  Contrast Enhanced Spectral Mammography (CESM) is a dual-energy mammographic
imaging technique that first needs intravenously administration of an iodinated
contrast medium; then, it collects both a low-energy image, comparable to
standard mammography, and a high-energy image. The two scans are then combined
to get a recombined image showing contrast enhancement. Despite CESM diagnostic
advantages for breast cancer diagnosis, the use of contrast medium can cause
side effects, and CESM also beams patients with a higher radiation dose
compared to standard mammography. To address these limitations this work
proposes to use deep generative models for virtual contrast enhancement on
CESM, aiming to make the CESM contrast-free as well as to reduce the radiation
dose. Our deep networks, consisting of an autoencoder and two Generative
Adversarial Networks, the Pix2Pix, and the CycleGAN, generate synthetic
recombined images solely from low-energy images. We perform an extensive
quantitative and qualitative analysis of the model's performance, also
exploiting radiologists' assessments, on a novel CESM dataset that includes
1138 images that, as a further contribution of this work, we make publicly
available. The results show that CycleGAN is the most promising deep network to
generate synthetic recombined images, highlighting the potential of artificial
intelligence techniques for virtual contrast enhancement in this field.
