying a conversion function to the pixel values in the mammographic images, based on models determined from images of breast equivalent material. VBDCT is computed from the average Hounsfield Unit (HU) over the manually delineated breast volume in the CT images. This average HU is then compared to the HU of adipose and fibroglandular tissues from patient images. The VBDMX method was applied to 663 mammographic patient images taken on two Siemens Inspiration (hospL) and one GE Senographe Essential (hospJ). For the comparison study, we collected images from patients who had a thorax CT and a mammography screening exam within the same year. In total, thorax CT images corresponding to 40 breasts (hospL) and 47 breasts (hospJ) were retrieved. Averaged over the 663 mammographic images the median VBDMX was 14.7% . The density distribution and the inverse correlation between VBDMX and breast thickness were found as expected. The average difference between VBDMX and VBDCT is smaller for hospJ (4%) than for hospL (10%). This study shows the possibility to compare VBDMX with the VBD from thorax CT exams, without additional examinations. In spite of the limitations caused by poorly defined breast limits, the calibration of mammographic images to local VBD provides opportunities for further quantitative evaluations.