The dataset includes computational digital breast phantoms derived from high-resolution 3D clinical breast images for the use in virtual clinical trials in 2D and 3D X-ray breast imaging. Uncompressed computational breast phantoms for investigations in dedicated breast CT (BCT) were derived from 60 clinical 3D breast images acquired via a dedicated CT scanner at UC Davis (California, USA). The uncompressed phantoms are submitted in a parallel dataset and present relate naming. Each image voxel was classified in one out of the four main materials presented in the field of view: fibro-glandular tissue, adipose tissue, skin tissue and air. Each of the classified materials is represented by one out of four values: 0 for the air, 1 for the adipose tissue, 2 for the glandular tissue and 3 for the skin tissue. For the image classification, a semi-automatic software was developed. 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 elastic deformation of the breast, taking into account the tissue's elastic coefficients.